fine_tune_BERT_dataset_size.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "ef0ebf6a919b462a9ec84a55d0f7ff73": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_1460e66cc9364da8965ed584354cc003",
              "IPY_MODEL_6016f464dca54a67b31ef592254a5ef8",
              "IPY_MODEL_6c10f672c31e487e9d2b9f6c443684ea"
            ],
            "layout": "IPY_MODEL_ab76c5bfe8874caaa696d404cc582a5c"
          }
        },
        "1460e66cc9364da8965ed584354cc003": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_68c6865641ff4c0490ce42f309a12e0e",
            "placeholder": "​",
            "style": "IPY_MODEL_9f7816858a0e4b1ba8042455f5d77db7",
            "value": "README.md: 100%"
          }
        },
        "6016f464dca54a67b31ef592254a5ef8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_67baa91b9b05456da2feef089e017ca8",
            "max": 12330,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_09707a5c7efb498fb3ced837b5d3eca4",
            "value": 12330
          }
        },
        "6c10f672c31e487e9d2b9f6c443684ea": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_deb25c53c63d466199335042306fef6d",
            "placeholder": "​",
            "style": "IPY_MODEL_190bf9276526459eb768ddaa2773d9dc",
            "value": " 12.3k/12.3k [00:00&lt;00:00, 330kB/s]"
          }
        },
        "ab76c5bfe8874caaa696d404cc582a5c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "68c6865641ff4c0490ce42f309a12e0e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9f7816858a0e4b1ba8042455f5d77db7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "67baa91b9b05456da2feef089e017ca8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "09707a5c7efb498fb3ced837b5d3eca4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "deb25c53c63d466199335042306fef6d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "190bf9276526459eb768ddaa2773d9dc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "c89eb39759914d9095664189254feef6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_8f03d18abf404740ade1c796eba8d55d",
              "IPY_MODEL_7a96529c68dc4ec9839df4eba67764bc",
              "IPY_MODEL_51f998b807f64d44ab0597f8216e28cf"
            ],
            "layout": "IPY_MODEL_b3d4ea89e282432a81569c0321d7d2cd"
          }
        },
        "8f03d18abf404740ade1c796eba8d55d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_eb15bcc62b764120bba2b527ee3e5693",
            "placeholder": "​",
            "style": "IPY_MODEL_97f50a64f1e3473cbc50167d850a94e0",
            "value": "conll2003.py: 100%"
          }
        },
        "7a96529c68dc4ec9839df4eba67764bc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_514e45cf0e93439aa882a03386bb9edd",
            "max": 9570,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_72661436fb2b40adb62441a1de734f34",
            "value": 9570
          }
        },
        "51f998b807f64d44ab0597f8216e28cf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_859cd6e5cfbb46c48ee9d94189ec3d49",
            "placeholder": "​",
            "style": "IPY_MODEL_ed05da41f0ab4dd89578eb3b752abb12",
            "value": " 9.57k/9.57k [00:00&lt;00:00, 206kB/s]"
          }
        },
        "b3d4ea89e282432a81569c0321d7d2cd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "eb15bcc62b764120bba2b527ee3e5693": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "97f50a64f1e3473cbc50167d850a94e0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "514e45cf0e93439aa882a03386bb9edd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "72661436fb2b40adb62441a1de734f34": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "859cd6e5cfbb46c48ee9d94189ec3d49": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ed05da41f0ab4dd89578eb3b752abb12": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "60c80922cf2840069e8df0abb2f8e49a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_20e8e1800e664d92b8a90e05585f9519",
              "IPY_MODEL_195f90f2cab94d63a7d731334473db9c",
              "IPY_MODEL_11e11942bf394888a28c9f9eb6e8be49"
            ],
            "layout": "IPY_MODEL_d67ad1a0629e46d9aeb06bdb8a6be64a"
          }
        },
        "20e8e1800e664d92b8a90e05585f9519": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d6d5ce7a194b4a819b83496557d9ef5d",
            "placeholder": "​",
            "style": "IPY_MODEL_b53601d5227445ad857ed39c1525ec4c",
            "value": "Downloading data: 100%"
          }
        },
        "195f90f2cab94d63a7d731334473db9c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_91287149bed843f2bd26ec689bae48ca",
            "max": 982975,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_91e59ed207cd4fa2bd5185df01032f68",
            "value": 982975
          }
        },
        "11e11942bf394888a28c9f9eb6e8be49": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f9869af1dfbc4955a969e31449e2ee8e",
            "placeholder": "​",
            "style": "IPY_MODEL_bac195184066440e8791964ba3d6a7a4",
            "value": " 983k/983k [00:00&lt;00:00, 1.76MB/s]"
          }
        },
        "d67ad1a0629e46d9aeb06bdb8a6be64a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d6d5ce7a194b4a819b83496557d9ef5d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b53601d5227445ad857ed39c1525ec4c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "91287149bed843f2bd26ec689bae48ca": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "91e59ed207cd4fa2bd5185df01032f68": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "f9869af1dfbc4955a969e31449e2ee8e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bac195184066440e8791964ba3d6a7a4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "3ad9054c704d4426b07220d5ea06c3e9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_63313a2a5e3d442aa2cace0cb302a912",
              "IPY_MODEL_ddad810881af4a6fad73a59de8f660c1",
              "IPY_MODEL_fd233bd8f4e44afcb2456c5955e6f1a8"
            ],
            "layout": "IPY_MODEL_9e71582384fc4f44b494c81bee35ab88"
          }
        },
        "63313a2a5e3d442aa2cace0cb302a912": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9f8d664f2ba049a6b68147ae02cb7cb3",
            "placeholder": "​",
            "style": "IPY_MODEL_519f47010ff247beb1fb1e27dd26c061",
            "value": "Generating train split: 100%"
          }
        },
        "ddad810881af4a6fad73a59de8f660c1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_753728292ede4f989828f59a8e078e25",
            "max": 14041,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_862794520e5047eaa22e9d5feae06497",
            "value": 14041
          }
        },
        "fd233bd8f4e44afcb2456c5955e6f1a8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2df6421f19194bb48e122dd8590e9679",
            "placeholder": "​",
            "style": "IPY_MODEL_1fb411a47d1843f7831f608d0cfefeda",
            "value": " 14041/14041 [00:03&lt;00:00, 4376.38 examples/s]"
          }
        },
        "9e71582384fc4f44b494c81bee35ab88": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9f8d664f2ba049a6b68147ae02cb7cb3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "519f47010ff247beb1fb1e27dd26c061": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "753728292ede4f989828f59a8e078e25": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "862794520e5047eaa22e9d5feae06497": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "2df6421f19194bb48e122dd8590e9679": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1fb411a47d1843f7831f608d0cfefeda": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6f12965fd6e5471c842195518728d1ed": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_e85227d4b8c34e3cbe0a5b2d694001ed",
              "IPY_MODEL_041150dffbe0408d9e21d9366cf2d79f",
              "IPY_MODEL_ddb11ace8f9e4074bc5130bbc4bffe94"
            ],
            "layout": "IPY_MODEL_ce6c5e261073462a87b3bb22a791264c"
          }
        },
        "e85227d4b8c34e3cbe0a5b2d694001ed": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ebecf57c337a4196a669bbf6c0759d5f",
            "placeholder": "​",
            "style": "IPY_MODEL_74204d12e61643eea5ef98660138251b",
            "value": "Generating validation split: 100%"
          }
        },
        "041150dffbe0408d9e21d9366cf2d79f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_09e0ca0e8af247d0a32bfbac84f16f2d",
            "max": 3250,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_25cd420677ec4897bd703f0b375d781c",
            "value": 3250
          }
        },
        "ddb11ace8f9e4074bc5130bbc4bffe94": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d07fe5e616b94560b4ffc1cbe40b8179",
            "placeholder": "​",
            "style": "IPY_MODEL_e90d811cfee445529f214448e817ded9",
            "value": " 3250/3250 [00:00&lt;00:00, 4032.80 examples/s]"
          }
        },
        "ce6c5e261073462a87b3bb22a791264c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ebecf57c337a4196a669bbf6c0759d5f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "74204d12e61643eea5ef98660138251b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "09e0ca0e8af247d0a32bfbac84f16f2d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "25cd420677ec4897bd703f0b375d781c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "d07fe5e616b94560b4ffc1cbe40b8179": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e90d811cfee445529f214448e817ded9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "fd531fdff98e4e298790a7523193cd1d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_8b048f1c0c8841c8907ed4f11723fc76",
              "IPY_MODEL_d7f8157b146c48d5a8369960b3777464",
              "IPY_MODEL_fb56d47cba89455384d3383f05a59734"
            ],
            "layout": "IPY_MODEL_925d03d06f85472fa227ce100745f923"
          }
        },
        "8b048f1c0c8841c8907ed4f11723fc76": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_69a05b59828e43a28a100dcc66d02d8e",
            "placeholder": "​",
            "style": "IPY_MODEL_89987f7483594e9c828dce9b1da0d27c",
            "value": "Generating test split: 100%"
          }
        },
        "d7f8157b146c48d5a8369960b3777464": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_35c2031546c24e36973ecba74ee90358",
            "max": 3453,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_ead417b0fc604cd7805ca271ae81bdf0",
            "value": 3453
          }
        },
        "fb56d47cba89455384d3383f05a59734": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_db192f5cb26b47c883cc0db0338d0323",
            "placeholder": "​",
            "style": "IPY_MODEL_e0317716be1544f9af960b26319198c4",
            "value": " 3453/3453 [00:00&lt;00:00, 4744.56 examples/s]"
          }
        },
        "925d03d06f85472fa227ce100745f923": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "69a05b59828e43a28a100dcc66d02d8e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "89987f7483594e9c828dce9b1da0d27c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "35c2031546c24e36973ecba74ee90358": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ead417b0fc604cd7805ca271ae81bdf0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "db192f5cb26b47c883cc0db0338d0323": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e0317716be1544f9af960b26319198c4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "ea9e0f1dcc9c4e3bb88313134d5288d6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_9fca4d37f1134a5b9fd7e5fde2739d9e",
              "IPY_MODEL_3ee73674dca44087ab5418475712840d",
              "IPY_MODEL_d9341ddb14564c1f8709537d863434f3"
            ],
            "layout": "IPY_MODEL_7c65d228fcb8472795495aa9f243b91b"
          }
        },
        "9fca4d37f1134a5b9fd7e5fde2739d9e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_255e1f21523349fbb683bc4b5e3a482d",
            "placeholder": "​",
            "style": "IPY_MODEL_cf35d5a1229a45b994677cc354b93ce2",
            "value": "tokenizer_config.json: 100%"
          }
        },
        "3ee73674dca44087ab5418475712840d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e6dc277845fd494aa1d4357799b25124",
            "max": 48,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_88d183c08f524cd3bfacb48232a21748",
            "value": 48
          }
        },
        "d9341ddb14564c1f8709537d863434f3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e3edaccc945f440ca3e6f4bc0e0d2b6a",
            "placeholder": "​",
            "style": "IPY_MODEL_7c80942bb8e349a8a20a7f254180d803",
            "value": " 48.0/48.0 [00:00&lt;00:00, 1.16kB/s]"
          }
        },
        "7c65d228fcb8472795495aa9f243b91b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "255e1f21523349fbb683bc4b5e3a482d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cf35d5a1229a45b994677cc354b93ce2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e6dc277845fd494aa1d4357799b25124": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "88d183c08f524cd3bfacb48232a21748": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "e3edaccc945f440ca3e6f4bc0e0d2b6a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7c80942bb8e349a8a20a7f254180d803": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "03f278471b834b48b46d834f529a7dc3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d40095140dec42a0af62a513bf07c2b4",
              "IPY_MODEL_c4978fd78a8346ae95cde4c22964c2ea",
              "IPY_MODEL_1f42be1cd688412c906b73d1b65d027e"
            ],
            "layout": "IPY_MODEL_a87b7eb4352a4eb6920b92648016ae44"
          }
        },
        "d40095140dec42a0af62a513bf07c2b4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e437dd24150e456ab741d9bb19ae3b6e",
            "placeholder": "​",
            "style": "IPY_MODEL_07dff7937ec24210be5a9c673c813642",
            "value": "vocab.txt: 100%"
          }
        },
        "c4978fd78a8346ae95cde4c22964c2ea": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e3f6df5c138f41d7accfd61911a6cae1",
            "max": 231508,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_05a2546439864a17a399ba9ff09d5a5c",
            "value": 231508
          }
        },
        "1f42be1cd688412c906b73d1b65d027e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1f936c79b71a4e82ba3c9b11ae1973a0",
            "placeholder": "​",
            "style": "IPY_MODEL_1bf064fb04424681935309dd99e23d80",
            "value": " 232k/232k [00:00&lt;00:00, 4.65MB/s]"
          }
        },
        "a87b7eb4352a4eb6920b92648016ae44": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e437dd24150e456ab741d9bb19ae3b6e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "07dff7937ec24210be5a9c673c813642": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e3f6df5c138f41d7accfd61911a6cae1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "05a2546439864a17a399ba9ff09d5a5c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "1f936c79b71a4e82ba3c9b11ae1973a0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1bf064fb04424681935309dd99e23d80": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "74ecf62c2b37454abece43b404e8b60e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_bc9c09a47b604a9f93f6741af91f6ffe",
              "IPY_MODEL_a3cadaddbe8d49259c872a8a4e13fef0",
              "IPY_MODEL_f5f24560f11e45da8d0a2ef3d875e7a1"
            ],
            "layout": "IPY_MODEL_2a9395bf940d4b7a9f87ed7f2559e9e1"
          }
        },
        "bc9c09a47b604a9f93f6741af91f6ffe": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b9da0383b3f44a1c9282f9fd2de803a7",
            "placeholder": "​",
            "style": "IPY_MODEL_eb1481244a0a427a9aac70e0b343e8ac",
            "value": "tokenizer.json: 100%"
          }
        },
        "a3cadaddbe8d49259c872a8a4e13fef0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3911b7d45da44f1f91707a8c38465f15",
            "max": 466062,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_5c2df40ac85744c69234a16483d2a7d7",
            "value": 466062
          }
        },
        "f5f24560f11e45da8d0a2ef3d875e7a1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f28583a8d4e2433ca610d4993ac92651",
            "placeholder": "​",
            "style": "IPY_MODEL_31e451503c6743b0bc43b6c12c633100",
            "value": " 466k/466k [00:00&lt;00:00, 9.42MB/s]"
          }
        },
        "2a9395bf940d4b7a9f87ed7f2559e9e1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b9da0383b3f44a1c9282f9fd2de803a7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "eb1481244a0a427a9aac70e0b343e8ac": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "3911b7d45da44f1f91707a8c38465f15": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5c2df40ac85744c69234a16483d2a7d7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "f28583a8d4e2433ca610d4993ac92651": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "31e451503c6743b0bc43b6c12c633100": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "4c6c979198a84152a1607f4040358ae0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_8a2d9a19754f4d1d81a9339b5a5d0692",
              "IPY_MODEL_d02318c825a04742b5676ca919fc8fba",
              "IPY_MODEL_8241338a0b924a418e0f0b795fed6195"
            ],
            "layout": "IPY_MODEL_2dda20347149464ab2a5093153b1abef"
          }
        },
        "8a2d9a19754f4d1d81a9339b5a5d0692": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_83f2d60ffb2a4534a7f8b98ea80c0de8",
            "placeholder": "​",
            "style": "IPY_MODEL_a94b2f1b1890426d9f490fe5a386dcb0",
            "value": "config.json: 100%"
          }
        },
        "d02318c825a04742b5676ca919fc8fba": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6e20ac99f1564fd7b2ec1063e7e8c50a",
            "max": 570,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_2c2d0c21284f4960a12f96be1fddee40",
            "value": 570
          }
        },
        "8241338a0b924a418e0f0b795fed6195": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d4b65b82a1a240aa8409b8347b120b60",
            "placeholder": "​",
            "style": "IPY_MODEL_1ff7e9d5ddf44f998cf7b839153f6db1",
            "value": " 570/570 [00:00&lt;00:00, 35.2kB/s]"
          }
        },
        "2dda20347149464ab2a5093153b1abef": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "83f2d60ffb2a4534a7f8b98ea80c0de8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a94b2f1b1890426d9f490fe5a386dcb0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6e20ac99f1564fd7b2ec1063e7e8c50a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2c2d0c21284f4960a12f96be1fddee40": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "d4b65b82a1a240aa8409b8347b120b60": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1ff7e9d5ddf44f998cf7b839153f6db1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "774a001140104d3f8301e8b56c4b6d4a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_0267a96eda914a5cb20778ba463808de",
              "IPY_MODEL_3534e1bb510b4c0baeaefc860fee48a5",
              "IPY_MODEL_320285e4ca944a44a2e8a0fdcfd9feb6"
            ],
            "layout": "IPY_MODEL_38fe3a4ad9654db1a59fbecc34a9df38"
          }
        },
        "0267a96eda914a5cb20778ba463808de": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_56593cc2895b4e37b347120e7584dd9e",
            "placeholder": "​",
            "style": "IPY_MODEL_2a292079df084fba991b9d27bf6b8a7f",
            "value": "model.safetensors: 100%"
          }
        },
        "3534e1bb510b4c0baeaefc860fee48a5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_37688047c8824e38819be8d2a2333ec0",
            "max": 440449768,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_c22a27ce6cd448c39ffaf9acd752375b",
            "value": 440449768
          }
        },
        "320285e4ca944a44a2e8a0fdcfd9feb6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1e5c72934b494047825794c55b9e7988",
            "placeholder": "​",
            "style": "IPY_MODEL_c50aeebae589463f855835e62e7f1a38",
            "value": " 440M/440M [00:02&lt;00:00, 242MB/s]"
          }
        },
        "38fe3a4ad9654db1a59fbecc34a9df38": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "56593cc2895b4e37b347120e7584dd9e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2a292079df084fba991b9d27bf6b8a7f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "37688047c8824e38819be8d2a2333ec0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c22a27ce6cd448c39ffaf9acd752375b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "1e5c72934b494047825794c55b9e7988": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c50aeebae589463f855835e62e7f1a38": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gonzaq94/NER-LLM-classification/blob/main/fine_tune_BERT_dataset_size.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OwYzuc4YtuSH",
        "outputId": "ebba9b50-250b-41d2-ae02-a3c8f8173a41"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import comet_ml\n",
        "import comet_llm\n",
        "import os\n",
        "import gdown\n",
        "from datasets import load_dataset\n",
        "import random\n",
        "from transformers import AutoTokenizer, AutoModelForTokenClassification, Trainer, TrainingArguments\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch\n",
        "from sklearn.metrics import precision_recall_fscore_support, accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "XvZXWnK-unYv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Read API key"
      ],
      "metadata": {
        "id": "49R-FRp5ns8G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load dataset"
      ],
      "metadata": {
        "id": "blPydpMkvFPA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Take 1000 random examples of the CONLL dataset."
      ],
      "metadata": {
        "id": "DBuAi-NG3WrV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the CONLL dataset\n",
        "dataset_train = load_dataset(\"conll2003\", split=\"train\").shuffle().select(range(1000))\n",
        "dataset_valid = load_dataset(\"conll2003\", split=\"validation\").shuffle().select(range(1000))\n",
        "dataset_test = load_dataset(\"conll2003\", split=\"test\")"
      ],
      "metadata": {
        "id": "B1kV4VACukSR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383,
          "referenced_widgets": [
            "ef0ebf6a919b462a9ec84a55d0f7ff73",
            "1460e66cc9364da8965ed584354cc003",
            "6016f464dca54a67b31ef592254a5ef8",
            "6c10f672c31e487e9d2b9f6c443684ea",
            "ab76c5bfe8874caaa696d404cc582a5c",
            "68c6865641ff4c0490ce42f309a12e0e",
            "9f7816858a0e4b1ba8042455f5d77db7",
            "67baa91b9b05456da2feef089e017ca8",
            "09707a5c7efb498fb3ced837b5d3eca4",
            "deb25c53c63d466199335042306fef6d",
            "190bf9276526459eb768ddaa2773d9dc",
            "c89eb39759914d9095664189254feef6",
            "8f03d18abf404740ade1c796eba8d55d",
            "7a96529c68dc4ec9839df4eba67764bc",
            "51f998b807f64d44ab0597f8216e28cf",
            "b3d4ea89e282432a81569c0321d7d2cd",
            "eb15bcc62b764120bba2b527ee3e5693",
            "97f50a64f1e3473cbc50167d850a94e0",
            "514e45cf0e93439aa882a03386bb9edd",
            "72661436fb2b40adb62441a1de734f34",
            "859cd6e5cfbb46c48ee9d94189ec3d49",
            "ed05da41f0ab4dd89578eb3b752abb12",
            "60c80922cf2840069e8df0abb2f8e49a",
            "20e8e1800e664d92b8a90e05585f9519",
            "195f90f2cab94d63a7d731334473db9c",
            "11e11942bf394888a28c9f9eb6e8be49",
            "d67ad1a0629e46d9aeb06bdb8a6be64a",
            "d6d5ce7a194b4a819b83496557d9ef5d",
            "b53601d5227445ad857ed39c1525ec4c",
            "91287149bed843f2bd26ec689bae48ca",
            "91e59ed207cd4fa2bd5185df01032f68",
            "f9869af1dfbc4955a969e31449e2ee8e",
            "bac195184066440e8791964ba3d6a7a4",
            "3ad9054c704d4426b07220d5ea06c3e9",
            "63313a2a5e3d442aa2cace0cb302a912",
            "ddad810881af4a6fad73a59de8f660c1",
            "fd233bd8f4e44afcb2456c5955e6f1a8",
            "9e71582384fc4f44b494c81bee35ab88",
            "9f8d664f2ba049a6b68147ae02cb7cb3",
            "519f47010ff247beb1fb1e27dd26c061",
            "753728292ede4f989828f59a8e078e25",
            "862794520e5047eaa22e9d5feae06497",
            "2df6421f19194bb48e122dd8590e9679",
            "1fb411a47d1843f7831f608d0cfefeda",
            "6f12965fd6e5471c842195518728d1ed",
            "e85227d4b8c34e3cbe0a5b2d694001ed",
            "041150dffbe0408d9e21d9366cf2d79f",
            "ddb11ace8f9e4074bc5130bbc4bffe94",
            "ce6c5e261073462a87b3bb22a791264c",
            "ebecf57c337a4196a669bbf6c0759d5f",
            "74204d12e61643eea5ef98660138251b",
            "09e0ca0e8af247d0a32bfbac84f16f2d",
            "25cd420677ec4897bd703f0b375d781c",
            "d07fe5e616b94560b4ffc1cbe40b8179",
            "e90d811cfee445529f214448e817ded9",
            "fd531fdff98e4e298790a7523193cd1d",
            "8b048f1c0c8841c8907ed4f11723fc76",
            "d7f8157b146c48d5a8369960b3777464",
            "fb56d47cba89455384d3383f05a59734",
            "925d03d06f85472fa227ce100745f923",
            "69a05b59828e43a28a100dcc66d02d8e",
            "89987f7483594e9c828dce9b1da0d27c",
            "35c2031546c24e36973ecba74ee90358",
            "ead417b0fc604cd7805ca271ae81bdf0",
            "db192f5cb26b47c883cc0db0338d0323",
            "e0317716be1544f9af960b26319198c4"
          ]
        },
        "outputId": "1b9a6428-4585-4bc8-e577-c67254d4774f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/12.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef0ebf6a919b462a9ec84a55d0f7ff73"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "conll2003.py:   0%|          | 0.00/9.57k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c89eb39759914d9095664189254feef6"
            }
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The repository for conll2003 contains custom code which must be executed to correctly load the dataset. You can inspect the repository content at https://hf.co/datasets/conll2003.\n",
            "You can avoid this prompt in future by passing the argument `trust_remote_code=True`.\n",
            "\n",
            "Do you wish to run the custom code? [y/N] Y\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/983k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60c80922cf2840069e8df0abb2f8e49a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/14041 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3ad9054c704d4426b07220d5ea06c3e9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/3250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f12965fd6e5471c842195518728d1ed"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/3453 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fd531fdff98e4e298790a7523193cd1d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset_train)\n",
        "print(dataset_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgRR0i_XuuP0",
        "outputId": "0329b136-041d-4e0b-cc93-49c654cd8ca0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
            "    num_rows: 1000\n",
            "})\n",
            "Dataset({\n",
            "    features: ['id', 'tokens', 'pos_tags', 'chunk_tags', 'ner_tags'],\n",
            "    num_rows: 1000\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(5):\n",
        "    print(dataset_train[i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uq64MCl8vl5P",
        "outputId": "e1fc332b-b843-45c8-fb6b-34b50bc059f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'id': '11127', 'tokens': ['The', 'formal', 'inauguration', 'had', 'been', 'due', 'to', 'take', 'place', 'this', 'week', 'but', 'was', 'put', 'back', '.'], 'pos_tags': [12, 16, 21, 38, 40, 16, 35, 37, 37, 12, 21, 10, 38, 40, 30, 7], 'chunk_tags': [11, 12, 12, 21, 22, 1, 21, 22, 22, 11, 12, 0, 21, 22, 3, 0], 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}\n",
            "{'id': '4471', 'tokens': ['GOLF', '-', 'MICKELSON', 'WINS', 'FOURTH', 'TITLE', 'OF', 'YEAR', 'IN', 'AKRON', '.'], 'pos_tags': [21, 8, 22, 42, 15, 12, 15, 21, 15, 22, 7], 'chunk_tags': [11, 0, 11, 21, 13, 11, 12, 12, 13, 11, 0], 'ner_tags': [0, 0, 1, 0, 0, 0, 0, 0, 0, 5, 0]}\n",
            "{'id': '7685', 'tokens': ['With', 'your', 'help', 'we', 'can', 're-elect', 'Bill', 'Clinton', '.', '\"'], 'pos_tags': [15, 29, 21, 28, 20, 37, 22, 22, 7, 0], 'chunk_tags': [13, 11, 12, 11, 21, 22, 11, 12, 12, 0], 'ner_tags': [0, 0, 0, 0, 0, 0, 1, 2, 0, 0]}\n",
            "{'id': '10958', 'tokens': ['The', 'August', 'contract', 'expired', 'at', '0.85', 'cent', 'down', 'at', '90.85', 'cents', '.'], 'pos_tags': [12, 22, 21, 38, 15, 11, 21, 33, 15, 11, 24, 7], 'chunk_tags': [11, 12, 12, 21, 13, 11, 12, 15, 13, 11, 12, 0], 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}\n",
            "{'id': '177', 'tokens': ['When', 'asked', 'whether', 'the', 'three', 'guards', 'would', 'travel', 'back', 'to', 'Kandahar', 'or', 'the', 'Afghan', 'capital', 'Kabul', ',', 'the', 'embassy', 'official', 'said', ':', '\"', 'That', 'has', 'not', 'been', 'decided', ',', 'but', 'possibly', 'Kandahar', '.', '\"'], 'pos_tags': [46, 38, 15, 12, 11, 24, 20, 37, 30, 35, 22, 10, 12, 16, 21, 22, 6, 12, 21, 21, 38, 8, 0, 12, 42, 30, 40, 40, 6, 10, 30, 22, 7, 0], 'chunk_tags': [3, 21, 17, 11, 12, 12, 21, 22, 3, 13, 11, 0, 11, 12, 12, 12, 0, 11, 12, 12, 21, 0, 0, 11, 21, 22, 22, 22, 0, 0, 3, 11, 12, 0], 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0, 7, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 0, 0]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YGgGG1OV3ipO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        " ## Load BERT model"
      ],
      "metadata": {
        "id": "T_gkErHO2sfv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train a BERT on this dataset to classify tokens (the ner_tags column). Report performance."
      ],
      "metadata": {
        "id": "FhQ-XU8p3ZQk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = 'bert-base-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202,
          "referenced_widgets": [
            "ea9e0f1dcc9c4e3bb88313134d5288d6",
            "9fca4d37f1134a5b9fd7e5fde2739d9e",
            "3ee73674dca44087ab5418475712840d",
            "d9341ddb14564c1f8709537d863434f3",
            "7c65d228fcb8472795495aa9f243b91b",
            "255e1f21523349fbb683bc4b5e3a482d",
            "cf35d5a1229a45b994677cc354b93ce2",
            "e6dc277845fd494aa1d4357799b25124",
            "88d183c08f524cd3bfacb48232a21748",
            "e3edaccc945f440ca3e6f4bc0e0d2b6a",
            "7c80942bb8e349a8a20a7f254180d803",
            "03f278471b834b48b46d834f529a7dc3",
            "d40095140dec42a0af62a513bf07c2b4",
            "c4978fd78a8346ae95cde4c22964c2ea",
            "1f42be1cd688412c906b73d1b65d027e",
            "a87b7eb4352a4eb6920b92648016ae44",
            "e437dd24150e456ab741d9bb19ae3b6e",
            "07dff7937ec24210be5a9c673c813642",
            "e3f6df5c138f41d7accfd61911a6cae1",
            "05a2546439864a17a399ba9ff09d5a5c",
            "1f936c79b71a4e82ba3c9b11ae1973a0",
            "1bf064fb04424681935309dd99e23d80",
            "74ecf62c2b37454abece43b404e8b60e",
            "bc9c09a47b604a9f93f6741af91f6ffe",
            "a3cadaddbe8d49259c872a8a4e13fef0",
            "f5f24560f11e45da8d0a2ef3d875e7a1",
            "2a9395bf940d4b7a9f87ed7f2559e9e1",
            "b9da0383b3f44a1c9282f9fd2de803a7",
            "eb1481244a0a427a9aac70e0b343e8ac",
            "3911b7d45da44f1f91707a8c38465f15",
            "5c2df40ac85744c69234a16483d2a7d7",
            "f28583a8d4e2433ca610d4993ac92651",
            "31e451503c6743b0bc43b6c12c633100"
          ]
        },
        "id": "1OoxVWXI3Ael",
        "outputId": "99491702-ac41-4bb0-bc06-669e5ec58b65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ea9e0f1dcc9c4e3bb88313134d5288d6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03f278471b834b48b46d834f529a7dc3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "74ecf62c2b37454abece43b404e8b60e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset preparation"
      ],
      "metadata": {
        "id": "WtlbTvv76r9f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, texts, labels, tokenizer, max_length):\n",
        "        self.texts = texts\n",
        "        self.labels = labels\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.texts)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        text = self.texts[idx]\n",
        "        labels = self.labels[idx]\n",
        "\n",
        "        tokens = []\n",
        "        tokens_labels = []\n",
        "        attention_masks = []\n",
        "\n",
        "        for word, label in zip(text, labels):\n",
        "          # Tokenize each word\n",
        "          word_encoding = self.tokenizer(\n",
        "              word,\n",
        "              add_special_tokens=False,\n",
        "              max_length=self.max_length,\n",
        "              truncation=True,\n",
        "              return_tensors=\"pt\"\n",
        "          )\n",
        "\n",
        "          tokens.extend(word_encoding['input_ids'][0].tolist())\n",
        "          attention_masks.extend(word_encoding['attention_mask'][0].tolist())\n",
        "          tokens_labels.extend([label] * len(word_encoding['input_ids'][0]))\n",
        "\n",
        "        # add padding\n",
        "        padding_len = self.max_length - len(tokens)\n",
        "        tokens += [0] * padding_len\n",
        "        attention_masks += [0] * padding_len\n",
        "        tokens_labels += [-100] * padding_len\n",
        "\n",
        "        return {\n",
        "            'input_ids': torch.tensor(tokens).flatten(),\n",
        "            'attention_mask': torch.tensor(attention_masks).flatten(),\n",
        "            'labels': torch.tensor(tokens_labels).flatten()\n",
        "        }\n"
      ],
      "metadata": {
        "id": "gfmxIqXB3dak"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_custom_dataset = CustomDataset(\n",
        "    texts=dataset_train['tokens'],\n",
        "    labels=dataset_train['ner_tags'],\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=256\n",
        ")\n",
        "\n",
        "valid_custom_dataset = CustomDataset(\n",
        "    texts=dataset_valid['tokens'],\n",
        "    labels=dataset_valid['ner_tags'],\n",
        "    tokenizer=tokenizer,\n",
        "    max_length=256\n",
        ")"
      ],
      "metadata": {
        "id": "hPuIXCi47C-X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine-tune model"
      ],
      "metadata": {
        "id": "e1qJVvBH670l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# log results and assets to Comet\n",
        "os.environ[\"COMET_LOG_ASSETS\"] = \"True\"\n",
        "os.environ[\"COMET_WORKSPACE\"] = \"gonzaq94\"\n",
        "os.environ[\"COMET_PROJECT_NAME\"] = \"ner_classif-bert-2\"\n",
        "os.environ[\"COMET_GIT_DIRECTORY\"] = \"https://github.com/gonzaq94/NER-LLM-classification\""
      ],
      "metadata": {
        "id": "YcZXiE0h6uFV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reload model\n",
        "model_name = 'bert-base-uncased'\n",
        "model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "4c6c979198a84152a1607f4040358ae0",
            "8a2d9a19754f4d1d81a9339b5a5d0692",
            "d02318c825a04742b5676ca919fc8fba",
            "8241338a0b924a418e0f0b795fed6195",
            "2dda20347149464ab2a5093153b1abef",
            "83f2d60ffb2a4534a7f8b98ea80c0de8",
            "a94b2f1b1890426d9f490fe5a386dcb0",
            "6e20ac99f1564fd7b2ec1063e7e8c50a",
            "2c2d0c21284f4960a12f96be1fddee40",
            "d4b65b82a1a240aa8409b8347b120b60",
            "1ff7e9d5ddf44f998cf7b839153f6db1",
            "774a001140104d3f8301e8b56c4b6d4a",
            "0267a96eda914a5cb20778ba463808de",
            "3534e1bb510b4c0baeaefc860fee48a5",
            "320285e4ca944a44a2e8a0fdcfd9feb6",
            "38fe3a4ad9654db1a59fbecc34a9df38",
            "56593cc2895b4e37b347120e7584dd9e",
            "2a292079df084fba991b9d27bf6b8a7f",
            "37688047c8824e38819be8d2a2333ec0",
            "c22a27ce6cd448c39ffaf9acd752375b",
            "1e5c72934b494047825794c55b9e7988",
            "c50aeebae589463f855835e62e7f1a38"
          ]
        },
        "id": "DmXVVsTzzYxj",
        "outputId": "abb1dbae-7d89-4302-8f10-b4ce48133482"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c6c979198a84152a1607f4040358ae0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "774a001140104d3f8301e8b56c4b6d4a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "    predictions = predictions.argmax(-1)  # Get the predicted class indices\n",
        "    true_predictions = [p for (p, l) in zip(predictions.flatten(), labels.flatten()) if l != -100]\n",
        "    true_labels = [l for l in labels.flatten() if l != -100]\n",
        "\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(true_labels, true_predictions, average='weighted', zero_division=1 )\n",
        "    acc = accuracy_score(true_labels, true_predictions)\n",
        "\n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall\n",
        "    }"
      ],
      "metadata": {
        "id": "-ZFdxp3tVeVh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',\n",
        "    num_train_epochs=15,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    logging_strategy=\"epoch\",\n",
        "    per_device_train_batch_size=24,\n",
        "    per_device_eval_batch_size=16,\n",
        "    warmup_steps=100,\n",
        "    weight_decay=0.01,\n",
        "    logging_dir='./logs',\n",
        "    dataloader_drop_last=False\n",
        ")\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_custom_dataset,\n",
        "    eval_dataset=valid_custom_dataset,\n",
        "    compute_metrics=compute_metrics\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldEAsCt46_JP",
        "outputId": "7d68fde2-6a52-4fcc-96e9-0932b6a57fae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ly5tmwXNRcCB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "gtx8Q9kK6_mF",
        "outputId": "27958601-c3cf-46b6-a281-8a21b6b8d409"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='631' max='630' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [630/630 17:50, Epoch 15/15]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.378700</td>\n",
              "      <td>0.813870</td>\n",
              "      <td>0.793627</td>\n",
              "      <td>0.702334</td>\n",
              "      <td>0.802354</td>\n",
              "      <td>0.793627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.619500</td>\n",
              "      <td>0.375166</td>\n",
              "      <td>0.878261</td>\n",
              "      <td>0.858744</td>\n",
              "      <td>0.874123</td>\n",
              "      <td>0.878261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.303900</td>\n",
              "      <td>0.229334</td>\n",
              "      <td>0.929480</td>\n",
              "      <td>0.922165</td>\n",
              "      <td>0.932523</td>\n",
              "      <td>0.929480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.953967</td>\n",
              "      <td>0.951871</td>\n",
              "      <td>0.953930</td>\n",
              "      <td>0.953967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.097900</td>\n",
              "      <td>0.166978</td>\n",
              "      <td>0.952149</td>\n",
              "      <td>0.951655</td>\n",
              "      <td>0.956888</td>\n",
              "      <td>0.952149</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.055800</td>\n",
              "      <td>0.147061</td>\n",
              "      <td>0.962094</td>\n",
              "      <td>0.961936</td>\n",
              "      <td>0.962418</td>\n",
              "      <td>0.962094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.036700</td>\n",
              "      <td>0.142508</td>\n",
              "      <td>0.964927</td>\n",
              "      <td>0.965209</td>\n",
              "      <td>0.965974</td>\n",
              "      <td>0.964927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.024100</td>\n",
              "      <td>0.156080</td>\n",
              "      <td>0.964981</td>\n",
              "      <td>0.965619</td>\n",
              "      <td>0.966669</td>\n",
              "      <td>0.964981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.020400</td>\n",
              "      <td>0.144489</td>\n",
              "      <td>0.969686</td>\n",
              "      <td>0.969342</td>\n",
              "      <td>0.969835</td>\n",
              "      <td>0.969686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.015700</td>\n",
              "      <td>0.137140</td>\n",
              "      <td>0.970701</td>\n",
              "      <td>0.970611</td>\n",
              "      <td>0.970832</td>\n",
              "      <td>0.970701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.008800</td>\n",
              "      <td>0.142030</td>\n",
              "      <td>0.971022</td>\n",
              "      <td>0.970843</td>\n",
              "      <td>0.971068</td>\n",
              "      <td>0.971022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.006300</td>\n",
              "      <td>0.144116</td>\n",
              "      <td>0.971236</td>\n",
              "      <td>0.971224</td>\n",
              "      <td>0.971568</td>\n",
              "      <td>0.971236</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.004300</td>\n",
              "      <td>0.146544</td>\n",
              "      <td>0.970701</td>\n",
              "      <td>0.970752</td>\n",
              "      <td>0.971147</td>\n",
              "      <td>0.970701</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.003800</td>\n",
              "      <td>0.146482</td>\n",
              "      <td>0.971129</td>\n",
              "      <td>0.971121</td>\n",
              "      <td>0.971335</td>\n",
              "      <td>0.971129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.004000</td>\n",
              "      <td>0.146449</td>\n",
              "      <td>0.971771</td>\n",
              "      <td>0.971744</td>\n",
              "      <td>0.971917</td>\n",
              "      <td>0.971771</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-76-3435b262f1ae>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1936\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1937\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1938\u001b[0;31m             return inner_training_loop(\n\u001b[0m\u001b[1;32m   1939\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1940\u001b[0m                 \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2436\u001b[0m                     \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrmtree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_errors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2437\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2438\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2440\u001b[0m         \u001b[0;31m# Wait for the checkpoint to be uploaded.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer_callback.py\u001b[0m in \u001b[0;36mon_train_end\u001b[0;34m(self, args, state, control)\u001b[0m\n\u001b[1;32m    461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    462\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerState\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerControl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"on_train_end\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_epoch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerState\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerControl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer_callback.py\u001b[0m in \u001b[0;36mcall_event\u001b[0;34m(self, event, args, state, control, **kwargs)\u001b[0m\n\u001b[1;32m    505\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    506\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 507\u001b[0;31m             result = getattr(callback, event)(\n\u001b[0m\u001b[1;32m    508\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    509\u001b[0m                 \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/integrations/integration_utils.py\u001b[0m in \u001b[0;36mon_train_end\u001b[0;34m(self, args, state, control, **kwargs)\u001b[0m\n\u001b[1;32m   1117\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_assets\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1118\u001b[0m                     \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Logging checkpoints. This may take time.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1119\u001b[0;31m                     self._experiment.log_asset_folder(\n\u001b[0m\u001b[1;32m   1120\u001b[0m                         \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursive\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_file_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1121\u001b[0m                     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/comet_ml/experiment.py\u001b[0m in \u001b[0;36mlog_asset_folder\u001b[0;34m(self, folder, step, log_file_name, recursive)\u001b[0m\n\u001b[1;32m   3314\u001b[0m             \u001b[0mlog_file_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3316\u001b[0;31m         return self._log_asset_folder(\n\u001b[0m\u001b[1;32m   3317\u001b[0m             \u001b[0mfolder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog_file_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog_file_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursive\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecursive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3318\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/comet_ml/experiment.py\u001b[0m in \u001b[0;36m_log_asset_folder\u001b[0;34m(self, folder, step, log_file_name, recursive, asset_type, metadata, grouping_name, folder_name, extension_filter, prepend_folder_name, overwrite)\u001b[0m\n\u001b[1;32m   3360\u001b[0m                         \u001b[0masset_file_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelpath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfolder_abs_path\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3362\u001b[0;31m                     asset_url = self._log_asset(\n\u001b[0m\u001b[1;32m   3363\u001b[0m                         \u001b[0mfile_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3364\u001b[0m                         \u001b[0mfile_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0masset_file_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/comet_ml/experiment.py\u001b[0m in \u001b[0;36m_log_asset\u001b[0;34m(self, file_data, file_name, overwrite, copy_to_tmp, asset_type, step, require_step, grouping_name, metadata, artifact_version_id, critical, asset_id, return_url, on_asset_upload, on_failed_asset_upload, framework)\u001b[0m\n\u001b[1;32m   3531\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3532\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3533\u001b[0;31m             return self._log_preprocessed_asset(\n\u001b[0m\u001b[1;32m   3534\u001b[0m                 \u001b[0mpreprocessed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3535\u001b[0m                 \u001b[0martifact_version_id\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0martifact_version_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/comet_ml/experiment.py\u001b[0m in \u001b[0;36m_log_preprocessed_asset\u001b[0;34m(self, preprocessed_asset, artifact_version_id, critical, return_url, require_step, on_asset_upload, on_failed_asset_upload)\u001b[0m\n\u001b[1;32m   3593\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpreprocessed_asset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_to_tmp\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3594\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocessed_asset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPreprocessedFileAsset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3595\u001b[0;31m                 new_upload_filepath = handle_in_memory_file_upload(\n\u001b[0m\u001b[1;32m   3596\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtmpdir\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3597\u001b[0m                     upload_filepath=ValidFilePath(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/comet_ml/file_uploader.py\u001b[0m in \u001b[0;36mhandle_in_memory_file_upload\u001b[0;34m(tmp_dir, upload_filepath, raise_on_error)\u001b[0m\n\u001b[1;32m   1383\u001b[0m         )\n\u001b[1;32m   1384\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1385\u001b[0;31m             \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopyfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mupload_filepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmpfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1386\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1387\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mraise_on_error\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/shutil.py\u001b[0m in \u001b[0;36mcopyfile\u001b[0;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[1;32m    265\u001b[0m                     \u001b[0;32melif\u001b[0m \u001b[0m_USE_CP_SENDFILE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 267\u001b[0;31m                             \u001b[0m_fastcopy_sendfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfsrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    268\u001b[0m                             \u001b[0;32mreturn\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    269\u001b[0m                         \u001b[0;32mexcept\u001b[0m \u001b[0m_GiveupOnFastCopy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/shutil.py\u001b[0m in \u001b[0;36m_fastcopy_sendfile\u001b[0;34m(fsrc, fdst)\u001b[0m\n\u001b[1;32m    140\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    141\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 142\u001b[0;31m             \u001b[0msent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msendfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutfd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblocksize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    143\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m             \u001b[0;31m# ...in oder to have a more informative exception.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "Pm2EjogfUEM6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " ## Evaluate the model"
      ],
      "metadata": {
        "id": "DOQnVa0GUGXN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics_eval(p):\n",
        "    predictions, labels = p\n",
        "    predictions = predictions.argmax(-1)  # Get the predicted class indices\n",
        "    true_predictions = [p for (p, l) in zip(predictions.flatten(), labels.flatten()) if l != -100]\n",
        "    true_labels = [l for l in labels.flatten() if l != -100]\n",
        "\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(true_labels, true_predictions, average='weighted', zero_division=1 )\n",
        "    acc = accuracy_score(true_labels, true_predictions)\n",
        "\n",
        "    # Calculate confusion matrix\n",
        "    conf_matrix = confusion_matrix(true_labels, true_predictions, normalize='true')\n",
        "\n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall,\n",
        "        'confusion_matrix': conf_matrix\n",
        "    }\n",
        "\n",
        "\n",
        "class Evaluator:\n",
        "    def __init__(self, model, tokenizer, compute_metrics):\n",
        "        self.model = model\n",
        "        self.tokenizer = tokenizer\n",
        "        self.compute_metrics = compute_metrics\n",
        "\n",
        "    def evaluate(self, eval_dataset, batch_size=16):\n",
        "        trainer = Trainer(\n",
        "            model=self.model,\n",
        "            args=training_args,\n",
        "            compute_metrics=self.compute_metrics,\n",
        "            eval_dataset=eval_dataset,\n",
        "        )\n",
        "        return trainer.evaluate()"
      ],
      "metadata": {
        "id": "f-1YI1Hc4qA9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluator = Evaluator(model, tokenizer, compute_metrics_eval)\n",
        "eval_results = evaluator.evaluate(valid_custom_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dt3RjshVT834",
        "outputId": "1f61fdf2-1bf9-4e41-cd7c-99de0bd4669a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='63' max='63' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [63/63 00:17]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Cannot safely convert array([[9.91107518e-01, 7.41040151e-04, 2.02101859e-04, 2.02101859e-03,\n",
            "        1.27997844e-03, 1.34734573e-04, 2.02101859e-04, 2.89679332e-03,\n",
            "        1.41471302e-03],\n",
            "       [5.94530321e-03, 9.63139120e-01, 9.51248514e-03, 9.51248514e-03,\n",
            "        0.00000000e+00, 8.32342449e-03, 0.00000000e+00, 3.56718193e-03,\n",
            "        0.00000000e+00],\n",
            "       [3.96825397e-03, 1.19047619e-02, 9.65608466e-01, 1.58730159e-02,\n",
            "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.64550265e-03,\n",
            "        0.00000000e+00],\n",
            "       [2.83911672e-02, 2.99684543e-02, 3.15457413e-03, 8.67507886e-01,\n",
            "        4.73186120e-03, 5.04731861e-02, 0.00000000e+00, 1.57728707e-02,\n",
            "        0.00000000e+00],\n",
            "       [1.19122257e-01, 6.26959248e-03, 2.19435737e-02, 5.95611285e-02,\n",
            "        7.61755486e-01, 1.25391850e-02, 1.56739812e-02, 0.00000000e+00,\n",
            "        3.13479624e-03],\n",
            "       [3.87096774e-03, 3.87096774e-03, 1.29032258e-03, 2.58064516e-02,\n",
            "        0.00000000e+00, 9.34193548e-01, 1.29032258e-02, 1.67741935e-02,\n",
            "        1.29032258e-03],\n",
            "       [3.48837209e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
            "        6.97674419e-02, 1.16279070e-02, 8.37209302e-01, 0.00000000e+00,\n",
            "        4.65116279e-02],\n",
            "       [9.21787709e-02, 2.51396648e-02, 0.00000000e+00, 1.39664804e-02,\n",
            "        0.00000000e+00, 7.54189944e-02, 0.00000000e+00, 7.68156425e-01,\n",
            "        2.51396648e-02],\n",
            "       [1.09890110e-01, 0.00000000e+00, 2.19780220e-02, 0.00000000e+00,\n",
            "        5.49450549e-02, 0.00000000e+00, 4.39560440e-02, 1.09890110e-01,\n",
            "        6.59340659e-01]]) object to a scalar value, using its string representation for logging. Resulting string might be invalid\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m String value length exceeds 1000 characters and will be truncated. Provided value: '[[9.91107518e-01 7.41040151e-04 2.02101859e-04 2.02101859e-03\n",
            "  1.27997844e-03 1.34734573e-04 2.02101859e-04 2.89679332e-03\n",
            "  1.41471302e-03]\n",
            " [5.94530321e-03 9.63139120e-01 9.51248514e-03 9.51248514e-03\n",
            "  0.00000000e+00 8.32342449e-03 0.00000000e+00 3.56718193e-03\n",
            "  0.00000000e+00]\n",
            " [3.96825397e-03 1.19047619e-02 9.65608466e-01 1.58730159e-02\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.64550265e-03\n",
            "  0.00000000e+00]\n",
            " [2.83911672e-02 2.99684543e-02 3.15457413e-03 8.67507886e-01\n",
            "  4.73186120e-03 5.04731861e-02 0.00000000e+00 1.57728707e-02\n",
            "  0.00000000e+00]\n",
            " [1.19122257e-01 6.26959248e-03 2.19435737e-02 5.95611285e-02\n",
            "  7.61755486e-01 1.25391850e-02 1.56739812e-02 0.00000000e+00\n",
            "  3.13479624e-03]\n",
            " [3.87096774e-03 3.87096774e-03 1.29032258e-03 2.58064516e-02\n",
            "  0.00000000e+00 9.34193548e-01 1.29032258e-02 1.67741935e-02\n",
            "  1.29032258e-03]\n",
            " [3.48837209e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  6.97674419e-02 1.16279070e-02 8.37209302e-01 0.00000000e+00\n",
            "  4.65116279e-02]\n",
            " [9.21787709e-02 2.51396648e-02 0.00000000e+00 1.39664804e-02\n",
            "  0.00000000e+00 7.54189944e-02 0.00000000e+00 7.68156425e-01\n",
            "  2.51396648e-02]\n",
            " [1.09890110e-01 0.00000000e+00 2.19780220e-02 0.00000000e+00\n",
            "  5.49450549e-02 0.00000000e+00 4.39560440e-02 1.09890110e-01\n",
            "  6.59340659e-01]]'\n",
            "Trainer is attempting to log a value of \"[[9.91107518e-01 7.41040151e-04 2.02101859e-04 2.02101859e-03\n",
            "  1.27997844e-03 1.34734573e-04 2.02101859e-04 2.89679332e-03\n",
            "  1.41471302e-03]\n",
            " [5.94530321e-03 9.63139120e-01 9.51248514e-03 9.51248514e-03\n",
            "  0.00000000e+00 8.32342449e-03 0.00000000e+00 3.56718193e-03\n",
            "  0.00000000e+00]\n",
            " [3.96825397e-03 1.19047619e-02 9.65608466e-01 1.58730159e-02\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.64550265e-03\n",
            "  0.00000000e+00]\n",
            " [2.83911672e-02 2.99684543e-02 3.15457413e-03 8.67507886e-01\n",
            "  4.73186120e-03 5.04731861e-02 0.00000000e+00 1.57728707e-02\n",
            "  0.00000000e+00]\n",
            " [1.19122257e-01 6.26959248e-03 2.19435737e-02 5.95611285e-02\n",
            "  7.61755486e-01 1.25391850e-02 1.56739812e-02 0.00000000e+00\n",
            "  3.13479624e-03]\n",
            " [3.87096774e-03 3.87096774e-03 1.29032258e-03 2.58064516e-02\n",
            "  0.00000000e+00 9.34193548e-01 1.29032258e-02 1.67741935e-02\n",
            "  1.29032258e-03]\n",
            " [3.48837209e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  6.97674419e-02 1.16279070e-02 8.37209302e-01 0.00000000e+00\n",
            "  4.65116279e-02]\n",
            " [9.21787709e-02 2.51396648e-02 0.00000000e+00 1.39664804e-02\n",
            "  0.00000000e+00 7.54189944e-02 0.00000000e+00 7.68156425e-01\n",
            "  2.51396648e-02]\n",
            " [1.09890110e-01 0.00000000e+00 2.19780220e-02 0.00000000e+00\n",
            "  5.49450549e-02 0.00000000e+00 4.39560440e-02 1.09890110e-01\n",
            "  6.59340659e-01]]\" of type <class 'numpy.ndarray'> for key \"eval/confusion_matrix\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for key, value in eval_results.items():\n",
        "  if key != 'eval_confusion_matrix':\n",
        "    print(f\"{key}: {value}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ImlTuiO70BUl",
        "outputId": "40892d18-df33-4475-a5fe-b89474518d13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:urllib3.connectionpool:Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'ReadTimeoutError(\"HTTPSConnectionPool(host='www.comet.com', port=443): Read timed out. (read timeout=10)\")': /api/rest/v2/write/experiment/log-other\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "eval_loss: 0.14644870162010193\n",
            "eval_model_preparation_time: 0.0138\n",
            "eval_accuracy: 0.9717707442258341\n",
            "eval_f1: 0.9717443625198972\n",
            "eval_precision: 0.9719172534270535\n",
            "eval_recall: 0.9717707442258341\n",
            "eval_runtime: 17.8971\n",
            "eval_samples_per_second: 55.875\n",
            "eval_steps_per_second: 3.52\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_confusion_matrix(cm, class_names):\n",
        "\n",
        "    # Plot confusion matrix\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
        "    plt.title('Normalized confusion Matrix')\n",
        "    plt.xlabel('Predicted Label')\n",
        "    plt.ylabel('True Label')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "izaVeKqPT9zn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "labels_dct = {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}\n",
        "class_names = [k for k, v in labels_dct.items()]\n",
        "plot_confusion_matrix(eval_results['eval_confusion_matrix'], class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "DSQ0CLJ5xzH3",
        "outputId": "fc44489e-860f-4c1a-cfba-a2ea15d92373"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RGztuzUbDvhR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Registry model"
      ],
      "metadata": {
        "id": "Y-L4c2gfDwR0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "experiment = comet_ml.ExistingExperiment(api_key=COMET_API_KEY, previous_experiment=\"04862a2a88704c2fba3522d9acfb9564\")\n",
        "experiment.log_model(\"BERT-base-NER-1k-bs_24-last\", \"results/checkpoint-588\")\n",
        "experiment.register_model(\"BERT-base-NER-1k-bs_24-last\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "svgprdWB0RA7",
        "outputId": "bf4d20b6-fbd0-4ad5-ec00-6ee8dd98232d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml ExistingExperiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : intense_adapter_2598\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/gonzaq94/ner-classif-bert-2/04862a2a88704c2fba3522d9acfb9564\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element : 3 (7.06 KB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/gonzaq94/ner-classif-bert-2/04862a2a88704c2fba3522d9acfb9564\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Deploy model"
      ],
      "metadata": {
        "id": "Ayrer7IkD6jb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "tOcJ9JHTsMlQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from comet_ml import API\n",
        "\n",
        "api = API(api_key=COMET_API_KEY)\n",
        "COMET_WORKSPACE = \"gonzaq94\"\n",
        "\n",
        "\n",
        "# model name\n",
        "model_name = \"BERT-base-NER-1k-bs_24-last\"\n",
        "\n",
        "#get the Model object\n",
        "model = api.get_model(workspace=COMET_WORKSPACE, model_name=model_name)\n",
        "\n",
        "# Download a Registry Model:\n",
        "model.download(\"1.0.0\", \"./deploy\", expand=True)"
      ],
      "metadata": {
        "id": "yG7gvIG2CVqr",
        "outputId": "2230d5cf-9c08-45c2-f01f-f181a064c52d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Remote Model 'gonzaq94/BERT-base-NER-1k-bs_24-last:1.0.0' download has been started asynchronously.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still downloading 9 file(s), remaining 415.47 MB/415.47 MB\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-588/rng_state.pth' has been overwritten by asset '30d89e8ba7754e538f5e00e0b966f94a' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-588/scheduler.pt' has been overwritten by asset '0f1700e00a42436bbd4fc706563c5bb4' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-588/config.json' has been overwritten by asset '08fe1243f6d642bead4c8cf22a840137' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-588/config.json' has been overwritten by asset '24957ce56c7e4b789a6c9999042d6ac8' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-588/training_args.bin' has been overwritten by asset '3dd7aee75ea94f069c28dfa77a1f6871' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-588/trainer_state.json' has been overwritten by asset 'c3cbead9cfee40deb6cfc92e07b03057' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-588/training_args.bin' has been overwritten by asset 'd4a85149234841ebacc2c4df8b58360d' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-588/scheduler.pt' has been overwritten by asset 'ef40827c868e4449a58b2aec56aee5b7' of remote model\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m File './deploy/checkpoint-588/model.safetensors' has been overwritten by asset 'bc61fb60ef104098806bb07e1021afde' of remote model\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Remote Model 'gonzaq94/BERT-base-NER-1k-bs_24-last:1.0.0' has been successfully downloaded.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Downloaded asset files is in './deploy' folder.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForTokenClassification.from_pretrained(\"./deploy/checkpoint-588\", num_labels=9)"
      ],
      "metadata": {
        "id": "YD-mgUMPDKL8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluator = Evaluator(model, tokenizer, compute_metrics_eval)\n",
        "eval_results = evaluator.evaluate(test_custom_dataset)"
      ],
      "metadata": {
        "id": "UKlhfmtyCV6j",
        "outputId": "a3b62a20-ff9f-4e92-8a83-dbbcf8bb0a55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='63' max='63' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [63/63 00:16]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Cannot safely convert array([[9.91174885e-01, 7.41040151e-04, 2.02101859e-04, 1.75154945e-03,\n",
            "        1.21261116e-03, 1.34734573e-04, 2.02101859e-04, 3.09889518e-03,\n",
            "        1.48208030e-03],\n",
            "       [5.94530321e-03, 9.54815696e-01, 9.51248514e-03, 1.30796671e-02,\n",
            "        2.37812128e-03, 1.07015458e-02, 0.00000000e+00, 3.56718193e-03,\n",
            "        0.00000000e+00],\n",
            "       [3.96825397e-03, 1.32275132e-02, 9.64285714e-01, 1.58730159e-02,\n",
            "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.64550265e-03,\n",
            "        0.00000000e+00],\n",
            "       [2.99684543e-02, 2.52365931e-02, 3.15457413e-03, 8.69085174e-01,\n",
            "        4.73186120e-03, 5.20504732e-02, 0.00000000e+00, 1.57728707e-02,\n",
            "        0.00000000e+00],\n",
            "       [1.22257053e-01, 6.26959248e-03, 2.19435737e-02, 5.95611285e-02,\n",
            "        7.49216301e-01, 1.25391850e-02, 1.88087774e-02, 0.00000000e+00,\n",
            "        9.40438871e-03],\n",
            "       [3.87096774e-03, 3.87096774e-03, 1.29032258e-03, 2.58064516e-02,\n",
            "        0.00000000e+00, 9.34193548e-01, 1.29032258e-02, 1.67741935e-02,\n",
            "        1.29032258e-03],\n",
            "       [3.48837209e-02, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
            "        6.97674419e-02, 1.16279070e-02, 8.37209302e-01, 0.00000000e+00,\n",
            "        4.65116279e-02],\n",
            "       [8.93854749e-02, 2.51396648e-02, 0.00000000e+00, 1.67597765e-02,\n",
            "        0.00000000e+00, 7.54189944e-02, 0.00000000e+00, 7.68156425e-01,\n",
            "        2.51396648e-02],\n",
            "       [1.20879121e-01, 0.00000000e+00, 2.19780220e-02, 0.00000000e+00,\n",
            "        5.49450549e-02, 0.00000000e+00, 4.39560440e-02, 1.20879121e-01,\n",
            "        6.37362637e-01]]) object to a scalar value, using its string representation for logging. Resulting string might be invalid\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m String value length exceeds 1000 characters and will be truncated. Provided value: '[[9.91174885e-01 7.41040151e-04 2.02101859e-04 1.75154945e-03\n",
            "  1.21261116e-03 1.34734573e-04 2.02101859e-04 3.09889518e-03\n",
            "  1.48208030e-03]\n",
            " [5.94530321e-03 9.54815696e-01 9.51248514e-03 1.30796671e-02\n",
            "  2.37812128e-03 1.07015458e-02 0.00000000e+00 3.56718193e-03\n",
            "  0.00000000e+00]\n",
            " [3.96825397e-03 1.32275132e-02 9.64285714e-01 1.58730159e-02\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.64550265e-03\n",
            "  0.00000000e+00]\n",
            " [2.99684543e-02 2.52365931e-02 3.15457413e-03 8.69085174e-01\n",
            "  4.73186120e-03 5.20504732e-02 0.00000000e+00 1.57728707e-02\n",
            "  0.00000000e+00]\n",
            " [1.22257053e-01 6.26959248e-03 2.19435737e-02 5.95611285e-02\n",
            "  7.49216301e-01 1.25391850e-02 1.88087774e-02 0.00000000e+00\n",
            "  9.40438871e-03]\n",
            " [3.87096774e-03 3.87096774e-03 1.29032258e-03 2.58064516e-02\n",
            "  0.00000000e+00 9.34193548e-01 1.29032258e-02 1.67741935e-02\n",
            "  1.29032258e-03]\n",
            " [3.48837209e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  6.97674419e-02 1.16279070e-02 8.37209302e-01 0.00000000e+00\n",
            "  4.65116279e-02]\n",
            " [8.93854749e-02 2.51396648e-02 0.00000000e+00 1.67597765e-02\n",
            "  0.00000000e+00 7.54189944e-02 0.00000000e+00 7.68156425e-01\n",
            "  2.51396648e-02]\n",
            " [1.20879121e-01 0.00000000e+00 2.19780220e-02 0.00000000e+00\n",
            "  5.49450549e-02 0.00000000e+00 4.39560440e-02 1.20879121e-01\n",
            "  6.37362637e-01]]'\n",
            "Trainer is attempting to log a value of \"[[9.91174885e-01 7.41040151e-04 2.02101859e-04 1.75154945e-03\n",
            "  1.21261116e-03 1.34734573e-04 2.02101859e-04 3.09889518e-03\n",
            "  1.48208030e-03]\n",
            " [5.94530321e-03 9.54815696e-01 9.51248514e-03 1.30796671e-02\n",
            "  2.37812128e-03 1.07015458e-02 0.00000000e+00 3.56718193e-03\n",
            "  0.00000000e+00]\n",
            " [3.96825397e-03 1.32275132e-02 9.64285714e-01 1.58730159e-02\n",
            "  0.00000000e+00 0.00000000e+00 0.00000000e+00 2.64550265e-03\n",
            "  0.00000000e+00]\n",
            " [2.99684543e-02 2.52365931e-02 3.15457413e-03 8.69085174e-01\n",
            "  4.73186120e-03 5.20504732e-02 0.00000000e+00 1.57728707e-02\n",
            "  0.00000000e+00]\n",
            " [1.22257053e-01 6.26959248e-03 2.19435737e-02 5.95611285e-02\n",
            "  7.49216301e-01 1.25391850e-02 1.88087774e-02 0.00000000e+00\n",
            "  9.40438871e-03]\n",
            " [3.87096774e-03 3.87096774e-03 1.29032258e-03 2.58064516e-02\n",
            "  0.00000000e+00 9.34193548e-01 1.29032258e-02 1.67741935e-02\n",
            "  1.29032258e-03]\n",
            " [3.48837209e-02 0.00000000e+00 0.00000000e+00 0.00000000e+00\n",
            "  6.97674419e-02 1.16279070e-02 8.37209302e-01 0.00000000e+00\n",
            "  4.65116279e-02]\n",
            " [8.93854749e-02 2.51396648e-02 0.00000000e+00 1.67597765e-02\n",
            "  0.00000000e+00 7.54189944e-02 0.00000000e+00 7.68156425e-01\n",
            "  2.51396648e-02]\n",
            " [1.20879121e-01 0.00000000e+00 2.19780220e-02 0.00000000e+00\n",
            "  5.49450549e-02 0.00000000e+00 4.39560440e-02 1.20879121e-01\n",
            "  6.37362637e-01]]\" of type <class 'numpy.ndarray'> for key \"eval/confusion_matrix\" as a scalar. This invocation of Tensorboard's writer.add_scalar() is incorrect so we dropped this attribute.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for key, value in eval_results.items():\n",
        "  if key != 'eval_confusion_matrix':\n",
        "    print(f\"{key}: {value}\")"
      ],
      "metadata": {
        "id": "uRORSYApEBur",
        "outputId": "8a443a2e-4c4d-4070-ea6e-848c3409d78e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "eval_loss: 0.1464821696281433\n",
            "eval_model_preparation_time: 0.0137\n",
            "eval_accuracy: 0.9711291702309667\n",
            "eval_f1: 0.9711212303632004\n",
            "eval_precision: 0.9713346140071276\n",
            "eval_recall: 0.9711291702309667\n",
            "eval_runtime: 16.8615\n",
            "eval_samples_per_second: 59.307\n",
            "eval_steps_per_second: 3.736\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-tune with different train size"
      ],
      "metadata": {
        "id": "pCztwdy9sN_N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import userdata\n",
        "\n",
        "# log results and assets to Comet\n",
        "os.environ[\"COMET_LOG_ASSETS\"] = \"True\"\n",
        "os.environ[\"COMET_PROJECT_NAME\"] = \"ner_classif-bert-diff_train_size-constant_its\"\n",
        "os.environ[\"COMET_GIT_DIRECTORY\"] = \"https://github.com/gonzaq94/NER-LLM-classification\"\n",
        "os.environ[\"COMET_WORKSPACE\"] = userdata.get('COMET_WORKSPACE')\n",
        "os.environ[\"COMET_API_KEY\"] = userdata.get('COMET_API_KEY')"
      ],
      "metadata": {
        "id": "HkLqcazjDF8n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the CONLL dataset\n",
        "dataset_train = load_dataset(\"conll2003\", split=\"train\")\n",
        "dataset_valid = load_dataset(\"conll2003\", split=\"validation\")\n",
        "dataset_test = load_dataset(\"conll2003\", split=\"test\")"
      ],
      "metadata": {
        "id": "O8WC8aGJsW8f"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(p):\n",
        "    predictions, labels = p\n",
        "    predictions = predictions.argmax(-1)  # Get the predicted class indices\n",
        "    true_predictions = [p for (p, l) in zip(predictions.flatten(), labels.flatten()) if l != -100]\n",
        "    true_labels = [l for l in labels.flatten() if l != -100]\n",
        "\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(true_labels, true_predictions, average='weighted', zero_division=1 )\n",
        "    acc = accuracy_score(true_labels, true_predictions)\n",
        "\n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall\n",
        "    }"
      ],
      "metadata": {
        "id": "hF81NhausYhC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class CustomDataset(Dataset):\n",
        "    def __init__(self, texts, labels, tokenizer, max_length):\n",
        "        self.texts = texts\n",
        "        self.labels = labels\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.texts)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        text = self.texts[idx]\n",
        "        labels = self.labels[idx]\n",
        "\n",
        "        tokens = []\n",
        "        tokens_labels = []\n",
        "        attention_masks = []\n",
        "\n",
        "        for word, label in zip(text, labels):\n",
        "          # Tokenize each word\n",
        "          word_encoding = self.tokenizer(\n",
        "              word,\n",
        "              add_special_tokens=False,\n",
        "              max_length=self.max_length,\n",
        "              truncation=True,\n",
        "              return_tensors=\"pt\"\n",
        "          )\n",
        "\n",
        "          tokens.extend(word_encoding['input_ids'][0].tolist())\n",
        "          attention_masks.extend(word_encoding['attention_mask'][0].tolist())\n",
        "          tokens_labels.extend([label] * len(word_encoding['input_ids'][0]))\n",
        "\n",
        "        # add padding\n",
        "        padding_len = self.max_length - len(tokens)\n",
        "        tokens += [0] * padding_len\n",
        "        attention_masks += [0] * padding_len\n",
        "        tokens_labels += [-100] * padding_len\n",
        "\n",
        "        return {\n",
        "            'input_ids': torch.tensor(tokens).flatten(),\n",
        "            'attention_mask': torch.tensor(attention_masks).flatten(),\n",
        "            'labels': torch.tensor(tokens_labels).flatten()\n",
        "        }"
      ],
      "metadata": {
        "id": "8uALquGjsZoc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers.integrations import CometCallback\n",
        "from transformers import TrainerCallback\n",
        "\n",
        "model_name = 'bert-base-uncased'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "def get_downsampled_datasets(orig_train_set, orig_valid_set, dsize):\n",
        "  # downsample datasets\n",
        "  ds_dataset_train = orig_train_set.shuffle().select(range(dsize))\n",
        "  ds_dataset_valid = orig_valid_set.shuffle().select(range(dsize))\n",
        "\n",
        "  train_custom_dataset = CustomDataset(\n",
        "      texts=ds_dataset_train['tokens'],\n",
        "      labels=ds_dataset_train['ner_tags'],\n",
        "      tokenizer=tokenizer,\n",
        "      max_length=256\n",
        "  )\n",
        "\n",
        "  valid_custom_dataset = CustomDataset(\n",
        "      texts=ds_dataset_valid['tokens'],\n",
        "      labels=ds_dataset_valid['ner_tags'],\n",
        "      tokenizer=tokenizer,\n",
        "      max_length=256\n",
        "  )\n",
        "\n",
        "  return train_custom_dataset, valid_custom_dataset\n",
        "\n",
        "def get_trainer(niterations, experiment, model, train_ds, valid_ds, batch_size=24):\n",
        "\n",
        "  class CustomCometCallback(CometCallback):\n",
        "\n",
        "    def __init__(self, experiment):\n",
        "        super().__init__()\n",
        "        self._experiment = experiment\n",
        "\n",
        "    \"\"\"\n",
        "    def on_train_end(self, args, state, control, **kwargs):\n",
        "        if self._experiment:  # Check if the experiment exists\n",
        "            self._experiment.end()  # Ends the experiment properly when training ends\n",
        "\n",
        "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
        "        print(self._log_assets)\n",
        "        if self._experiment and logs:  # Ensure experiment and logs are valid\n",
        "            global_step = state.global_step  # Access the global_step from the state\n",
        "            for key, value in logs.items():\n",
        "                if isinstance(value, (int, float)):  # Check if the value is a scalar\n",
        "                    self._experiment.log_metric(key, value, step=global_step)\n",
        "    \"\"\"\n",
        "    \"\"\"\n",
        "    def on_train_end(self, args, state, control, **kwargs):\n",
        "        self.experiment.end()  # Ends the experiment properly when training ends\n",
        "\n",
        "    def on_log(self, args, state, control, logs=None, **kwargs):\n",
        "        if self.experiment:\n",
        "            global_step = state.global_step  # Access the global_step from the state\n",
        "            for key, value in logs.items():\n",
        "                self.experiment.log_metric(key, value, step=global_step)\n",
        "    \"\"\"\n",
        "\n",
        "  training_args = TrainingArguments(\n",
        "    output_dir='./results',\n",
        "    max_steps=niterations,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"f1\",\n",
        "    greater_is_better=True,\n",
        "    save_total_limit=1,\n",
        "    #num_train_epochs=nepochs,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=niterations // 15,\n",
        "    save_strategy=\"steps\",\n",
        "    save_steps=niterations // 15,\n",
        "    logging_strategy=\"no\",\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    warmup_steps=niterations // 10,\n",
        "    weight_decay=0.01,\n",
        "    logging_dir=None,\n",
        "    dataloader_drop_last=False,\n",
        "    )\n",
        "\n",
        "  trainer = Trainer(\n",
        "      model=model,\n",
        "      args=training_args,\n",
        "      train_dataset=train_ds,\n",
        "      eval_dataset=valid_ds,\n",
        "      compute_metrics=compute_metrics,\n",
        "      callbacks=[CustomCometCallback(experiment=experiment)],\n",
        "  )\n",
        "\n",
        "  return trainer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G7NDrZyhsawt",
        "outputId": "bac77543-f64d-46e5-e7e1-2dbe0796d13c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!rm -r ./results"
      ],
      "metadata": {
        "id": "UudO4YIa4nwL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "import os\n",
        "\n",
        "#dataset_sizes = [100, 300, 1000]\n",
        "dataset_sizes = [10, 30, 100, 300, 1000]\n",
        "# set the same number of iterations for each run, so as to have 15 epochs for a training dataset of size 1000 and a batch size of 24\n",
        "niterations = int((1000 / 24)*15)\n",
        "#niterations = 2\n",
        "\n",
        "for dsize in dataset_sizes:\n",
        "\n",
        "  # define comet experiment\n",
        "  experiment = comet_ml.Experiment(\n",
        "    api_key=os.getenv(\"COMET_API_KEY\"),\n",
        "    project_name=os.getenv(\"COMET_PROJECT_NAME\"),\n",
        "    workspace=os.getenv(\"COMET_WORKSPACE\"))\n",
        "\n",
        "  experiment.set_name(f\"train_size_{dsize}\")\n",
        "\n",
        "  # sample datasets\n",
        "  train_ds, valid_ds = get_downsampled_datasets(dataset_train, dataset_valid, dsize)\n",
        "\n",
        "  # reload model and trainer\n",
        "  model = AutoModelForTokenClassification.from_pretrained(model_name, num_labels=9)\n",
        "  trainer = get_trainer(niterations, experiment, model, train_ds, valid_ds, batch_size=24)\n",
        "\n",
        "  # train\n",
        "  trainer.train()\n",
        "\n",
        "  # Copy the best model checkpoint to a \"best_model\" directory with a custom name\n",
        "  best_model_dir = \"./results/best_model\"\n",
        "  if os.path.exists(best_model_dir):\n",
        "      shutil.rmtree(best_model_dir)  # Remove existing directory if needed\n",
        "  os.makedirs(best_model_dir, exist_ok=True)\n",
        "  shutil.copytree(trainer.state.best_model_checkpoint, best_model_dir, dirs_exist_ok=True)\n",
        "\n",
        "  experiment.log_model(f\"BERT-base-NER-size{dsize}\", \"results/best_model\")\n",
        "  experiment.register_model(f\"BERT-base-NER-size{dsize}\")\n",
        "\n",
        "  # remove files\n",
        "  shutil.rmtree('./results')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gq3MHYX9shXC",
        "outputId": "ec4a38f2-9a47-49df-c92e-72fb070a6cd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : train_size_10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/59a254c72b8542efa299710d4e48c987\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epoch [2]                   : (41.0, 82.0)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/accuracy [2]           : (0.8421052631578947, 0.8526315789473684)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/f1 [2]                 : (0.7848086124401913, 0.8132181199266822)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/loss [2]               : (0.5484954714775085, 0.6174136996269226)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/precision [2]          : (0.8343017949498023, 0.8743490304709142)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/recall [2]             : (0.8421052631578947, 0.8526315789473684)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/runtime [2]            : (0.1617, 0.1625)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/samples_per_second [2] : (61.552, 61.844)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/steps_per_second [2]   : (6.155, 6.184)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_accuracy [2]           : (0.8421052631578947, 0.8526315789473684)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_f1 [2]                 : (0.7848086124401913, 0.8132181199266822)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_loss [2]               : (0.5484954714775085, 0.6174136996269226)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_precision [2]          : (0.8343017949498023, 0.8743490304709142)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_recall [2]             : (0.8421052631578947, 0.8526315789473684)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_runtime [2]            : (0.1617, 0.1625)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_samples_per_second [2] : (61.552, 61.844)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_steps_per_second [2]   : (6.155, 6.184)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [9]                    : (0.008171292953193188, 2.234769105911255)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/epoch [2]             : (41.0, 82.0)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name            : train_size_10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook_url    : https://colab.research.google.com/notebook#fileId=https%3A%2F%2Fgithub.com%2Fgonzaq94%2FNER-LLM-classification%2Fblob%2Fmain%2Ffine_tune_BERT_dataset_size.ipynb\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|dispatch_batches             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|even_batches                 : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|gradient_accumulation_kwargs : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|non_blocking                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|split_batches                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|use_seedable_sampler         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adafactor                                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta1                                      : 0.9\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta2                                      : 0.999\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_epsilon                                    : 1e-08\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|auto_find_batch_size                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|batch_eval_metrics                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|data_seed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_drop_last                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_num_workers                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_persistent_workers                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_pin_memory                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_prefetch_factor                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_backend                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_broadcast_buffers                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_bucket_cap_mb                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_find_unused_parameters                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_timeout                                     : 1800\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|debug                                           : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|deepspeed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|disable_tqdm                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dispatch_batches                                : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_eval                                         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_predict                                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_train                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_accumulation_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_delay                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_do_concat_batches                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_on_start                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_use_gather_object                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|evaluation_strategy                             : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_backend                                    : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_opt_level                                  : O1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp                                            : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|min_num_params                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_grad_ckpt                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_v2                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_min_num_params                             : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_transformer_layer_cls_to_wrap              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|full_determinism                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_accumulation_steps                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing_kwargs                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|greater_is_better                               : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|group_by_length                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|half_precision_backend                          : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_always_push                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_model_id                                    : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_private_repo                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_strategy                                    : every_save\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_token                                       : <HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ignore_data_skip                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_inputs_for_metrics                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_num_input_tokens_seen                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_tokens_per_second                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|jit_mode_eval                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_names                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_smoothing_factor                          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|learning_rate                                   : 5e-05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|length_column_name                              : length\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|load_best_model_at_end                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|local_rank                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level                                       : passive\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level_replica                               : warning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_on_each_node                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_dir                                     : ./results/runs/Nov04_16-26-46_1eb698b7797d\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_first_step                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_nan_inf_filter                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_steps                                   : 500\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_strategy                                : no\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_kwargs                             : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_type                               : linear\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_grad_norm                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_steps                                       : 625\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|metric_for_best_model                           : f1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|mp_parameters                                   : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|neftune_noise_alpha                             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|no_cuda                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|num_train_epochs                                : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim                                           : adamw_torch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_args                                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_target_modules                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|output_dir                                      : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|overwrite_output_dir                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|past_index                                      : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_eval_batch_size                      : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_train_batch_size                     : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_eval_batch_size                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_train_batch_size                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|prediction_loss_only                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_model_id                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_organization                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_token                               : <PUSH_TO_HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ray_scope                                       : last\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|remove_unused_columns                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|report_to                                       : ['tensorboard', 'wandb']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|restore_callback_states_from_checkpoint         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|resume_from_checkpoint                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|run_name                                        : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_on_each_node                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_only_model                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_safetensors                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_total_limit                                : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|seed                                            : 42\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|skip_memory_metrics                             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|split_batches                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tf32                                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_backend                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_mode                              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_empty_cache_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torchdynamo                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_metrics_debug                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_num_cores                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_cpu                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_ipex                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_legacy_prediction_loop                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_mps_device                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_ratio                                    : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_steps                                    : 62\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|weight_decay                                    : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|_name_or_path                                 : bert-base-uncased\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|add_cross_attention                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|architectures                                 : ['BertForMaskedLM']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|attention_probs_dropout_prob                  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bad_words_ids                                 : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|begin_suppress_tokens                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|chunk_size_feed_forward                       : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|classifier_dropout                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|cross_attention_hidden_size                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|decoder_start_token_id                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|diversity_penalty                             : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|do_sample                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|early_stopping                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|encoder_no_repeat_ngram_size                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|eos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|exponential_decay_length_penalty              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|finetuning_task                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_bos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_eos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|gradient_checkpointing                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_act                                    : gelu\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_dropout_prob                           : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_size                                   : 768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|0                                    : LABEL_0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|1                                    : LABEL_1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|2                                    : LABEL_2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|3                                    : LABEL_3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|4                                    : LABEL_4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|5                                    : LABEL_5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|6                                    : LABEL_6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|7                                    : LABEL_7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|8                                    : LABEL_8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|initializer_range                             : 0.02\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|intermediate_size                             : 3072\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_decoder                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_encoder_decoder                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_0                              : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_1                              : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_2                              : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_3                              : 3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_4                              : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_5                              : 5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_6                              : 6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_7                              : 7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_8                              : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|layer_norm_eps                                : 1e-12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|length_penalty                                : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_length                                    : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_position_embeddings                       : 512\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|min_length                                    : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|model_type                                    : bert\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|no_repeat_ngram_size                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_attention_heads                           : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beam_groups                               : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beams                                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_hidden_layers                             : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_return_sequences                          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_attentions                             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_hidden_states                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_scores                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pad_token_id                                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|position_embedding_type                       : absolute\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|prefix                                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|problem_type                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pruned_heads                                  : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|remove_invalid_values                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|repetition_penalty                            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict                                   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict_in_generate                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|sep_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|suppress_tokens                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|task_specific_params                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|temperature                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tf_legacy_loss                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_encoder_decoder                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_word_embeddings                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tokenizer_class                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_k                                         : 50\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_p                                         : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torch_dtype                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torchscript                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|transformers_version                          : 4.44.2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|type_vocab_size                               : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|typical_p                                     : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_bfloat16                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_cache                                     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|vocab_size                                    : 30522\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook            : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/d8e6b0491a484ddb82ae1132f8cd1ec6\n",
            "\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in 'https://github.com/gonzaq94/NER-LLM-classification' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "comet_ml is installed but the Comet API Key is not configured. Please set the `COMET_API_KEY` environment variable to enable Comet logging. Check out the documentation for other ways of configuring it: https://www.comet.com/docs/v2/guides/experiment-management/configure-sdk/#set-the-api-key\n",
            "max_steps is given, it will override any value given in num_train_epochs\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [625/625 08:16, Epoch 625/625]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.555364</td>\n",
              "      <td>0.852632</td>\n",
              "      <td>0.784809</td>\n",
              "      <td>0.874349</td>\n",
              "      <td>0.852632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.591152</td>\n",
              "      <td>0.836842</td>\n",
              "      <td>0.831566</td>\n",
              "      <td>0.885479</td>\n",
              "      <td>0.836842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.676981</td>\n",
              "      <td>0.852632</td>\n",
              "      <td>0.864077</td>\n",
              "      <td>0.914261</td>\n",
              "      <td>0.852632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.662133</td>\n",
              "      <td>0.852632</td>\n",
              "      <td>0.861831</td>\n",
              "      <td>0.921502</td>\n",
              "      <td>0.852632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.704575</td>\n",
              "      <td>0.847368</td>\n",
              "      <td>0.847339</td>\n",
              "      <td>0.881843</td>\n",
              "      <td>0.847368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.730360</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.840449</td>\n",
              "      <td>0.874826</td>\n",
              "      <td>0.842105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>287</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.741894</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.840449</td>\n",
              "      <td>0.874826</td>\n",
              "      <td>0.842105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>328</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.764208</td>\n",
              "      <td>0.847368</td>\n",
              "      <td>0.844858</td>\n",
              "      <td>0.877014</td>\n",
              "      <td>0.847368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>369</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.777007</td>\n",
              "      <td>0.847368</td>\n",
              "      <td>0.844858</td>\n",
              "      <td>0.877014</td>\n",
              "      <td>0.847368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.789991</td>\n",
              "      <td>0.847368</td>\n",
              "      <td>0.844858</td>\n",
              "      <td>0.877014</td>\n",
              "      <td>0.847368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>451</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.799041</td>\n",
              "      <td>0.847368</td>\n",
              "      <td>0.844858</td>\n",
              "      <td>0.877014</td>\n",
              "      <td>0.847368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>492</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.806181</td>\n",
              "      <td>0.847368</td>\n",
              "      <td>0.844858</td>\n",
              "      <td>0.877014</td>\n",
              "      <td>0.847368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>533</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.810122</td>\n",
              "      <td>0.847368</td>\n",
              "      <td>0.844858</td>\n",
              "      <td>0.877014</td>\n",
              "      <td>0.847368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>574</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.813231</td>\n",
              "      <td>0.847368</td>\n",
              "      <td>0.844858</td>\n",
              "      <td>0.877014</td>\n",
              "      <td>0.847368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>615</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.812050</td>\n",
              "      <td>0.847368</td>\n",
              "      <td>0.847339</td>\n",
              "      <td>0.881843</td>\n",
              "      <td>0.847368</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Successfully registered 'BERT-base-NER-size10', version None in workspace 'gonzaq94'\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : train_size_10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/d8e6b0491a484ddb82ae1132f8cd1ec6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epoch [16]                     : (41.0, 625.0)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/accuracy [15]             : (0.8368421052631579, 0.8526315789473684)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/f1 [15]                   : (0.7848086124401913, 0.8640765550239233)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/loss [15]                 : (0.5553635954856873, 0.8132314682006836)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/precision [15]            : (0.8743490304709142, 0.9215019255455713)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/recall [15]               : (0.8368421052631579, 0.8526315789473684)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/runtime [15]              : (0.1596, 0.1714)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/samples_per_second [15]   : (58.337, 62.676)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/steps_per_second [15]     : (5.834, 6.268)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_accuracy [15]             : (0.8368421052631579, 0.8526315789473684)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_f1 [15]                   : (0.7848086124401913, 0.8640765550239233)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_loss [15]                 : (0.5553635954856873, 0.8132314682006836)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_precision [15]            : (0.8743490304709142, 0.9215019255455713)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_recall [15]               : (0.8368421052631579, 0.8526315789473684)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_runtime [15]              : (0.1596, 0.1714)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_samples_per_second [15]   : (58.337, 62.676)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_steps_per_second [15]     : (5.834, 6.268)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [63]                      : (0.0005206961650401354, 2.154189109802246)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     total_flos                     : 816604041600000.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/epoch [16]               : (41.0, 625.0)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/total_flos               : 816604041600000.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_loss               : 0.07405513916015626\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_runtime            : 488.8542\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_samples_per_second : 30.684\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_steps_per_second   : 1.278\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_loss                     : 0.07405513916015626\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_runtime                  : 488.8542\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_samples_per_second       : 30.684\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_steps_per_second         : 1.278\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name            : train_size_10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook_url    : https://colab.research.google.com/notebook#fileId=https%3A%2F%2Fgithub.com%2Fgonzaq94%2FNER-LLM-classification%2Fblob%2Fmain%2Ffine_tune_BERT_dataset_size.ipynb\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|dispatch_batches             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|even_batches                 : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|gradient_accumulation_kwargs : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|non_blocking                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|split_batches                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|use_seedable_sampler         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adafactor                                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta1                                      : 0.9\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta2                                      : 0.999\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_epsilon                                    : 1e-08\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|auto_find_batch_size                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|batch_eval_metrics                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|data_seed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_drop_last                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_num_workers                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_persistent_workers                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_pin_memory                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_prefetch_factor                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_backend                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_broadcast_buffers                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_bucket_cap_mb                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_find_unused_parameters                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_timeout                                     : 1800\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|debug                                           : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|deepspeed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|disable_tqdm                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dispatch_batches                                : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_eval                                         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_predict                                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_train                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_accumulation_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_delay                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_do_concat_batches                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_on_start                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_use_gather_object                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|evaluation_strategy                             : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_backend                                    : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_opt_level                                  : O1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp                                            : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|min_num_params                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_grad_ckpt                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_v2                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_min_num_params                             : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_transformer_layer_cls_to_wrap              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|full_determinism                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_accumulation_steps                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing_kwargs                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|greater_is_better                               : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|group_by_length                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|half_precision_backend                          : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_always_push                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_model_id                                    : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_private_repo                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_strategy                                    : every_save\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_token                                       : <HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ignore_data_skip                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_inputs_for_metrics                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_num_input_tokens_seen                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_tokens_per_second                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|jit_mode_eval                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_names                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_smoothing_factor                          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|learning_rate                                   : 5e-05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|length_column_name                              : length\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|load_best_model_at_end                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|local_rank                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level                                       : passive\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level_replica                               : warning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_on_each_node                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_dir                                     : ./results/runs/Nov04_16-28-28_1eb698b7797d\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_first_step                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_nan_inf_filter                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_steps                                   : 500\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_strategy                                : no\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_kwargs                             : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_type                               : linear\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_grad_norm                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_steps                                       : 625\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|metric_for_best_model                           : f1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|mp_parameters                                   : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|neftune_noise_alpha                             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|no_cuda                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|num_train_epochs                                : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim                                           : adamw_torch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_args                                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_target_modules                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|output_dir                                      : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|overwrite_output_dir                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|past_index                                      : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_eval_batch_size                      : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_train_batch_size                     : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_eval_batch_size                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_train_batch_size                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|prediction_loss_only                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_model_id                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_organization                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_token                               : <PUSH_TO_HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ray_scope                                       : last\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|remove_unused_columns                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|report_to                                       : ['tensorboard', 'wandb']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|restore_callback_states_from_checkpoint         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|resume_from_checkpoint                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|run_name                                        : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_on_each_node                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_only_model                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_safetensors                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_total_limit                                : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|seed                                            : 42\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|skip_memory_metrics                             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|split_batches                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tf32                                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_backend                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_mode                              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_empty_cache_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torchdynamo                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_metrics_debug                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_num_cores                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_cpu                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_ipex                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_legacy_prediction_loop                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_mps_device                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_ratio                                    : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_steps                                    : 62\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|weight_decay                                    : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|_name_or_path                                 : bert-base-uncased\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|add_cross_attention                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|architectures                                 : ['BertForMaskedLM']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|attention_probs_dropout_prob                  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bad_words_ids                                 : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|begin_suppress_tokens                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|chunk_size_feed_forward                       : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|classifier_dropout                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|cross_attention_hidden_size                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|decoder_start_token_id                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|diversity_penalty                             : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|do_sample                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|early_stopping                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|encoder_no_repeat_ngram_size                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|eos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|exponential_decay_length_penalty              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|finetuning_task                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_bos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_eos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|gradient_checkpointing                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_act                                    : gelu\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_dropout_prob                           : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_size                                   : 768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|0                                    : LABEL_0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|1                                    : LABEL_1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|2                                    : LABEL_2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|3                                    : LABEL_3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|4                                    : LABEL_4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|5                                    : LABEL_5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|6                                    : LABEL_6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|7                                    : LABEL_7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|8                                    : LABEL_8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|initializer_range                             : 0.02\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|intermediate_size                             : 3072\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_decoder                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_encoder_decoder                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_0                              : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_1                              : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_2                              : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_3                              : 3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_4                              : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_5                              : 5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_6                              : 6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_7                              : 7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_8                              : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|layer_norm_eps                                : 1e-12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|length_penalty                                : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_length                                    : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_position_embeddings                       : 512\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|min_length                                    : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|model_type                                    : bert\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|no_repeat_ngram_size                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_attention_heads                           : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beam_groups                               : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beams                                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_hidden_layers                             : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_return_sequences                          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_attentions                             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_hidden_states                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_scores                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pad_token_id                                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|position_embedding_type                       : absolute\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|prefix                                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|problem_type                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pruned_heads                                  : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|remove_invalid_values                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|repetition_penalty                            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict                                   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict_in_generate                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|sep_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|suppress_tokens                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|task_specific_params                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|temperature                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tf_legacy_loss                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_encoder_decoder                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_word_embeddings                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tokenizer_class                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_k                                         : 50\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_p                                         : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torch_dtype                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torchscript                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|transformers_version                          : 4.44.2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|type_vocab_size                               : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|typical_p                                     : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_bfloat16                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_cache                                     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|vocab_size                                    : 30522\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset               : 8 (1.22 GB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element       : 7 (1.22 GB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook            : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/cb3ded810da249a497f8dd9ac3a474d4\n",
            "\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in 'https://github.com/gonzaq94/NER-LLM-classification' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "comet_ml is installed but the Comet API Key is not configured. Please set the `COMET_API_KEY` environment variable to enable Comet logging. Check out the documentation for other ways of configuring it: https://www.comet.com/docs/v2/guides/experiment-management/configure-sdk/#set-the-api-key\n",
            "max_steps is given, it will override any value given in num_train_epochs\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [625/625 10:13, Epoch 312/313]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.654209</td>\n",
              "      <td>0.830420</td>\n",
              "      <td>0.753485</td>\n",
              "      <td>0.859177</td>\n",
              "      <td>0.830420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.518351</td>\n",
              "      <td>0.856643</td>\n",
              "      <td>0.860685</td>\n",
              "      <td>0.884659</td>\n",
              "      <td>0.856643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.541775</td>\n",
              "      <td>0.865385</td>\n",
              "      <td>0.866640</td>\n",
              "      <td>0.888306</td>\n",
              "      <td>0.865385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.597560</td>\n",
              "      <td>0.868881</td>\n",
              "      <td>0.872302</td>\n",
              "      <td>0.897198</td>\n",
              "      <td>0.868881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.631545</td>\n",
              "      <td>0.867133</td>\n",
              "      <td>0.873561</td>\n",
              "      <td>0.901455</td>\n",
              "      <td>0.867133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.620654</td>\n",
              "      <td>0.870629</td>\n",
              "      <td>0.867509</td>\n",
              "      <td>0.880860</td>\n",
              "      <td>0.870629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>287</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.651629</td>\n",
              "      <td>0.872378</td>\n",
              "      <td>0.873114</td>\n",
              "      <td>0.899364</td>\n",
              "      <td>0.872378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>328</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.661134</td>\n",
              "      <td>0.868881</td>\n",
              "      <td>0.870943</td>\n",
              "      <td>0.898369</td>\n",
              "      <td>0.868881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>369</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.668408</td>\n",
              "      <td>0.870629</td>\n",
              "      <td>0.871504</td>\n",
              "      <td>0.897605</td>\n",
              "      <td>0.870629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.672235</td>\n",
              "      <td>0.870629</td>\n",
              "      <td>0.871818</td>\n",
              "      <td>0.892134</td>\n",
              "      <td>0.870629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>451</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.680956</td>\n",
              "      <td>0.870629</td>\n",
              "      <td>0.870565</td>\n",
              "      <td>0.889482</td>\n",
              "      <td>0.870629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>492</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.686013</td>\n",
              "      <td>0.870629</td>\n",
              "      <td>0.870565</td>\n",
              "      <td>0.889482</td>\n",
              "      <td>0.870629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>533</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.682158</td>\n",
              "      <td>0.868881</td>\n",
              "      <td>0.870318</td>\n",
              "      <td>0.896955</td>\n",
              "      <td>0.868881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>574</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.685183</td>\n",
              "      <td>0.868881</td>\n",
              "      <td>0.868028</td>\n",
              "      <td>0.886881</td>\n",
              "      <td>0.868881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>615</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.686958</td>\n",
              "      <td>0.868881</td>\n",
              "      <td>0.868028</td>\n",
              "      <td>0.886881</td>\n",
              "      <td>0.868881</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Successfully registered 'BERT-base-NER-size30', version None in workspace 'gonzaq94'\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : train_size_30\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/cb3ded810da249a497f8dd9ac3a474d4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epoch [16]                     : (20.5, 312.5)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/accuracy [15]             : (0.8304195804195804, 0.8723776223776224)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/f1 [15]                   : (0.7534848150893997, 0.8735609513231798)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/loss [15]                 : (0.5183510780334473, 0.6869577765464783)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/precision [15]            : (0.8591770991246516, 0.9014550463903911)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/recall [15]               : (0.8304195804195804, 0.8723776223776224)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/runtime [15]              : (0.464, 0.5082)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/samples_per_second [15]   : (59.037, 64.661)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/steps_per_second [15]     : (3.936, 4.311)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_accuracy [15]             : (0.8304195804195804, 0.8723776223776224)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_f1 [15]                   : (0.7534848150893997, 0.8735609513231798)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_loss [15]                 : (0.5183510780334473, 0.6869577765464783)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_precision [15]            : (0.8591770991246516, 0.9014550463903911)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_recall [15]               : (0.8304195804195804, 0.8723776223776224)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_runtime [15]              : (0.464, 0.5082)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_samples_per_second [15]   : (59.037, 64.661)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_steps_per_second [15]     : (3.936, 4.311)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [63]                      : (0.0005064934375695884, 2.0098962783813477)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     total_flos                     : 1226081972219904.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/epoch [16]               : (20.5, 312.5)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/total_flos               : 1226081972219904.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_loss               : 0.08813071899414063\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_runtime            : 595.2006\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_samples_per_second : 25.202\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_steps_per_second   : 1.05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_loss                     : 0.08813071899414063\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_runtime                  : 595.2006\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_samples_per_second       : 25.202\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_steps_per_second         : 1.05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name            : train_size_30\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook_url    : https://colab.research.google.com/notebook#fileId=https%3A%2F%2Fgithub.com%2Fgonzaq94%2FNER-LLM-classification%2Fblob%2Fmain%2Ffine_tune_BERT_dataset_size.ipynb\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|dispatch_batches             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|even_batches                 : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|gradient_accumulation_kwargs : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|non_blocking                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|split_batches                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|use_seedable_sampler         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adafactor                                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta1                                      : 0.9\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta2                                      : 0.999\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_epsilon                                    : 1e-08\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|auto_find_batch_size                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|batch_eval_metrics                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|data_seed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_drop_last                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_num_workers                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_persistent_workers                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_pin_memory                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_prefetch_factor                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_backend                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_broadcast_buffers                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_bucket_cap_mb                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_find_unused_parameters                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_timeout                                     : 1800\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|debug                                           : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|deepspeed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|disable_tqdm                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dispatch_batches                                : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_eval                                         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_predict                                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_train                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_accumulation_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_delay                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_do_concat_batches                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_on_start                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_use_gather_object                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|evaluation_strategy                             : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_backend                                    : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_opt_level                                  : O1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp                                            : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|min_num_params                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_grad_ckpt                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_v2                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_min_num_params                             : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_transformer_layer_cls_to_wrap              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|full_determinism                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_accumulation_steps                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing_kwargs                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|greater_is_better                               : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|group_by_length                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|half_precision_backend                          : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_always_push                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_model_id                                    : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_private_repo                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_strategy                                    : every_save\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_token                                       : <HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ignore_data_skip                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_inputs_for_metrics                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_num_input_tokens_seen                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_tokens_per_second                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|jit_mode_eval                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_names                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_smoothing_factor                          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|learning_rate                                   : 5e-05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|length_column_name                              : length\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|load_best_model_at_end                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|local_rank                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level                                       : passive\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level_replica                               : warning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_on_each_node                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_dir                                     : ./results/runs/Nov04_16-37-18_1eb698b7797d\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_first_step                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_nan_inf_filter                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_steps                                   : 500\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_strategy                                : no\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_kwargs                             : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_type                               : linear\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_grad_norm                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_steps                                       : 625\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|metric_for_best_model                           : f1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|mp_parameters                                   : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|neftune_noise_alpha                             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|no_cuda                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|num_train_epochs                                : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim                                           : adamw_torch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_args                                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_target_modules                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|output_dir                                      : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|overwrite_output_dir                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|past_index                                      : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_eval_batch_size                      : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_train_batch_size                     : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_eval_batch_size                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_train_batch_size                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|prediction_loss_only                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_model_id                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_organization                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_token                               : <PUSH_TO_HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ray_scope                                       : last\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|remove_unused_columns                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|report_to                                       : ['tensorboard', 'wandb']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|restore_callback_states_from_checkpoint         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|resume_from_checkpoint                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|run_name                                        : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_on_each_node                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_only_model                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_safetensors                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_total_limit                                : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|seed                                            : 42\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|skip_memory_metrics                             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|split_batches                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tf32                                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_backend                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_mode                              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_empty_cache_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torchdynamo                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_metrics_debug                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_num_cores                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_cpu                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_ipex                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_legacy_prediction_loop                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_mps_device                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_ratio                                    : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_steps                                    : 62\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|weight_decay                                    : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|_name_or_path                                 : bert-base-uncased\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|add_cross_attention                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|architectures                                 : ['BertForMaskedLM']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|attention_probs_dropout_prob                  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bad_words_ids                                 : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|begin_suppress_tokens                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|chunk_size_feed_forward                       : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|classifier_dropout                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|cross_attention_hidden_size                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|decoder_start_token_id                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|diversity_penalty                             : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|do_sample                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|early_stopping                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|encoder_no_repeat_ngram_size                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|eos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|exponential_decay_length_penalty              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|finetuning_task                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_bos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_eos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|gradient_checkpointing                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_act                                    : gelu\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_dropout_prob                           : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_size                                   : 768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|0                                    : LABEL_0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|1                                    : LABEL_1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|2                                    : LABEL_2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|3                                    : LABEL_3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|4                                    : LABEL_4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|5                                    : LABEL_5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|6                                    : LABEL_6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|7                                    : LABEL_7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|8                                    : LABEL_8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|initializer_range                             : 0.02\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|intermediate_size                             : 3072\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_decoder                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_encoder_decoder                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_0                              : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_1                              : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_2                              : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_3                              : 3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_4                              : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_5                              : 5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_6                              : 6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_7                              : 7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_8                              : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|layer_norm_eps                                : 1e-12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|length_penalty                                : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_length                                    : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_position_embeddings                       : 512\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|min_length                                    : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|model_type                                    : bert\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|no_repeat_ngram_size                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_attention_heads                           : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beam_groups                               : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beams                                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_hidden_layers                             : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_return_sequences                          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_attentions                             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_hidden_states                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_scores                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pad_token_id                                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|position_embedding_type                       : absolute\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|prefix                                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|problem_type                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pruned_heads                                  : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|remove_invalid_values                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|repetition_penalty                            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict                                   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict_in_generate                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|sep_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|suppress_tokens                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|task_specific_params                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|temperature                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tf_legacy_loss                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_encoder_decoder                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_word_embeddings                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tokenizer_class                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_k                                         : 50\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_p                                         : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torch_dtype                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torchscript                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|transformers_version                          : 4.44.2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|type_vocab_size                               : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|typical_p                                     : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_bfloat16                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_cache                                     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|vocab_size                                    : 30522\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset               : 8 (1.22 GB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element       : 7 (1.22 GB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook            : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/2068d2ce046d45c3acb92798b0778ff7\n",
            "\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in 'https://github.com/gonzaq94/NER-LLM-classification' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "comet_ml is installed but the Comet API Key is not configured. Please set the `COMET_API_KEY` environment variable to enable Comet logging. Check out the documentation for other ways of configuring it: https://www.comet.com/docs/v2/guides/experiment-management/configure-sdk/#set-the-api-key\n",
            "max_steps is given, it will override any value given in num_train_epochs\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [625/625 13:13, Epoch 125/125]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.653940</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>0.736364</td>\n",
              "      <td>0.851240</td>\n",
              "      <td>0.818182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.333288</td>\n",
              "      <td>0.907532</td>\n",
              "      <td>0.899875</td>\n",
              "      <td>0.905669</td>\n",
              "      <td>0.907532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.357480</td>\n",
              "      <td>0.917403</td>\n",
              "      <td>0.910620</td>\n",
              "      <td>0.915535</td>\n",
              "      <td>0.917403</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.390904</td>\n",
              "      <td>0.914286</td>\n",
              "      <td>0.909568</td>\n",
              "      <td>0.918128</td>\n",
              "      <td>0.914286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.380511</td>\n",
              "      <td>0.921558</td>\n",
              "      <td>0.917415</td>\n",
              "      <td>0.920279</td>\n",
              "      <td>0.921558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.429177</td>\n",
              "      <td>0.915325</td>\n",
              "      <td>0.909645</td>\n",
              "      <td>0.913171</td>\n",
              "      <td>0.915325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>287</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.415192</td>\n",
              "      <td>0.922597</td>\n",
              "      <td>0.919093</td>\n",
              "      <td>0.919369</td>\n",
              "      <td>0.922597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>328</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.419551</td>\n",
              "      <td>0.926753</td>\n",
              "      <td>0.922787</td>\n",
              "      <td>0.922583</td>\n",
              "      <td>0.926753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>369</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.421795</td>\n",
              "      <td>0.927273</td>\n",
              "      <td>0.922874</td>\n",
              "      <td>0.922752</td>\n",
              "      <td>0.927273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.426123</td>\n",
              "      <td>0.926234</td>\n",
              "      <td>0.922254</td>\n",
              "      <td>0.922220</td>\n",
              "      <td>0.926234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>451</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.435328</td>\n",
              "      <td>0.926234</td>\n",
              "      <td>0.922042</td>\n",
              "      <td>0.922062</td>\n",
              "      <td>0.926234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>492</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.440219</td>\n",
              "      <td>0.926234</td>\n",
              "      <td>0.921715</td>\n",
              "      <td>0.921260</td>\n",
              "      <td>0.926234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>533</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.437218</td>\n",
              "      <td>0.927273</td>\n",
              "      <td>0.922889</td>\n",
              "      <td>0.922438</td>\n",
              "      <td>0.927273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>574</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.439700</td>\n",
              "      <td>0.926234</td>\n",
              "      <td>0.921683</td>\n",
              "      <td>0.921200</td>\n",
              "      <td>0.926234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>615</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.432378</td>\n",
              "      <td>0.926234</td>\n",
              "      <td>0.922435</td>\n",
              "      <td>0.922134</td>\n",
              "      <td>0.926234</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Successfully registered 'BERT-base-NER-size100', version None in workspace 'gonzaq94'\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : train_size_100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/2068d2ce046d45c3acb92798b0778ff7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epoch [16]                     : (8.2, 125.0)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/accuracy [15]             : (0.8181818181818182, 0.9272727272727272)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/f1 [15]                   : (0.7363636363636363, 0.9228894787890212)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/loss [15]                 : (0.3332875072956085, 0.6539396643638611)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/precision [15]            : (0.8512396694214877, 0.9227516876105645)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/recall [15]               : (0.8181818181818182, 0.9272727272727272)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/runtime [15]              : (1.5778, 1.8525)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/samples_per_second [15]   : (53.982, 63.377)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/steps_per_second [15]     : (2.699, 3.169)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_accuracy [15]             : (0.8181818181818182, 0.9272727272727272)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_f1 [15]                   : (0.7363636363636363, 0.9228894787890212)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_loss [15]                 : (0.3332875072956085, 0.6539396643638611)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_precision [15]            : (0.8512396694214877, 0.9227516876105645)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_recall [15]               : (0.8181818181818182, 0.9272727272727272)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_runtime [15]              : (1.5778, 1.8525)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_samples_per_second [15]   : (53.982, 63.377)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_steps_per_second [15]     : (2.699, 3.169)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [63]                      : (0.00040854013059288263, 2.2343270778656006)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     total_flos                     : 1633208083200000.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/epoch [16]               : (8.2, 125.0)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/total_flos               : 1633208083200000.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_loss               : 0.10646317138671875\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_runtime            : 781.7397\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_samples_per_second : 19.188\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_steps_per_second   : 0.799\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_loss                     : 0.10646317138671875\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_runtime                  : 781.7397\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_samples_per_second       : 19.188\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_steps_per_second         : 0.799\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name            : train_size_100\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook_url    : https://colab.research.google.com/notebook#fileId=https%3A%2F%2Fgithub.com%2Fgonzaq94%2FNER-LLM-classification%2Fblob%2Fmain%2Ffine_tune_BERT_dataset_size.ipynb\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|dispatch_batches             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|even_batches                 : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|gradient_accumulation_kwargs : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|non_blocking                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|split_batches                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|use_seedable_sampler         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adafactor                                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta1                                      : 0.9\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta2                                      : 0.999\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_epsilon                                    : 1e-08\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|auto_find_batch_size                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|batch_eval_metrics                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|data_seed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_drop_last                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_num_workers                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_persistent_workers                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_pin_memory                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_prefetch_factor                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_backend                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_broadcast_buffers                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_bucket_cap_mb                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_find_unused_parameters                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_timeout                                     : 1800\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|debug                                           : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|deepspeed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|disable_tqdm                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dispatch_batches                                : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_eval                                         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_predict                                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_train                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_accumulation_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_delay                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_do_concat_batches                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_on_start                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_use_gather_object                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|evaluation_strategy                             : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_backend                                    : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_opt_level                                  : O1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp                                            : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|min_num_params                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_grad_ckpt                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_v2                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_min_num_params                             : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_transformer_layer_cls_to_wrap              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|full_determinism                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_accumulation_steps                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing_kwargs                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|greater_is_better                               : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|group_by_length                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|half_precision_backend                          : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_always_push                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_model_id                                    : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_private_repo                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_strategy                                    : every_save\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_token                                       : <HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ignore_data_skip                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_inputs_for_metrics                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_num_input_tokens_seen                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_tokens_per_second                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|jit_mode_eval                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_names                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_smoothing_factor                          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|learning_rate                                   : 5e-05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|length_column_name                              : length\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|load_best_model_at_end                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|local_rank                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level                                       : passive\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level_replica                               : warning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_on_each_node                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_dir                                     : ./results/runs/Nov04_16-48-12_1eb698b7797d\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_first_step                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_nan_inf_filter                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_steps                                   : 500\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_strategy                                : no\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_kwargs                             : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_type                               : linear\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_grad_norm                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_steps                                       : 625\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|metric_for_best_model                           : f1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|mp_parameters                                   : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|neftune_noise_alpha                             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|no_cuda                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|num_train_epochs                                : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim                                           : adamw_torch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_args                                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_target_modules                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|output_dir                                      : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|overwrite_output_dir                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|past_index                                      : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_eval_batch_size                      : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_train_batch_size                     : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_eval_batch_size                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_train_batch_size                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|prediction_loss_only                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_model_id                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_organization                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_token                               : <PUSH_TO_HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ray_scope                                       : last\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|remove_unused_columns                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|report_to                                       : ['tensorboard', 'wandb']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|restore_callback_states_from_checkpoint         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|resume_from_checkpoint                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|run_name                                        : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_on_each_node                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_only_model                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_safetensors                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_total_limit                                : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|seed                                            : 42\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|skip_memory_metrics                             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|split_batches                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tf32                                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_backend                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_mode                              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_empty_cache_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torchdynamo                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_metrics_debug                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_num_cores                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_cpu                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_ipex                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_legacy_prediction_loop                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_mps_device                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_ratio                                    : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_steps                                    : 62\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|weight_decay                                    : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|_name_or_path                                 : bert-base-uncased\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|add_cross_attention                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|architectures                                 : ['BertForMaskedLM']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|attention_probs_dropout_prob                  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bad_words_ids                                 : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|begin_suppress_tokens                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|chunk_size_feed_forward                       : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|classifier_dropout                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|cross_attention_hidden_size                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|decoder_start_token_id                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|diversity_penalty                             : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|do_sample                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|early_stopping                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|encoder_no_repeat_ngram_size                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|eos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|exponential_decay_length_penalty              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|finetuning_task                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_bos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_eos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|gradient_checkpointing                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_act                                    : gelu\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_dropout_prob                           : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_size                                   : 768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|0                                    : LABEL_0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|1                                    : LABEL_1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|2                                    : LABEL_2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|3                                    : LABEL_3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|4                                    : LABEL_4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|5                                    : LABEL_5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|6                                    : LABEL_6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|7                                    : LABEL_7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|8                                    : LABEL_8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|initializer_range                             : 0.02\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|intermediate_size                             : 3072\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_decoder                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_encoder_decoder                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_0                              : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_1                              : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_2                              : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_3                              : 3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_4                              : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_5                              : 5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_6                              : 6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_7                              : 7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_8                              : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|layer_norm_eps                                : 1e-12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|length_penalty                                : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_length                                    : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_position_embeddings                       : 512\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|min_length                                    : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|model_type                                    : bert\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|no_repeat_ngram_size                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_attention_heads                           : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beam_groups                               : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beams                                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_hidden_layers                             : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_return_sequences                          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_attentions                             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_hidden_states                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_scores                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pad_token_id                                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|position_embedding_type                       : absolute\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|prefix                                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|problem_type                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pruned_heads                                  : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|remove_invalid_values                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|repetition_penalty                            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict                                   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict_in_generate                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|sep_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|suppress_tokens                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|task_specific_params                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|temperature                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tf_legacy_loss                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_encoder_decoder                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_word_embeddings                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tokenizer_class                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_k                                         : 50\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_p                                         : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torch_dtype                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torchscript                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|transformers_version                          : 4.44.2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|type_vocab_size                               : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|typical_p                                     : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_bfloat16                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_cache                                     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|vocab_size                                    : 30522\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset               : 8 (1.22 GB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element       : 7 (1.22 GB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook            : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in 'https://github.com/gonzaq94/NER-LLM-classification' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/30a5ef0ccbce48d9ae483d7f424e9a57\n",
            "\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "comet_ml is installed but the Comet API Key is not configured. Please set the `COMET_API_KEY` environment variable to enable Comet logging. Check out the documentation for other ways of configuring it: https://www.comet.com/docs/v2/guides/experiment-management/configure-sdk/#set-the-api-key\n",
            "max_steps is given, it will override any value given in num_train_epochs\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='618' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [618/625 14:20 < 00:09, 0.72 it/s, Epoch 47.46/49]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.714359</td>\n",
              "      <td>0.808594</td>\n",
              "      <td>0.723019</td>\n",
              "      <td>0.845230</td>\n",
              "      <td>0.808594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.304875</td>\n",
              "      <td>0.909986</td>\n",
              "      <td>0.908914</td>\n",
              "      <td>0.923218</td>\n",
              "      <td>0.909986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.285832</td>\n",
              "      <td>0.927140</td>\n",
              "      <td>0.926954</td>\n",
              "      <td>0.932866</td>\n",
              "      <td>0.927140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.278881</td>\n",
              "      <td>0.940897</td>\n",
              "      <td>0.936767</td>\n",
              "      <td>0.936543</td>\n",
              "      <td>0.940897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.303358</td>\n",
              "      <td>0.936141</td>\n",
              "      <td>0.936347</td>\n",
              "      <td>0.938754</td>\n",
              "      <td>0.936141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.321303</td>\n",
              "      <td>0.933933</td>\n",
              "      <td>0.936201</td>\n",
              "      <td>0.941303</td>\n",
              "      <td>0.933933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>287</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.303546</td>\n",
              "      <td>0.943784</td>\n",
              "      <td>0.944184</td>\n",
              "      <td>0.946483</td>\n",
              "      <td>0.943784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>328</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.334652</td>\n",
              "      <td>0.941406</td>\n",
              "      <td>0.940606</td>\n",
              "      <td>0.941298</td>\n",
              "      <td>0.941406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>369</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.338574</td>\n",
              "      <td>0.939029</td>\n",
              "      <td>0.939088</td>\n",
              "      <td>0.941055</td>\n",
              "      <td>0.939029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.353936</td>\n",
              "      <td>0.940048</td>\n",
              "      <td>0.937948</td>\n",
              "      <td>0.940465</td>\n",
              "      <td>0.940048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>451</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.335835</td>\n",
              "      <td>0.940217</td>\n",
              "      <td>0.940633</td>\n",
              "      <td>0.943204</td>\n",
              "      <td>0.940217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>492</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.356338</td>\n",
              "      <td>0.938519</td>\n",
              "      <td>0.939239</td>\n",
              "      <td>0.943592</td>\n",
              "      <td>0.938519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>533</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.352745</td>\n",
              "      <td>0.940387</td>\n",
              "      <td>0.940504</td>\n",
              "      <td>0.943697</td>\n",
              "      <td>0.940387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>574</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.353621</td>\n",
              "      <td>0.941067</td>\n",
              "      <td>0.940656</td>\n",
              "      <td>0.943519</td>\n",
              "      <td>0.941067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>615</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.351606</td>\n",
              "      <td>0.941236</td>\n",
              "      <td>0.940618</td>\n",
              "      <td>0.942866</td>\n",
              "      <td>0.941236</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [625/625 14:50, Epoch 48/49]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.714359</td>\n",
              "      <td>0.808594</td>\n",
              "      <td>0.723019</td>\n",
              "      <td>0.845230</td>\n",
              "      <td>0.808594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.304875</td>\n",
              "      <td>0.909986</td>\n",
              "      <td>0.908914</td>\n",
              "      <td>0.923218</td>\n",
              "      <td>0.909986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.285832</td>\n",
              "      <td>0.927140</td>\n",
              "      <td>0.926954</td>\n",
              "      <td>0.932866</td>\n",
              "      <td>0.927140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.278881</td>\n",
              "      <td>0.940897</td>\n",
              "      <td>0.936767</td>\n",
              "      <td>0.936543</td>\n",
              "      <td>0.940897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.303358</td>\n",
              "      <td>0.936141</td>\n",
              "      <td>0.936347</td>\n",
              "      <td>0.938754</td>\n",
              "      <td>0.936141</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.321303</td>\n",
              "      <td>0.933933</td>\n",
              "      <td>0.936201</td>\n",
              "      <td>0.941303</td>\n",
              "      <td>0.933933</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>287</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.303546</td>\n",
              "      <td>0.943784</td>\n",
              "      <td>0.944184</td>\n",
              "      <td>0.946483</td>\n",
              "      <td>0.943784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>328</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.334652</td>\n",
              "      <td>0.941406</td>\n",
              "      <td>0.940606</td>\n",
              "      <td>0.941298</td>\n",
              "      <td>0.941406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>369</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.338574</td>\n",
              "      <td>0.939029</td>\n",
              "      <td>0.939088</td>\n",
              "      <td>0.941055</td>\n",
              "      <td>0.939029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.353936</td>\n",
              "      <td>0.940048</td>\n",
              "      <td>0.937948</td>\n",
              "      <td>0.940465</td>\n",
              "      <td>0.940048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>451</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.335835</td>\n",
              "      <td>0.940217</td>\n",
              "      <td>0.940633</td>\n",
              "      <td>0.943204</td>\n",
              "      <td>0.940217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>492</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.356338</td>\n",
              "      <td>0.938519</td>\n",
              "      <td>0.939239</td>\n",
              "      <td>0.943592</td>\n",
              "      <td>0.938519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>533</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.352745</td>\n",
              "      <td>0.940387</td>\n",
              "      <td>0.940504</td>\n",
              "      <td>0.943697</td>\n",
              "      <td>0.940387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>574</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.353621</td>\n",
              "      <td>0.941067</td>\n",
              "      <td>0.940656</td>\n",
              "      <td>0.943519</td>\n",
              "      <td>0.941067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>615</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.351606</td>\n",
              "      <td>0.941236</td>\n",
              "      <td>0.940618</td>\n",
              "      <td>0.942866</td>\n",
              "      <td>0.941236</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Successfully registered 'BERT-base-NER-size300', version None in workspace 'gonzaq94'\n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m Couldn't retrieve and log Google Colab notebook content, reason: 'NoneType' object is not subscriptable\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     name                  : train_size_300\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/30a5ef0ccbce48d9ae483d7f424e9a57\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     epoch [16]                     : (3.1538461538461537, 48.07692307692308)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/accuracy [15]             : (0.80859375, 0.9437839673913043)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/f1 [15]                   : (0.7230190334773219, 0.9441842107488716)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/loss [15]                 : (0.27888116240501404, 0.7143592834472656)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/precision [15]            : (0.8452301025390625, 0.9464831161620273)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/recall [15]               : (0.80859375, 0.9437839673913043)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/runtime [15]              : (4.8039, 5.2743)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/samples_per_second [15]   : (56.88, 62.449)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval/steps_per_second [15]     : (2.465, 2.706)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_accuracy [15]             : (0.80859375, 0.9437839673913043)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_f1 [15]                   : (0.7230190334773219, 0.9441842107488716)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_loss [15]                 : (0.27888116240501404, 0.7143592834472656)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_precision [15]            : (0.8452301025390625, 0.9464831161620273)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_recall [15]               : (0.80859375, 0.9437839673913043)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_runtime [15]              : (4.8039, 5.2743)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_samples_per_second [15]   : (56.88, 62.449)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     eval_steps_per_second [15]     : (2.465, 2.706)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [63]                      : (0.0007852756534703076, 2.0172903537750244)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     total_flos                     : 1884591471366144.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/epoch [16]               : (3.1538461538461537, 48.07692307692308)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/total_flos               : 1884591471366144.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_loss               : 0.122151318359375\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_runtime            : 880.9344\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_samples_per_second : 17.027\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train/train_steps_per_second   : 0.709\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_loss                     : 0.122151318359375\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_runtime                  : 880.9344\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_samples_per_second       : 17.027\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_steps_per_second         : 0.709\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Name            : train_size_300\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     hasNestedParams : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook_url    : https://colab.research.google.com/notebook#fileId=https%3A%2F%2Fgithub.com%2Fgonzaq94%2FNER-LLM-classification%2Fblob%2Fmain%2Ffine_tune_BERT_dataset_size.ipynb\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|dispatch_batches             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|even_batches                 : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|gradient_accumulation_kwargs : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|non_blocking                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|split_batches                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|accelerator_config|use_seedable_sampler         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adafactor                                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta1                                      : 0.9\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_beta2                                      : 0.999\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|adam_epsilon                                    : 1e-08\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|auto_find_batch_size                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|batch_eval_metrics                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|bf16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|data_seed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_drop_last                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_num_workers                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_persistent_workers                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_pin_memory                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dataloader_prefetch_factor                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_backend                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_broadcast_buffers                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_bucket_cap_mb                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_find_unused_parameters                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ddp_timeout                                     : 1800\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|debug                                           : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|deepspeed                                       : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|disable_tqdm                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|dispatch_batches                                : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_eval                                         : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_predict                                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|do_train                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_accumulation_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_delay                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_do_concat_batches                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_on_start                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|eval_use_gather_object                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|evaluation_strategy                             : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16                                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_backend                                    : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_full_eval                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fp16_opt_level                                  : O1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp                                            : []\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|min_num_params                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_grad_ckpt                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_config|xla_fsdp_v2                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_min_num_params                             : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|fsdp_transformer_layer_cls_to_wrap              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|full_determinism                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_accumulation_steps                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|gradient_checkpointing_kwargs                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|greater_is_better                               : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|group_by_length                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|half_precision_backend                          : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_always_push                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_model_id                                    : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_private_repo                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_strategy                                    : every_save\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|hub_token                                       : <HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ignore_data_skip                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_inputs_for_metrics                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_num_input_tokens_seen                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|include_tokens_per_second                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|jit_mode_eval                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_names                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|label_smoothing_factor                          : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|learning_rate                                   : 5e-05\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|length_column_name                              : length\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|load_best_model_at_end                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|local_rank                                      : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level                                       : passive\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_level_replica                               : warning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|log_on_each_node                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_dir                                     : ./results/runs/Nov04_17-02-19_1eb698b7797d\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_first_step                              : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_nan_inf_filter                          : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_steps                                   : 500\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|logging_strategy                                : no\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_kwargs                             : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|lr_scheduler_type                               : linear\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_grad_norm                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|max_steps                                       : 625\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|metric_for_best_model                           : f1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|mp_parameters                                   : \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|neftune_noise_alpha                             : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|no_cuda                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|num_train_epochs                                : 3.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim                                           : adamw_torch\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_args                                      : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|optim_target_modules                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|output_dir                                      : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|overwrite_output_dir                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|past_index                                      : -1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_eval_batch_size                      : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_device_train_batch_size                     : 24\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_eval_batch_size                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|per_gpu_train_batch_size                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|prediction_loss_only                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_model_id                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_organization                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|push_to_hub_token                               : <PUSH_TO_HUB_TOKEN>\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|ray_scope                                       : last\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|remove_unused_columns                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|report_to                                       : ['tensorboard', 'wandb']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|restore_callback_states_from_checkpoint         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|resume_from_checkpoint                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|run_name                                        : ./results\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_on_each_node                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_only_model                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_safetensors                                : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_steps                                      : 41\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_strategy                                   : steps\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|save_total_limit                                : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|seed                                            : 42\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|skip_memory_metrics                             : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|split_batches                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tf32                                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_backend                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_compile_mode                              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torch_empty_cache_steps                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|torchdynamo                                     : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_metrics_debug                               : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|tpu_num_cores                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_cpu                                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_ipex                                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_legacy_prediction_loop                      : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|use_mps_device                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_ratio                                    : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|warmup_steps                                    : 62\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     args|weight_decay                                    : 0.01\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|_name_or_path                                 : bert-base-uncased\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|add_cross_attention                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|architectures                                 : ['BertForMaskedLM']\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|attention_probs_dropout_prob                  : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bad_words_ids                                 : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|begin_suppress_tokens                         : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|bos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|chunk_size_feed_forward                       : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|classifier_dropout                            : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|cross_attention_hidden_size                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|decoder_start_token_id                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|diversity_penalty                             : 0.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|do_sample                                     : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|early_stopping                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|encoder_no_repeat_ngram_size                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|eos_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|exponential_decay_length_penalty              : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|finetuning_task                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_bos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|forced_eos_token_id                           : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|gradient_checkpointing                        : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_act                                    : gelu\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_dropout_prob                           : 0.1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|hidden_size                                   : 768\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|0                                    : LABEL_0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|1                                    : LABEL_1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|2                                    : LABEL_2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|3                                    : LABEL_3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|4                                    : LABEL_4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|5                                    : LABEL_5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|6                                    : LABEL_6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|7                                    : LABEL_7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|id2label|8                                    : LABEL_8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|initializer_range                             : 0.02\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|intermediate_size                             : 3072\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_decoder                                    : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|is_encoder_decoder                            : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_0                              : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_1                              : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_2                              : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_3                              : 3\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_4                              : 4\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_5                              : 5\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_6                              : 6\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_7                              : 7\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|label2id|LABEL_8                              : 8\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|layer_norm_eps                                : 1e-12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|length_penalty                                : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_length                                    : 20\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|max_position_embeddings                       : 512\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|min_length                                    : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|model_type                                    : bert\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|no_repeat_ngram_size                          : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_attention_heads                           : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beam_groups                               : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_beams                                     : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_hidden_layers                             : 12\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|num_return_sequences                          : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_attentions                             : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_hidden_states                          : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|output_scores                                 : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pad_token_id                                  : 0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|position_embedding_type                       : absolute\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|prefix                                        : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|problem_type                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|pruned_heads                                  : {}\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|remove_invalid_values                         : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|repetition_penalty                            : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict                                   : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|return_dict_in_generate                       : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|sep_token_id                                  : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|suppress_tokens                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|task_specific_params                          : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|temperature                                   : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tf_legacy_loss                                : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_encoder_decoder                           : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tie_word_embeddings                           : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|tokenizer_class                               : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_k                                         : 50\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|top_p                                         : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torch_dtype                                   : None\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|torchscript                                   : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|transformers_version                          : 4.44.2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|type_vocab_size                               : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|typical_p                                     : 1.0\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_bfloat16                                  : False\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|use_cache                                     : True\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     config|vocab_size                                    : 30522\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     asset               : 8 (1.22 GB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element       : 7 (1.22 GB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/gonzaq94/ner-classif-bert-diff-train-size-constant-its/97382c6c78a0494aa4dcba635e8ed406\n",
            "\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in 'https://github.com/gonzaq94/NER-LLM-classification' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "Some weights of BertForTokenClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n",
            "comet_ml is installed but the Comet API Key is not configured. Please set the `COMET_API_KEY` environment variable to enable Comet logging. Check out the documentation for other ways of configuring it: https://www.comet.com/docs/v2/guides/experiment-management/configure-sdk/#set-the-api-key\n",
            "max_steps is given, it will override any value given in num_train_epochs\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m An experiment with the same configuration options is already running and will be reused.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='625' max='625' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [625/625 17:40, Epoch 14/15]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.676589</td>\n",
              "      <td>0.813648</td>\n",
              "      <td>0.749654</td>\n",
              "      <td>0.829320</td>\n",
              "      <td>0.813648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>82</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.256209</td>\n",
              "      <td>0.926874</td>\n",
              "      <td>0.918923</td>\n",
              "      <td>0.924263</td>\n",
              "      <td>0.926874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>123</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.176264</td>\n",
              "      <td>0.948247</td>\n",
              "      <td>0.945382</td>\n",
              "      <td>0.946129</td>\n",
              "      <td>0.948247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>164</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.174376</td>\n",
              "      <td>0.952013</td>\n",
              "      <td>0.952220</td>\n",
              "      <td>0.953711</td>\n",
              "      <td>0.952013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>205</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.155302</td>\n",
              "      <td>0.960816</td>\n",
              "      <td>0.960019</td>\n",
              "      <td>0.959978</td>\n",
              "      <td>0.960816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>246</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.168555</td>\n",
              "      <td>0.959951</td>\n",
              "      <td>0.960106</td>\n",
              "      <td>0.961336</td>\n",
              "      <td>0.959951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>287</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.177123</td>\n",
              "      <td>0.962088</td>\n",
              "      <td>0.961961</td>\n",
              "      <td>0.963373</td>\n",
              "      <td>0.962088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>328</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.169243</td>\n",
              "      <td>0.963462</td>\n",
              "      <td>0.963028</td>\n",
              "      <td>0.963812</td>\n",
              "      <td>0.963462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>369</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.170951</td>\n",
              "      <td>0.964022</td>\n",
              "      <td>0.963394</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>0.964022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>410</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.160073</td>\n",
              "      <td>0.964328</td>\n",
              "      <td>0.964647</td>\n",
              "      <td>0.965523</td>\n",
              "      <td>0.964328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>451</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.172111</td>\n",
              "      <td>0.963819</td>\n",
              "      <td>0.964058</td>\n",
              "      <td>0.964944</td>\n",
              "      <td>0.963819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>492</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.178545</td>\n",
              "      <td>0.963208</td>\n",
              "      <td>0.963824</td>\n",
              "      <td>0.964985</td>\n",
              "      <td>0.963208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>533</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.172697</td>\n",
              "      <td>0.965142</td>\n",
              "      <td>0.965085</td>\n",
              "      <td>0.965508</td>\n",
              "      <td>0.965142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>574</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.178123</td>\n",
              "      <td>0.964277</td>\n",
              "      <td>0.964270</td>\n",
              "      <td>0.965198</td>\n",
              "      <td>0.964277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>615</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.176790</td>\n",
              "      <td>0.964887</td>\n",
              "      <td>0.964698</td>\n",
              "      <td>0.965230</td>\n",
              "      <td>0.964887</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Deploy and evaluate the best model"
      ],
      "metadata": {
        "id": "79UTB1GIuJ0v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "-FaKmrX-uL8N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from comet_ml import API\n",
        "\n",
        "api = API(api_key=os.getenv(\"COMET_API_KEY\"))\n",
        "\n",
        "# model name\n",
        "model_name = \"BERT-base-NER-size1000\"\n",
        "\n",
        "#get the Model object\n",
        "model = api.get_model(workspace=os.getenv(\"COMET_WORKSPACE\"), model_name=model_name)\n",
        "\n",
        "# Download a Registry Model:\n",
        "model.download(\"1.0.0\", \"./deploy\", expand=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxcovKjxs150",
        "outputId": "dc01e244-1be9-48b3-9949-d8b321f43a29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Remote Model 'gonzaq94/BERT-base-NER-size1000:1.0.0' download has been started asynchronously.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still downloading 7 file(s), remaining 1.22 GB/1.22 GB\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still downloading 2 file(s), remaining 686.43 MB/1.22 GB, Throughput 37.28 MB/s, ETA ~19s\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Still downloading 1 file(s), remaining 257.99 MB/1.22 GB, Throughput 28.53 MB/s, ETA ~10s\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Remote Model 'gonzaq94/BERT-base-NER-size1000:1.0.0' has been successfully downloaded.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Downloaded asset files is in './deploy' folder.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def plot_confusion_matrix(cm, class_names):\n",
        "\n",
        "    # Plot confusion matrix\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues', xticklabels=class_names, yticklabels=class_names)\n",
        "    plt.title('Normalized confusion Matrix')\n",
        "    plt.xlabel('Predicted Label')\n",
        "    plt.ylabel('True Label')\n",
        "    plt.show()\n",
        "\n",
        "def compute_metrics_eval(p):\n",
        "    predictions, labels = p\n",
        "    predictions = predictions.argmax(-1)  # Get the predicted class indices\n",
        "    true_predictions = [p for (p, l) in zip(predictions.flatten(), labels.flatten()) if l != -100]\n",
        "    true_labels = [l for l in labels.flatten() if l != -100]\n",
        "\n",
        "    precision, recall, f1, _ = precision_recall_fscore_support(true_labels, true_predictions, average='weighted', zero_division=1 )\n",
        "    acc = accuracy_score(true_labels, true_predictions)\n",
        "\n",
        "    # Calculate confusion matrix\n",
        "    conf_matrix = confusion_matrix(true_labels, true_predictions, normalize='true')\n",
        "\n",
        "    return {\n",
        "        'accuracy': acc,\n",
        "        'f1': f1,\n",
        "        'precision': precision,\n",
        "        'recall': recall,\n",
        "        'confusion_matrix': conf_matrix\n",
        "    }\n",
        "\n",
        "\n",
        "eval_args = TrainingArguments(\n",
        "  output_dir='./results',\n",
        "  max_steps=niterations,\n",
        "  evaluation_strategy=\"epoch\",\n",
        "  save_strategy=\"epoch\",\n",
        "  logging_strategy=\"no\",\n",
        "  per_device_train_batch_size=24,\n",
        "  per_device_eval_batch_size=16,\n",
        "  warmup_steps=100,\n",
        "  weight_decay=0.01,\n",
        "  logging_dir=None,\n",
        "  dataloader_drop_last=False,\n",
        "  load_best_model_at_end=True,\n",
        "  report_to=[]\n",
        "  )\n",
        "\n",
        "class Evaluator:\n",
        "\n",
        "    def __init__(self, model, tokenizer, compute_metrics):\n",
        "        self.model = model\n",
        "        self.tokenizer = tokenizer\n",
        "        self.compute_metrics = compute_metrics\n",
        "\n",
        "    def evaluate(self, eval_dataset, batch_size=16):\n",
        "        trainer = Trainer(\n",
        "            model=self.model,\n",
        "            args=eval_args,\n",
        "            compute_metrics=self.compute_metrics,\n",
        "            eval_dataset=eval_dataset,\n",
        "        )\n",
        "        return trainer.evaluate()\n",
        "\n",
        "test_custom_dataset = CustomDataset(\n",
        "      texts=dataset_test['tokens'],\n",
        "      labels=dataset_test['ner_tags'],\n",
        "      tokenizer=tokenizer,\n",
        "      max_length=256\n",
        "  )"
      ],
      "metadata": {
        "id": "D0UR5MqHuqQc",
        "outputId": "0ec0e175-fe9c-4070-b44d-6a5598ffe2b3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PtG925e-SGWM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForTokenClassification.from_pretrained(\"./deploy/best_model\", num_labels=9)\n",
        "evaluator = Evaluator(model, tokenizer, compute_metrics_eval)\n",
        "eval_results = evaluator.evaluate(test_custom_dataset)\n"
      ],
      "metadata": {
        "id": "TWj4jeNmvoDC",
        "outputId": "672b9708-9fa4-425d-c3e3-b55761112fa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='216' max='216' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [216/216 00:51]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for key, value in eval_results.items():\n",
        "  if key != 'eval_confusion_matrix':\n",
        "    print(f\"{key}: {value}\")"
      ],
      "metadata": {
        "id": "zIpBXoyrvofS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0918b8e2-6fee-4db1-fcac-063020ccc04f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "eval_loss: 0.24071307480335236\n",
            "eval_model_preparation_time: 0.006\n",
            "eval_accuracy: 0.9601719494510048\n",
            "eval_f1: 0.9610030059028781\n",
            "eval_precision: 0.962094836318926\n",
            "eval_recall: 0.9601719494510048\n",
            "eval_runtime: 52.6826\n",
            "eval_samples_per_second: 65.543\n",
            "eval_steps_per_second: 4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = {'O': 0, 'B-PER': 1, 'I-PER': 2, 'B-ORG': 3, 'I-ORG': 4, 'B-LOC': 5, 'I-LOC': 6, 'B-MISC': 7, 'I-MISC': 8}\n",
        "plot_confusion_matrix(eval_results['eval_confusion_matrix'], class_names)"
      ],
      "metadata": {
        "id": "pqeBUNR1xK_K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "2912ddea-0a33-453b-b573-598b906ab749"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}