FineTuningMistral7BUsing4BitQuantizationWithLudwig.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ibndias/poor-mans-llm/blob/main/FineTuningMistral7BUsing4BitQuantizationWithLudwig.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Install Ludwig and Ludwig's LLM related dependencies.**"
      ],
      "metadata": {
        "id": "wXMVoEFkPXJJ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QZBPosPfcWbA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall -y tensorflow --quiet\n",
        "!pip install --upgrade git+https://github.com/huggingface/transformers\n",
        "!pip install --upgrade git+https://github.com/huggingface/peft.git\n",
        "!pip install git+https://github.com/ludwig-ai/ludwig.git@master --quiet"
      ],
      "metadata": {
        "id": "yfYXyUUvM-R6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f2c39ff-fed5-43bf-a8fe-5c2883c80f22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33mWARNING: Skipping tensorflow as it is not installed.\u001b[0m\u001b[33m\n",
            "\u001b[0mCollecting git+https://github.com/huggingface/transformers\n",
            "  Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-8fqc93o4\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-8fqc93o4\n",
            "  Resolved https://github.com/huggingface/transformers to commit 43bfd093e1817c0333a1e10fcbdd54f1032baad0\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (3.12.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (0.16.4)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (0.14.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (0.3.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.35.0.dev0) (4.66.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers==4.35.0.dev0) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers==4.35.0.dev0) (4.5.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.35.0.dev0) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.35.0.dev0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.35.0.dev0) (2.0.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.35.0.dev0) (2023.7.22)\n",
            "Collecting git+https://github.com/huggingface/peft.git\n",
            "  Cloning https://github.com/huggingface/peft.git to /tmp/pip-req-build-82yzmqcy\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-82yzmqcy\n",
            "  Resolved https://github.com/huggingface/peft.git to commit 99f792e8a3be001c55e71d148a7a8d25b931181f\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (23.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (5.9.4)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (6.0)\n",
            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (2.0.1+cu118)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (4.35.0.dev0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (4.66.1)\n",
            "Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (0.23.0)\n",
            "Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from peft==0.6.0.dev0) (0.3.3)\n",
            "Requirement already satisfied: huggingface-hub in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.21.0->peft==0.6.0.dev0) (0.16.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft==0.6.0.dev0) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft==0.6.0.dev0) (3.27.5)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch>=1.13.0->peft==0.6.0.dev0) (17.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.6.0.dev0) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.6.0.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.15,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers->peft==0.6.0.dev0) (0.14.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub->accelerate>=0.21.0->peft==0.6.0.dev0) (2023.6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft==0.6.0.dev0) (2.1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.6.0.dev0) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.6.0.dev0) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.6.0.dev0) (2.0.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers->peft==0.6.0.dev0) (2023.7.22)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft==0.6.0.dev0) (1.3.0)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade datasets"
      ],
      "metadata": {
        "id": "Mj6CRIq4_eYd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19a2c609-d96c-4c12-81c7-12d83ad852f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.14.5)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.23.5)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
            "Requirement already satisfied: dill<0.3.8,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.7)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.1)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.15)\n",
            "Requirement already satisfied: fsspec[http]<2023.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.5)\n",
            "Requirement already satisfied: huggingface-hub<1.0.0,>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.16.4)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (3.2.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets) (4.5.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2.0.5)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->datasets) (2023.7.22)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install py7zr"
      ],
      "metadata": {
        "id": "zlK4qI6a_lw6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c7c2792-c976-4fad-8877-50863240b9ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: py7zr in /usr/local/lib/python3.10/dist-packages (0.20.6)\n",
            "Requirement already satisfied: texttable in /usr/local/lib/python3.10/dist-packages (from py7zr) (1.7.0)\n",
            "Requirement already satisfied: pycryptodomex>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from py7zr) (3.19.0)\n",
            "Requirement already satisfied: pyzstd>=0.14.4 in /usr/local/lib/python3.10/dist-packages (from py7zr) (0.15.9)\n",
            "Requirement already satisfied: pyppmd<1.1.0,>=0.18.1 in /usr/local/lib/python3.10/dist-packages (from py7zr) (1.0.0)\n",
            "Requirement already satisfied: pybcj>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from py7zr) (1.0.1)\n",
            "Requirement already satisfied: multivolumefile>=0.2.3 in /usr/local/lib/python3.10/dist-packages (from py7zr) (0.2.3)\n",
            "Requirement already satisfied: brotli>=1.0.9 in /usr/local/lib/python3.10/dist-packages (from py7zr) (1.1.0)\n",
            "Requirement already satisfied: inflate64>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from py7zr) (0.3.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from py7zr) (5.9.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install xformers"
      ],
      "metadata": {
        "id": "lxBu-hTh_sET",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1599e74d-15e0-4e60-d88e-85a0ee0956f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: xformers in /usr/local/lib/python3.10/dist-packages (0.0.22)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xformers) (1.23.5)\n",
            "Requirement already satisfied: torch==2.0.1 in /usr/local/lib/python3.10/dist-packages (from xformers) (2.0.1+cu118)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (3.1.2)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch==2.0.1->xformers) (2.0.0)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1->xformers) (3.27.5)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch==2.0.1->xformers) (17.0.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch==2.0.1->xformers) (2.1.3)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch==2.0.1->xformers) (1.3.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SFnqVkBgt9VX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show torch"
      ],
      "metadata": {
        "id": "RqicMQs_t8Nu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a86a2a63-9d1c-46ea-a67b-01d8e82107a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: torch\n",
            "Version: 2.0.1+cu118\n",
            "Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration\n",
            "Home-page: https://pytorch.org/\n",
            "Author: PyTorch Team\n",
            "Author-email: packages@pytorch.org\n",
            "License: BSD-3\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: filelock, jinja2, networkx, sympy, triton, typing-extensions\n",
            "Required-by: accelerate, fastai, ludwig, peft, torchaudio, torchdata, torchmetrics, torchtext, torchvision, triton, xformers\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip show transformers"
      ],
      "metadata": {
        "id": "OwO4iNggt7-F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fba43e43-ee15-47c3-dce7-e28ccb0443d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name: transformers\n",
            "Version: 4.35.0.dev0\n",
            "Summary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\n",
            "Home-page: https://github.com/huggingface/transformers\n",
            "Author: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\n",
            "Author-email: transformers@huggingface.co\n",
            "License: Apache 2.0 License\n",
            "Location: /usr/local/lib/python3.10/dist-packages\n",
            "Requires: filelock, huggingface-hub, numpy, packaging, pyyaml, regex, requests, safetensors, tokenizers, tqdm\n",
            "Required-by: ludwig, peft\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "yDbFgDs-_uIT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "y2zlqMNM_v7t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import os\n",
        "\n",
        "import copy\n",
        "\n",
        "import gc\n",
        "\n",
        "from typing import Any, Callable\n",
        "\n",
        "import time\n",
        "\n",
        "from functools import wraps\n",
        "from inspect import (\n",
        "    BoundArguments,\n",
        "    signature,\n",
        ")\n",
        "\n",
        "from collections import OrderedDict\n",
        "\n",
        "from google.colab import data_table\n",
        "\n",
        "import yaml\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import torch\n",
        "from torch import Tensor\n",
        "\n",
        "import datasets\n",
        "from datasets import load_dataset, Dataset, DatasetDict\n",
        "\n",
        "import transformers\n",
        "from transformers import BitsAndBytesConfig, AutoModelForCausalLM, LlamaForCausalLM, MistralForCausalLM, AutoTokenizer, LlamaTokenizerFast, GenerationConfig, TextGenerationPipeline, BatchEncoding\n",
        "from transformers.generation.utils import GreedySearchDecoderOnlyOutput\n",
        "\n",
        "from peft import PeftModel, PeftModelForCausalLM, PeftConfig, LoraConfig\n",
        "\n",
        "from ludwig.api import LudwigModel, TrainingResults\n",
        "\n",
        "\n",
        "import logging"
      ],
      "metadata": {
        "id": "fd_LA_2Wx_qr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Enable text wrapping so we don't have to scroll horizontally and create a function to flush CUDA cache."
      ],
      "metadata": {
        "id": "A2SRHcKAJYuu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import HTML, display\n",
        "\n",
        "def set_css() -> None:\n",
        "  display(HTML('''\n",
        "  <style>\n",
        "    pre {\n",
        "        white-space: pre-wrap;\n",
        "    }\n",
        "  </style>\n",
        "  '''))\n",
        "\n",
        "get_ipython().events.register('pre_run_cell', set_css)\n"
      ],
      "metadata": {
        "id": "Ht4eVWxB13QL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(model: LudwigModel, df_test: pd.DataFrame) -> list[list[str]]:\n",
        "  return model.predict(df_test)[0][\"summary_response\"].tolist()"
      ],
      "metadata": {
        "id": "E_dFOQDtqY3R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "b6b13677-2e69-46c0-8f9c-8a05e8bcc094"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wzdU2cA6eEY5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g0g8MijBeFFW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Setup Your HuggingFace Token** 🤗\n",
        "\n",
        "Obtain a [HuggingFace API Token](https://huggingface.co/settings/tokens) before proceeding. You may need to signup on HuggingFace if you do not aleady have an account: https://huggingface.co/join\n"
      ],
      "metadata": {
        "id": "SUkPQ9OLP0Po"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "# import locale; locale.getpreferredencoding = lambda: \"UTF-8\"\n",
        "import logging\n",
        "import os\n",
        "import torch\n",
        "import yaml\n",
        "\n",
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "\n",
        "locale.getpreferredencoding = getpreferredencoding\n",
        "\n",
        "\n",
        "os.environ[\"HUGGING_FACE_HUB_TOKEN\"] = getpass.getpass(\"Token:\")\n",
        "assert os.environ[\"HUGGING_FACE_HUB_TOKEN\"]"
      ],
      "metadata": {
        "id": "nXOIxmfbQHSz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bb381ea2-cd64-4211-e9f0-764452547799"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token:··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Import The SAMSum Dataset** 📋\n",
        "\n"
      ],
      "metadata": {
        "id": "sV0D2rjRbk4z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import datasets\n",
        "from datasets import load_dataset, Dataset, DatasetDict"
      ],
      "metadata": {
        "id": "9G2U5Acllz10",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "5672a40c-4c11-4d85-8de4-9407f95b7aea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_dataset_dict: DatasetDict = datasets.load_dataset(\"samsum\")\n",
        "samsum_dataset_dict"
      ],
      "metadata": {
        "id": "vy6LbLqNpD5Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491,
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        "outputId": "4176011c-3e76-4437-ce65-9805bf8bf5a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
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              "  "
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading builder script:   0%|          | 0.00/3.36k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 14732\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 819\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'dialogue', 'summary'],\n",
              "        num_rows: 818\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataset: Dataset = samsum_dataset_dict[\"train\"]\n",
        "test_dataset: Dataset = samsum_dataset_dict[\"test\"]\n",
        "validation_dataset: Dataset = samsum_dataset_dict[\"validation\"]"
      ],
      "metadata": {
        "id": "Xbsyizk1lzlI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "58188afc-d496-491b-8304-bbdc0fd67f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train: pd.DataFrame = train_dataset.to_pandas()\n",
        "df_test: pd.DataFrame = test_dataset.to_pandas()\n",
        "df_validation: pd.DataFrame = validation_dataset.to_pandas()"
      ],
      "metadata": {
        "id": "KCOZtrnmpixz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "8a7da8f0-3d86-4e77-8d21-94ea623089b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_evaluation: pd.DataFrame = df_train.sample(n=10, random_state=200)\n",
        "df_train = df_train.drop(df_evaluation.index)"
      ],
      "metadata": {
        "id": "XzRWZ_2EX4kz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "d6df4d5a-4822-4bf4-d216-2bdc7f2c6236"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train.sample(n=700, random_state=200)\n",
        "df_test = df_test.sample(n=200, random_state=200)\n",
        "df_validation = df_validation.sample(n=100, random_state=200)"
      ],
      "metadata": {
        "id": "wHRTHrj7pilB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "07bc8a0f-0ef9-469d-9048-579127db2ef7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assert df_train.shape[0] == 700\n",
        "assert df_test.shape[0] == 200\n",
        "assert df_validation.shape[0] == 100"
      ],
      "metadata": {
        "id": "0VAG6CQIq6oK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "addc3e63-b7e4-4e3b-d558-5bf3f52db1c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import data_table; data_table.enable_dataframe_formatter()\n",
        "import numpy as np; np.random.seed(123)"
      ],
      "metadata": {
        "id": "jDFfITwQr-9s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "6355e0e2-78f4-43cb-e7cf-5ffdf7cc1973"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train[\"split\"] = np.zeros(df_train.shape[0])\n",
        "df_test[\"split\"] = np.ones(df_test.shape[0])\n",
        "df_validation[\"split\"] = np.full(df_validation.shape[0], 2)"
      ],
      "metadata": {
        "id": "_XjPa4XCq6dv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "ed49a57a-7f6e-430a-de20-6dc2429f734b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset = pd.concat([df_train, df_test, df_validation])"
      ],
      "metadata": {
        "id": "QwjCzdMsq6RC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "58c0f5ff-e963-4bbb-a5c5-746d7a08035a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset[\"split\"] = df_dataset[\"split\"].astype(int)"
      ],
      "metadata": {
        "id": "o0yh3pl3piVJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "2793e00f-968e-4335-e035-4b2af920ecf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset.shape"
      ],
      "metadata": {
        "id": "aHV-Gty_s1sA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "ce1f30db-0dcc-47ab-be41-ee426ca01a63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "assert df_dataset[df_dataset[\"split\"] == 0].shape[0] == 700\n",
        "assert df_dataset[df_dataset[\"split\"] == 1].shape[0] == 200\n",
        "assert df_dataset[df_dataset[\"split\"] == 2].shape[0] == 100"
      ],
      "metadata": {
        "id": "sjj5g2bOYpDV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "0aba7725-b5c3-4036-a7d6-fe21622e940a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_dataset.head(10)"
      ],
      "metadata": {
        "id": "6YlNOIOSlzNM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 998
        },
        "outputId": "21bae15d-bc29-48f0-d5f5-9f7576a2ab97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
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              "5690   13810506  Jane: Hey gorgeous, have any plans for the sum...   \n",
              "2512   13729573  Regina: did you finish the required trainings ...   \n",
              "\n",
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              "592                Nick decided to buy an induction hob.      0  \n",
              "1681   Jackson is on a trip. His journey on the train...      0  \n",
              "3470   Jenna asks Scott to buy her an adhesive bandag...      0  \n",
              "5690   Patricia and Jane will go on an organised trip...      0  \n",
              "2512   Tatiana hasn't finished the required trainings...      0  "
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
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              "\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f62acad1-0f78-44a6-8006-e9dae793fd09 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f62acad1-0f78-44a6-8006-e9dae793fd09');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-83ab627b-f868-4e37-8ea7-b579c6a315db button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 7270,\n            'f': \"7270\",\n        },\n\"13828796\",\n\"Adella: Hey!! So, for the longest time I've a crazy idea, that we should compose something together :D (Seeing as we both have Musescore, why not?) Basically, I would compose a few bars of music, then you, then me ect. What do you think?\\r\\nRod: Great idea \\r\\nAdella: :D\\r\\nRod: Have you composed something already?\\r\\nAdella: No! :P Wwould you like to start? We have different styles, but I don't mind adjusting to whatever you create.\\r\\nRod: Okay, I can try :) I'm curious what we'll create. XD\\r\\nAdella: Oh, and I'm insisting on the rule: \\\"I'll compose it when I compose it and send it when I send it\\\". Sounds good?\\r\\nRod: Sounds great! Obviously we're both so busy and this definitely doesn't take priority :)\\r\\nRod: I'll probably only manage to get started on the weekend. \\r\\nAdella: That's alright - are you sure you're okay with starting? I don't mind.\\r\\nRod: No, it's fine :) I want to.\\r\\nAdella: Great! :D See you on the other side ;)\",\n\"Rod and Adella will compose a song together. They are both busy and Rod will start first at the weekend.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 1109,\n            'f': \"1109\",\n        },\n\"13863061\",\n\"Tori: New hair!\\nTori: <file_photo>\\nHugh: Pretty girl \\ud83d\\ude0d\\nTori: \\ud83d\\ude0a\",\n\"Hugh likes Tori's new haircut.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 13165,\n            'f': \"13165\",\n        },\n\"13681152\",\n\"Martin: Hi Tom, this is my new mobile number in my new country. I hope you're all fine and enjoy your christmas holidays? Speak to you soon.\\r\\nTom: Hi Martin nice to hear from you.\\r\\nTom: we're all ready to come and play cards at your new home, what do you think of that? Everyone is ok, we only need the cards\\r\\nTom:  <file_photo>\\r\\nMartin: I miss you all. Ok for cards at home. Bring your own cards, but not the spoon \\ud83d\\ude02\\r\\nMartin: don't forgot the bottles\\r\\nTom: \\ud83d\\ude02\\ud83d\\ude02\\r\\nMartin: Have a nice christmas \\r\\nTom: you too. we think about you, it'll be great to come and visit you next year... with ou without cards, but with \\ud83c\\udf7e\",\n\"Martin and Tom are going to meet next year to play cards and have a drink together at Martin's.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 11264,\n            'f': \"11264\",\n        },\n\"13716583\",\n\"Phoebe: I had a terrible day. Anyone up for some cheerful movie today?\\r\\nJennifer: There is Despicable Me in Helios at 8.\\r\\nPhoebe: Sounds lovely, thank you, hon.\\r\\nPatrick: Sorry, ladies, no can do today. But, Phoebs, I\\u2019m sending you a virtual hug\\r\\nPatrick: <file_gif>\\r\\nPhoebe: Thank you, sir <3 Next time! Jen, let\\u2019s meet at the entrance at 7.45, OK?\\r\\nJennifer: Sure, see you then.\",\n\"Phoebe had a terrible day. She will meet at 7:45 with Jennifer to watch Despicable Me in Helios. Patrick can't go.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 11662,\n            'f': \"11662\",\n        },\n\"13812757\",\n\"Michael: I was in no mood for talk \\r\\nMichael: I was unpleasantly surprised to find they were talking about me behind my back\\r\\nRichard: Oh, I see\\r\\nRichard: Leave it behind\\r\\nRichard: Are you coming to a Christmas party though?\\r\\nMichael: Still thinking\\r\\nMichael: I don't want to give them any additional opportunity of gossiping \\r\\nRichard: You don't have influence on it\\r\\nMichael: Yeah, maybe you are wright \\r\\nRichard: You see, now you are talking\\r\\nRichard: How about before at my place?\\r\\nRichard: With Monika and Sandra?\\r\\nMichael: ...\\r\\nRichard: Does it mean yes?\\r\\nMichael: Yes, whatever...\\r\\nRichard: Come on man, don't be such a jerk\\r\\nRichard: We'll have fun\\r\\nRichard: Just come and I'll arrange everything\\r\\nMichael: Ok man\\r\\nMichael: Thx\\r\\nRichard: Ok settled then\",\n\"They were gossiping about Michael. He will come to the Christmas party. He, Richard, Monica and Sandra will have a before at Richard's place. \",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 592,\n            'f': \"592\",\n        },\n\"13715945\",\n\"Nick: Opinions required! Gas or induction hob?\\r\\nBen: Having lived with an induction hob for a while, i\\u2019m not convinced.. \\r\\nRuth: induction- very sleek and quick to boil!\\r\\nBen: but it doesn\\u2019t maintain a constant temperature! Is it typical of all induction or i just got an old one?\\r\\nRuth: they pulse if use don\\u2019t use proper pans\\r\\nBen: what do you mean proper? Do you mean better+heavier?\\r\\nRuth: yeah, simply suitable\\r\\nBen: and i guess i have to learn how to use it..\\r\\nRuth: yeah, it\\u2019s just different comparing to gas\\r\\nChristian: gas, absolutely without a question- nothing else gives you the control!\\r\\nNick: I\\u2019m definitely more interested in a controllable consistent heat\\r\\nMary: with induction it\\u2019s like on and off so you have to regulate temperature.. \\r\\nKate: induction- yes, gas- no cause it takes ages to boil water!\\r\\nTim: you can always use an electric kettle you know?\\r\\nKate: haha! Not funny!\\r\\nKate: it\\u2019s easier to clean as well.\\r\\nHarry: I\\u2019d go for induction cause it keeps the temp after you finish cooking so the food is still warm \\r\\nTom: Induction! 100%\\r\\nSusan: our induction was terrible! I think it\\u2019s common!\\r\\nEmma: another vote for induction here! \\r\\nRuth: All chefs seem to say gas!\\r\\nTom: I sell more induction hobs then gas! It\\u2019s getting popular and i can see why!\\r\\nEmma: we got ours from the John Lewis outlet so it was ex display and therefore very affordable!\\r\\nNick: cheer guys for all your opinions! Great talk! I think i\\u2019ll go for.. Induction.\",\n\"Nick decided to buy an induction hob.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 1681,\n            'f': \"1681\",\n        },\n\"13815727\",\n\"Aubrey: how was your trip, babe? are you already in the hotel?\\r\\nJackson: no, i'm not, my train is delayed by two hours... :/ there was an accident on the railway line and all trains were held up for 40 minutes\\r\\nJackson: and i sit next to the guy who's constantly humming\\r\\nJackson: he's driving me CRAZY!!\\r\\nAubrey: why won't you tell him to stop?\\r\\nJackson: because i've already asked him not to this\\r\\nJackson: he just stared at me for a while and then started humming even louder\\r\\nAubrey: what a jerk!\\r\\nAubrey: babe, let me know when you get to the hotel, ok?\\r\\nJackson: sure. i'm going to lose my mind here!\\r\\nAubrey: be brave, hon ;) love you :*\\r\\nJackson: I love you too! :* :*\",\n\"Jackson is on a trip. His journey on the train is not pleasant. He is annoyed by one of the passengers. The train is delayed because of an accident on the railway. Jackson will contact Aubrey once he arrives in the hotel.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 3470,\n            'f': \"3470\",\n        },\n\"13611993\",\n\"Scott: Wanna smthg from the shop?\\r\\nJenna: no, thx\\r\\nScott: and from the pharmacy?\\r\\nJenna: oh yes, can you buy me an adhesive bandage?\\r\\nJenna: u know, yesterday I cut my finger\\u2026\\r\\nScott: u did what?\\r\\nJenna: I was slicing bread\\u2026 everything\\u2019s alright now. Don\\u2019t worry\\r\\nScott: OMG\\r\\nScott: sure, no probs\\r\\nJenna: thanx\",\n\"Jenna asks Scott to buy her an adhesive bandage. She cut her finger the previous day.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 5690,\n            'f': \"5690\",\n        },\n\"13810506\",\n\"Jane: Hey gorgeous, have any plans for the summer? :D\\r\\nPatricia: Not yet, what are you up to? :>\\r\\nJane: Just been thinking what to do this and I kind of want to go somewhere far far away\\r\\nPatricia: Cool, up for that. Do you have anything specific in mind?\\r\\nJane: Yeah, couple of places. Cuba, Mexico or, other side, Thailand or Vietnam?\\r\\nPatricia: Wow, exotic! :D\\r\\nJane: Well, far far away as I said ;) Remember the guy I told you about? The traveller? He was telling me all about Cuba and Mexico, it sounded really cool.\\r\\nPatricia: Isn't it a bit dangerous?...\\r\\nJane: I don't know, I was thinking about going for an organised trip?\\r\\nPatricia: Ok, sounds fine. Never been on one, but happy to discover something new :)\\r\\nJane: I've been to a few and I think it's great if you don't have a lot of time and want to see a lot.\\r\\nPatricia: Are there any good trips? Cuba sounds cool, actually I'd prefer it to Mexico.\\r\\nJane: How about Vietnam or Thailand? We could look into both options.\\r\\nPatricia: Been to Thailand, but wouldn't mind coming back, the food <3\\r\\nJane: :D hahahaha. Honestly can't decide. There's that one really cool trip to Thailand, Vietnam and Cambodia.\\r\\nPatricia: Wow, sounds great! How much is it?\\r\\nJane: Around 2k for almost two weeks, seems fair to me.\\r\\nPatricia: Are the meals included? \\r\\nJane: Hm, breakfasts and some dinners, depends on where you are.\\r\\nPatricia: Cool, maybe it's even better so we could explore the local stuff ;)\\r\\nJane: So it kind of sounds Asia it is :D God, already excited <3\\r\\nPatricia: hahahaha, so it does! When could you get any time off? August is off limits for me and probably not the best month to go there.\\r\\nJane: I know, July then? It may be cheaper, because no one wants to go there in the summer :D\\r\\nPatricia: Quite true, July sounds fine for me. Gotta go tell my boss I need a time off and ready to go!\\r\\nJane: Yeah, start the countdown, I will contact the agency to book the trip. Will send you all details.\\r\\nPatricia: Thanks! Also, is it a direct flight?\\r\\nJane: Yes! Couldn't be more perfect, could it?\",\n\"Patricia and Jane will go on an organised trip to Thailand, Vietnam and Cambodia. They will go in July. Patricia will ask her boss for a time off and Jane will contact the agency. She will send Patricia the details. They will take a direct flight. The trip lasts almost 2 weeks and costs around 2k.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }],\n [{\n            'v': 2512,\n            'f': \"2512\",\n        },\n\"13729573\",\n\"Regina: did you finish the required trainings they sent us this week?\\r\\nTatiana: no, not yet\\r\\nRegina: you should, the deadline is tomorrow\\r\\nTatiana: what? why didn't they say sooner?\\r\\nRegina: they did, it was in the memo\\r\\nTatiana: i didn't read it... shit\\r\\nRegina: you better get to it right away\\r\\nTatiana: is there a lot of them?\\r\\nRegina: they are all on this online platform we always work on but they took a lot of time\\r\\nTatiana: so how long? I asked if there is a lot of them?\\r\\nRegina: I tried to go as quickly as possible and it took me almost 9 hours\\r\\nTatiana: what?! shit... i am so screwed...\\r\\nRegina: if you start now you will make it tomorrow by the end of the day\\r\\nTatiana: i was supposed to have a day off tomorrow... i am so stupid\\r\\nRegina: oh crap :/ sorry to hear\\r\\nTatiana: yeah, i wanted to go home sooner to spend more time with my mom... i will have to cancel \\r\\nRegina: i can help you with some of the quizzes if you want, i probably remember a lot of answers\\r\\nTatiana: that would actually be great help but i don't want to take too much of your time\\r\\nRegina: no worries, we gotta help each other right?\\r\\nTatiana: yeah but are you sure?\\r\\nRegina: yes, we can do some of them together but you have to start now, otherwise you won't make it in time\\r\\nTatiana: I will... thanks a lot of reminding me, i can't imagine what would happen if i forgot it lol\\r\\nRegina: the boss wouldn't be too happy about it\\r\\nTatiana: that's for sure ;)\",\n\"Tatiana hasn't finished the required trainings. Regina needed 9 hours to complete them. Regina will help Tatiana but she has to start now to finish till tomorrow.\",\n{\n            'v': 0,\n            'f': \"0\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"string\", \"id\"], [\"string\", \"dialogue\"], [\"string\", \"summary\"], [\"number\", \"split\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-cd40df14-24cb-4c2a-8e88-5e761692bac6\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cd40df14-24cb-4c2a-8e88-5e761692bac6')\"\n            title=\"Suggest charts.\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n    </g>\n</svg>\n  </button>\n  \n<style>\n  .colab-df-quickchart {\n      --bg-color: #E8F0FE;\n      --fill-color: #1967D2;\n      --hover-bg-color: #E2EBFA;\n      --hover-fill-color: #174EA6;\n      --disabled-fill-color: #AAA;\n      --disabled-bg-color: #DDD;\n  }\n\n  [theme=dark] .colab-df-quickchart {\n      --bg-color: #3B4455;\n      --fill-color: #D2E3FC;\n      --hover-bg-color: #434B5C;\n      --hover-fill-color: #FFFFFF;\n      --disabled-bg-color: #3B4455;\n      --disabled-fill-color: #666;\n  }\n\n  .colab-df-quickchart {\n    background-color: var(--bg-color);\n    border: none;\n    border-radius: 50%;\n    cursor: pointer;\n    display: none;\n    fill: var(--fill-color);\n    height: 32px;\n    padding: 0;\n    width: 32px;\n  }\n\n  .colab-df-quickchart:hover {\n    background-color: var(--hover-bg-color);\n    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n    fill: var(--button-hover-fill-color);\n  }\n\n  .colab-df-quickchart-complete:disabled,\n  .colab-df-quickchart-complete:disabled:hover {\n    background-color: var(--disabled-bg-color);\n    fill: var(--disabled-fill-color);\n    box-shadow: none;\n  }\n\n  .colab-df-spinner {\n    border: 2px solid var(--fill-color);\n    border-color: transparent;\n    border-bottom-color: var(--fill-color);\n    animation:\n      spin 1s steps(1) infinite;\n  }\n\n  @keyframes spin {\n    0% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n      border-left-color: var(--fill-color);\n    }\n    20% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    30% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n      border-right-color: var(--fill-color);\n    }\n    40% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    60% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n    }\n    80% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-bottom-color: var(--fill-color);\n    }\n    90% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n    }\n  }\n</style>\n\n  <script>\n    async function quickchart(key) {\n      const quickchartButtonEl =\n        document.querySelector('#' + key + ' button');\n      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n      quickchartButtonEl.classList.add('colab-df-spinner');\n      try {\n        const charts = await google.colab.kernel.invokeFunction(\n            'suggestCharts', [key], {});\n      } catch (error) {\n        console.error('Error during call to suggestCharts:', error);\n      }\n      quickchartButtonEl.classList.remove('colab-df-spinner');\n      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n    }\n    (() => {\n      let quickchartButtonEl =\n        document.querySelector('#df-cd40df14-24cb-4c2a-8e88-5e761692bac6 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XVjCEQ9_shBM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the length of each cell in each column\n",
        "df_dataset['num_characters_dialogue'] = df_dataset['dialogue'].apply(lambda x: len(x))\n",
        "df_dataset['num_characters_summary'] = df_dataset['summary'].apply(lambda x: len(x))\n",
        "\n",
        "# Show Distribution\n",
        "df_dataset.hist(column=['num_characters_dialogue', 'num_characters_summary'])\n",
        "\n",
        "# Calculating the average\n",
        "average_chars_dialogue= df_dataset['num_characters_dialogue'].mean()\n",
        "average_chars_summary = df_dataset['num_characters_summary'].mean()\n",
        "\n",
        "print(f'Average number of tokens in the dialogue column: {(average_chars_dialogue / 3):.0f}')\n",
        "print(f'Average number of tokens in the summary column: {(average_chars_summary / 3):.0f}')\n"
      ],
      "metadata": {
        "id": "_3x3stdDKoCT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "0528028a-3682-44d0-bb94-e468b992b2b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average number of tokens in the dialogue column: 173\n",
            "Average number of tokens in the summary column: 37\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_evaluation"
      ],
      "metadata": {
        "id": "S7WejJVkP-tF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849
        },
        "outputId": "aa0074f4-3af0-44f3-b41d-6ca4f5116dc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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        {
          "output_type": "execute_result",
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              "               id                                           dialogue  \\\n",
              "2613     13817590  Adriana: Sorry, I guess I can't make it. I'm f...   \n",
              "1446   13820141-1  Claire: Have you seen the news?\\r\\nGreg: No. W...   \n",
              "123      13863151  Josh: You're a nutter!!!\\nJosh: I could never ...   \n",
              "10820    13682324  Marcel: Hey\\r\\nRodolfo: Hello\\r\\nMarcel: What'...   \n",
              "7557     13862729  Don: Do you see me online?\\nAlfred: Nope\\nDon:...   \n",
              "1472     13682038  Travis: Hi, are you there?\\r\\nLana: Hi Travis,...   \n",
              "12537    13611551  Alex: hey, I'm writing about the book :)\\r\\nEl...   \n",
              "3599     13812226  Karen: Hi John, it's me, lost my phone\\r\\nKare...   \n",
              "12418    13821509  Claire: There is a letter for Tracy from the t...   \n",
              "12571    13728189  Donald: Are you playing poker tonight? It's at...   \n",
              "\n",
              "                                                 summary  \n",
              "2613   Adriana is feeling sick, so she needs to give ...  \n",
              "1446   Claire is unhappy about her neighbour becoming...  \n",
              "123    Paul didn't convince Josh to go bungee jumping...  \n",
              "10820  Rodolfo took a difficult exam today, he'll get...  \n",
              "7557   Don is not showing to Alfred as online. Don wi...  \n",
              "1472   Lana and Travis are talking about his trip to ...  \n",
              "12537  Elizabeth is bringing a book for Alex. Alex ne...  \n",
              "3599       Karen lost her phone and is on her way home.   \n",
              "12418  Tracy got a letter from the tax office. Claire...  \n",
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              "      <th>2613</th>\n",
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              "      <td>Adriana: Sorry, I guess I can't make it. I'm f...</td>\n",
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              "      <td>Karen: Hi John, it's me, lost my phone\\r\\nKare...</td>\n",
              "      <td>Karen lost her phone and is on her way home.</td>\n",
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              "      <td>Tracy got a letter from the tax office. Claire...</td>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-d0a7c565-71f4-4073-90d7-cc6d85d3782f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d0a7c565-71f4-4073-90d7-cc6d85d3782f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-15734eb4-d3f4-476a-888f-0ad395a089d6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-15734eb4-d3f4-476a-888f-0ad395a089d6')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-15734eb4-d3f4-476a-888f-0ad395a089d6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 2613,\n            'f': \"2613\",\n        },\n\"13817590\",\n\"Adriana: Sorry, I guess I can't make it. I'm feeling sick again.\\r\\nAdriana: I planned so many things for today...\\r\\nAdriana: Hate it.\\r\\nElisa: What a pity...\\r\\nElisa: Next time!\\r\\nElisa: Hope you'll get better soon!\\r\\nAdriana: Hope so too.\\r\\nAdriana: How was at the pool?\\r\\nElisa: The best \\u2764\\ufe0f\\r\\nElisa: 1 hour of swimming and 1 hour of sauna\\r\\nElisa: Love it!\\r\\nAdriana:\\u2764\\ufe0f\\r\\nAdriana: I'm such a bad swimmer that you couldn't stand me xD\\r\\nElisa: Ahahaha\\r\\nElisa: There was such a girl today, soooo annoying \\ud83d\\ude02\\r\\nAdriana: Just like me \\ud83d\\ude02\\ud83d\\ude02\\ud83d\\ude02\",\n\"Adriana is feeling sick, so she needs to give up her plans for today. Elisa went to the pool, she did 1 hour of swimming and 1 hour of sauna. \"],\n [{\n            'v': 1446,\n            'f': \"1446\",\n        },\n\"13820141-1\",\n\"Claire: Have you seen the news?\\r\\nGreg: No. What\\u2019s happened?\\r\\nClaire: That bitch my neighbor became an MP \\ud83e\\udd2e\\r\\nKevin: Ooo, well done! You must be so jealous \\ud83e\\udd23\\r\\nClaire: Stop it. I hate her so much. She killed my cat \\ud83d\\ude48\\r\\nGreg: Whaaaaat???\\r\\nClaire: Really. It was 5 years ago. She hit it with a car.\\r\\nGreg: Okay. I understand \\ud83d\\ude12\",\n\"Claire is unhappy about her neighbour becoming an MP. \"],\n [{\n            'v': 123,\n            'f': \"123\",\n        },\n\"13863151\",\n\"Josh: You're a nutter!!!\\nJosh: I could never do things like that\\nPaul: Wouldn't you ever do the bungee jumping or skydiving? C'mon that's not so scary :)\\nJosh: hahahah\\nJosh: Nope, I wouldn't do that :)\\nPaul: The next time you're coming with me!\\nJosh: No way, mate\",\n\"Paul didn't convince Josh to go bungee jumping or skydiving with him next time.\"],\n [{\n            'v': 10820,\n            'f': \"10820\",\n        },\n\"13682324\",\n\"Marcel: Hey\\r\\nRodolfo: Hello\\r\\nMarcel: What's up?\\r\\nRodolfo: Nothing just got back to the house\\r\\nMarcel: I had an exam today and I  think I will get a great grade\\r\\nRodolfo: Good for you. Mine was pretty hard. Not what I was expecting. But I should pass it\\r\\nMarcel: Oh yeah now I remember you had an exam. Let me know if u have results\\r\\nRodolfo: How nice nice of you to remember now\\r\\nMarcel: Haha i can't believe. I saw when one car hit another one. They were one behind another. It wasn't so much but still it was. The drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\\r\\nRodolfo: I will only know in like 3 weeks\\r\\nMarcel: Oh long time\\r\\nRodolfo: Yea rather long\\r\\nMarcel: Hopefully it will be fine\",\n\"Rodolfo took a difficult exam today, he'll get results in 3 weeks. Marcel had a different exam and he expects a good grade. Marcel saw a car slightly hit another car, but no trouble came from it.\"],\n [{\n            'v': 7557,\n            'f': \"7557\",\n        },\n\"13862729\",\n\"Don: Do you see me online?\\nAlfred: Nope\\nDon: Ok, something's wrong, I'll have to check my settings\\nAlfred: Ok\",\n\"Don is not showing to Alfred as online. Don will check the settings.\"],\n [{\n            'v': 1472,\n            'f': \"1472\",\n        },\n\"13682038\",\n\"Travis: Hi, are you there?\\r\\nLana: Hi Travis, good to hear from you after holiday.\\r\\nTravis: Yeah, long time\\r\\nLana: So how was it? Greece, right?\\r\\nTravis: Yep, pure magic. 10 day there and 4 days in Albania\\r\\nLana: Wow, Albania. Never been there.\\r\\nTravis: Kind of wild place, still. But I liked Greece better\\r\\nLana: You stayed in one place?\\r\\nTravis: No, moved around. We saw almost everything there was to see\\r\\nLana: Sounds exciting. \\r\\nTravis: Take a look. <file_photo>\\r\\nLana: Wow, breathtaking. I guess you'll show us more when we meet in the office\\r\\nTravis: Sure thing. But I'm not back till Monday\\r\\nLana: I see. Two more days of freedom.\\r\\nTravis: Precisely. How are things there anyway\\r\\nLana: Same old, same old.\\r\\nTravis: Been worse?\\r\\nLana: I guess it's just this time of the year when not much happens. \\r\\nTravis: Have you planned your holiday yet?\\r\\nLana: Not really. I guess I'll just find something last minute in September.\\r\\nTravis: Greece perhaps\\r\\nLana: I'll decide when I see the photos ha ha ha. See you around. Gotta go.\\r\\nTravis: Sure. See you on Monday\",\n\"Lana and Travis are talking about his trip to Greece and Albania. He liked Greece more. Travis and Luna will see each other in the office on Monday. Lana will probably go on holiday in September.\"],\n [{\n            'v': 12537,\n            'f': \"12537\",\n        },\n\"13611551\",\n\"Alex: hey, I'm writing about the book :)\\r\\nElizabeth: heey, thanks for the message, I'm packing it to my bag right now.\\r\\nAlex: thank you so much...you don't even know grateful I am\\r\\nElizabeth: No problem, really. If I can help you in any other way just ask.\\r\\nAlex: Actually I have a few questions for you if you don't mind\\r\\nElizabeth: Sure, what is it?\\r\\nAlex: I'm starting with the senior group next week and I'm so stressed out cause I never taught the adults...could you give me any clues how to prepare, what to do and so on?\\r\\nElizabeth: Of course. First thing - relax, cause stress is destructive. I remember my first classes with the adults and I couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\\r\\nAlex: I get it...and how does your lesson usually look like?\\r\\nElizabeth: It depends, you know. But for most of the time I'm just trying to stick to my fixed plan. At the beginning checking the attendance, asking them to give me the date in the foreing language so I can put it down in the register. Then a short conversation.\\r\\nAlex: and what after that?\\r\\nElizabeth: Then I usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. I don't want them to do the exercices only, cause it's too automatic.\\r\\nAlex: sounds really cool...you are so experienced...I wish I was too :/\\r\\nElizabeth: calm down, you'll just learn everything with practice. If you don't want to make your lessons too formal, you can look for some games. I recommend Kaboom.\\r\\nAlex: Kaboom? I don't know this game\\r\\nElizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. Every person takes out a single piece of paper and has to translate a word written on it. If they spot the answer, they keep it, if they don't, the card goes back to the bag.\\r\\nAlex: Sounds really cool! I'm sure I'll try it. Thank you so much for your help!!\\r\\nElizabeth: No problem, any time.\",\n\"Elizabeth is bringing a book for Alex. Alex needs advice on how to teach adults. Elizabeth suggests getting relaxed and devising own work schedule. Elizabeth describes her usual lesson. Elizabeth recommends a vocabulary game called Kaboom.\"],\n [{\n            'v': 3599,\n            'f': \"3599\",\n        },\n\"13812226\",\n\"Karen: Hi John, it's me, lost my phone\\r\\nKaren: On my way home, don't worry. Text me if you've got this. I'll have this phone only for couple of minutes.\\r\\nJohn: Got it, go back home safely.\",\n\"Karen lost her phone and is on her way home. \"],\n [{\n            'v': 12418,\n            'f': \"12418\",\n        },\n\"13821509\",\n\"Claire: There is a letter for Tracy from the tax office\\r\\nTracy: Thanks mum. Can you open it?\\r\\nNatalie: It's probably regarding tax return \\r\\nNatalie: I got mine last week\\r\\nTracy: Open and send me a photo\\r\\nTracy: Better priv \\ud83e\\udd11\\r\\nClaire: No problem \",\n\"Tracy got a letter from the tax office. Claire will open it and send a photo to Tracy.\"],\n [{\n            'v': 12571,\n            'f': \"12571\",\n        },\n\"13728189\",\n\"Donald: Are you playing poker tonight? It's at Dave's house\\r\\nSteve: Yeah I was planning to. Do we need to bring whiskey?\\r\\nDonald: I've got some, no worries.\\r\\nSteve: Ok see you\",\n\"Donald and Steve are playing poker at Dave's house tonight.\"]],\n        columns: [[\"number\", \"index\"], [\"string\", \"id\"], [\"string\", \"dialogue\"], [\"string\", \"summary\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-6d774f21-3a51-412a-b252-3bf44369f7d7\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d774f21-3a51-412a-b252-3bf44369f7d7')\"\n            title=\"Suggest charts.\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n    </g>\n</svg>\n  </button>\n  \n<style>\n  .colab-df-quickchart {\n      --bg-color: #E8F0FE;\n      --fill-color: #1967D2;\n      --hover-bg-color: #E2EBFA;\n      --hover-fill-color: #174EA6;\n      --disabled-fill-color: #AAA;\n      --disabled-bg-color: #DDD;\n  }\n\n  [theme=dark] .colab-df-quickchart {\n      --bg-color: #3B4455;\n      --fill-color: #D2E3FC;\n      --hover-bg-color: #434B5C;\n      --hover-fill-color: #FFFFFF;\n      --disabled-bg-color: #3B4455;\n      --disabled-fill-color: #666;\n  }\n\n  .colab-df-quickchart {\n    background-color: var(--bg-color);\n    border: none;\n    border-radius: 50%;\n    cursor: pointer;\n    display: none;\n    fill: var(--fill-color);\n    height: 32px;\n    padding: 0;\n    width: 32px;\n  }\n\n  .colab-df-quickchart:hover {\n    background-color: var(--hover-bg-color);\n    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n    fill: var(--button-hover-fill-color);\n  }\n\n  .colab-df-quickchart-complete:disabled,\n  .colab-df-quickchart-complete:disabled:hover {\n    background-color: var(--disabled-bg-color);\n    fill: var(--disabled-fill-color);\n    box-shadow: none;\n  }\n\n  .colab-df-spinner {\n    border: 2px solid var(--fill-color);\n    border-color: transparent;\n    border-bottom-color: var(--fill-color);\n    animation:\n      spin 1s steps(1) infinite;\n  }\n\n  @keyframes spin {\n    0% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n      border-left-color: var(--fill-color);\n    }\n    20% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    30% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n      border-right-color: var(--fill-color);\n    }\n    40% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    60% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n    }\n    80% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-bottom-color: var(--fill-color);\n    }\n    90% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n    }\n  }\n</style>\n\n  <script>\n    async function quickchart(key) {\n      const quickchartButtonEl =\n        document.querySelector('#' + key + ' button');\n      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n      quickchartButtonEl.classList.add('colab-df-spinner');\n      try {\n        const charts = await google.colab.kernel.invokeFunction(\n            'suggestCharts', [key], {});\n      } catch (error) {\n        console.error('Error during call to suggestCharts:', error);\n      }\n      quickchartButtonEl.classList.remove('colab-df-spinner');\n      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n    }\n    (() => {\n      let quickchartButtonEl =\n        document.querySelector('#df-6d774f21-3a51-412a-b252-3bf44369f7d7 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K2AqdTATItST"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bg8BH0pT32w6"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o7gfR8kd5b8D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_prompt_template: str = \"\"\"\n",
        "Summarize this dialogue:\n",
        "\n",
        "### Dialogue: {dialogue}\n",
        "\n",
        "### Synopsis:\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "rRaPGgG-5bDW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "75f6ce60-4346-4faf-a98d-64828f8d1382"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PbZhNfI15cyi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9yjx0fvm5c-O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_test_dialogue: str = \"\"\"\n",
        "A: Hi Tom, are you busy tomorrow’s afternoon?\n",
        "B: I’m pretty sure I am. What’s up?\n",
        "A: Can you go with me to the animal shelter?.\n",
        "B: What do you want to do?\n",
        "A: I want to get a puppy for my son.\n",
        "B: That will make him so happy.\n",
        "A: Yeah, we’ve discussed it many times. I think he’s ready now.\n",
        "B: That’s good. Raising a dog is a tough issue. Like having a baby ;-)\n",
        "A: I'll get him one of those little dogs.\n",
        "B: One that won't grow up too big;-)\n",
        "A: And eat too much;-))\n",
        "B: Do you know which one he would like?\n",
        "A: Oh, yes, I took him there last Monday. He showed me one that he really liked.\n",
        "B: I bet you had to drag him away.\n",
        "A: He wanted to take it home right away ;-).\n",
        "B: I wonder what he'll name it.\n",
        "A: He said he’d name it after his dead hamster – Lemmy  - he's  a great Motorhead fan :-)))\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "elhEYFUR4yxi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "995779bc-b0aa-4b7b-f8b3-2efb2bb11516"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_test_prompt: str = samsum_prompt_template.format(**{\"dialogue\": samsum_test_dialogue})"
      ],
      "metadata": {
        "id": "l-vMflb342J8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f3c91611-df35-4d58-fd18-0ad25755fe28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QX6M61CZ4y1u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M4gonDZq4y6J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q_i2mDo332w6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mistral_7b_sharded_base_model_name: str = \"alexsherstinsky/Mistral-7B-v0.1-sharded\""
      ],
      "metadata": {
        "id": "5fxfxlqA32w7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "4d1b3546-20f7-4369-ccfe-80df1aedd48c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qOoWU4rt32w7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bz3jHQrj32w7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4-YvfTHq32w7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the pre-trained (general) Mistral 7B LLM (the sharded version)."
      ],
      "metadata": {
        "id": "UuxaFU40Wgg8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bnb_config_base_model: BitsAndBytesConfig = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    load_in_8bit=False,\n",
        "    llm_int8_threshold=6.0,\n",
        "    llm_int8_has_fp16_weight=False,\n",
        "    bnb_4bit_compute_dtype=\"float16\",\n",
        "    bnb_4bit_use_double_quant=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        ")"
      ],
      "metadata": {
        "id": "-qdQYJM-32w7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "109ed4b1-332b-4991-fdaa-f34aad3ec896"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_tokenizer: LlamaTokenizerFast = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=mistral_7b_sharded_base_model_name, trust_remote_code=True, padding_side=\"left\")\n",
        "print(base_model_tokenizer.eos_token)\n",
        "base_model_tokenizer.pad_token = base_model_tokenizer.eos_token"
      ],
      "metadata": {
        "id": "IJCnFnDG32w7",
        "colab": {
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        "outputId": "b6a158fd-ca5f-4834-db4f-5b40ccbd71a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/979 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1794a89749f453b9b0045b4b6ef6b46"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "17f5b46c05b6427fa8fcc3e390133b71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/42.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05f12ee006464f2e9f0d99641984490b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/145 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1590c20970204cd6bd38c1b1d0469df1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "</s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model: MistralForCausalLM = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=mistral_7b_sharded_base_model_name, device_map=\"auto\", torch_dtype=torch.float16, offload_folder=\"offload\", trust_remote_code=True, low_cpu_mem_usage=True, quantization_config=bnb_config_base_model)"
      ],
      "metadata": {
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        "colab": {
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          "height": 401,
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        "outputId": "07464ef7-1662-4c7f-c51e-cb445a953606"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
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          "metadata": {}
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ANwf1E1G32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9bEoIjnr32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run inference on the pre-trained (general) Mistral 7B LLM to establish baseline quality."
      ],
      "metadata": {
        "id": "I8CZ_aogX_XR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_sequences_generator: TextGenerationPipeline = transformers.pipeline(\n",
        "    task=\"text-generation\",\n",
        "    tokenizer=base_model_tokenizer,\n",
        "    model=base_model,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")"
      ],
      "metadata": {
        "id": "Dxgx07i332w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "78d71d7a-c406-4aae-868a-0eec28fffa97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Am-v3jIQ32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_sequences: list[dict] | list[list[dict]] = base_model_sequences_generator(\n",
        "    text_inputs=samsum_test_prompt,\n",
        "    do_sample=True,\n",
        "    top_k=50,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=base_model_tokenizer.eos_token_id,\n",
        "    max_length=512,\n",
        "    return_text=True,\n",
        ")"
      ],
      "metadata": {
        "id": "42dF57oM32w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b1e66201-dba9-467a-e4ee-5d1d9be21281"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_sequence: dict = base_model_sequences[0]"
      ],
      "metadata": {
        "id": "MLjLzOUU32w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "45a1fa52-c093-43e3-d605-c1054ab0b73b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[GENERATED_TEXT] BASE_MODEL_PREDICTION:\\n{base_model_sequence[\"generated_text\"]} ; TYPE: {str(type(base_model_sequence[\"generated_text\"]))}')"
      ],
      "metadata": {
        "id": "s-jeBZKA32w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "outputId": "a8704f4f-da00-4e58-8afc-1eb1ec223631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[GENERATED_TEXT] BASE_MODEL_PREDICTION:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: \n",
            "A: Hi Tom, are you busy tomorrow’s afternoon?\n",
            "B: I’m pretty sure I am. What’s up?\n",
            "A: Can you go with me to the animal shelter?.\n",
            "B: What do you want to do?\n",
            "A: I want to get a puppy for my son.\n",
            "B: That will make him so happy.\n",
            "A: Yeah, we’ve discussed it many times. I think he’s ready now.\n",
            "B: That’s good. Raising a dog is a tough issue. Like having a baby ;-)\n",
            "A: I'll get him one of those little dogs.\n",
            "B: One that won't grow up too big;-)\n",
            "A: And eat too much;-))\n",
            "B: Do you know which one he would like?\n",
            "A: Oh, yes, I took him there last Monday. He showed me one that he really liked.\n",
            "B: I bet you had to drag him away.\n",
            "A: He wanted to take it home right away ;-).\n",
            "B: I wonder what he'll name it.\n",
            "A: He said he’d name it after his dead hamster – Lemmy  - he's  a great Motorhead fan :-)))\n",
            "\n",
            "\n",
            "### Synopsis:\n",
            "It's another weekend day, so it seems that A's son wants to have a puppy, which is OK for his parents. They have discussed it many times before so A knows the details about his son's choice: which kind of dog he wants and he even knows what to feed it (I believe A is a real dog-loving guy ;-)\n",
            "\n",
            "B also thinks that to raise a dog is a tough issue, like raising a baby;)\n",
            "\n",
            "And that's a funny question, how much will he eat ;-)) (He'll probably say the same, like he's said about his kid already! ;-P).\n",
            "\n",
            "Let's see which dog he'll choose: the biggest dog? ;)  Or maybe something bigger than a hamster but smaller than a Labrador (as his former pet)? ;-P\n",
            "\n",
            "B wishes him luck with his new hobby and also with his name choice ;-)\n",
            "\n",
            "### Answers:\n",
            "\n",
            "1. How long have you known A?\n",
            "\n",
            "\n",
            "2. ; TYPE: <class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V5ScZM4p32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eoO12V8I32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "id": "vgCkhl_O32w8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b10db25e-35e2-4060-fe77-3aed2b067381"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "21"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Z3ktXeSv32w8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YPEOgtpV5l8e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run inference on the pre-trained (general) Mistral 7B LLM over the evaluation dataset."
      ],
      "metadata": {
        "id": "qzUigfZGaA5g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_samsum_evaluation: pd.DataFrame = df_evaluation.copy()"
      ],
      "metadata": {
        "id": "-Xul_RaA32w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "6eb8a85e-1916-4239-c4fd-a89f9721619b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_samsum_evaluation[\"prompt\"] = df_samsum_evaluation[\"dialogue\"].apply(lambda x: samsum_prompt_template.format(**{\"dialogue\": x}))"
      ],
      "metadata": {
        "id": "nKbudLf-32w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "44f16b0e-9c54-40b8-efff-89882f97ee26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "base_model_sequences = base_model_sequences_generator(\n",
        "    text_inputs=df_samsum_evaluation[\"prompt\"].to_list(),\n",
        "    do_sample=True,\n",
        "    top_k=50,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=base_model_tokenizer.eos_token_id,\n",
        "    max_length=512,\n",
        "    return_text=True,\n",
        ")"
      ],
      "metadata": {
        "id": "OzLqIVSi32w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "outputId": "7489b935-e806-45c7-ec4e-480cded9de3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1268: UserWarning: Input length of input_ids is 571, but `max_length` is set to 512. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print(base_model_sequences)"
      ],
      "metadata": {
        "id": "d2RDrO5k32w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "05bdb886-c2cc-41ec-bf98-aa831cc3a25e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[BASE_MODEL_EVALUATION_BEGIN]')\n",
        "idx: int = 0\n",
        "print(f'\\n[=============EXAMPLE_{idx}_BEGIN=============]')\n",
        "for dialogue, synopsis in zip(df_samsum_evaluation[\"dialogue\"].to_list(), base_model_sequences):\n",
        "  print(f'\\n[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\\n{synopsis[0][\"generated_text\"]}')\n",
        "  print(f'\\n[=============EXAMPLE_{idx}_END=============]')\n",
        "  idx += 1\n",
        "\n",
        "print(f'\\n[BASE_MODEL_EVALUATION_END]')"
      ],
      "metadata": {
        "id": "rSl262o732w9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "12646c3f-442e-4533-c5bb-284730c8993a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[BASE_MODEL_EVALUATION_BEGIN]\n",
            "\n",
            "[=============EXAMPLE_0_BEGIN=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Adriana: Sorry, I guess I can't make it. I'm feeling sick again.\r\n",
            "Adriana: I planned so many things for today...\r\n",
            "Adriana: Hate it.\r\n",
            "Elisa: What a pity...\r\n",
            "Elisa: Next time!\r\n",
            "Elisa: Hope you'll get better soon!\r\n",
            "Adriana: Hope so too.\r\n",
            "Adriana: How was at the pool?\r\n",
            "Elisa: The best ❤️\r\n",
            "Elisa: 1 hour of swimming and 1 hour of sauna\r\n",
            "Elisa: Love it!\r\n",
            "Adriana:❤️\r\n",
            "Adriana: I'm such a bad swimmer that you couldn't stand me xD\r\n",
            "Elisa: Ahahaha\r\n",
            "Elisa: There was such a girl today, soooo annoying 😂\r\n",
            "Adriana: Just like me 😂😂😂\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Adriana is soooo upset because she couldn't go to the pool with Elisa. She wanted to relax and do some sport because of her recent sickness. Also, her cousin canceled last minute one more time. Her mood was pretty bad and she ended up crying. When Elisa came over she found out what happened and tried to help but she also failed at trying to cheer up her cousin.\n",
            "\n",
            "### Explanation:\n",
            "\n",
            "\"Adriana was feeling sick again, so for the third time in three months she cancelled the pool plan. So she cancelled the plan in Elisa's and her cousin's company. Elisa, who had planned to go with them couldnt do it and she got sad about this. When Adriana cried because of the reason, she became even sadder. So she went out to calm down and take her mind off of everything. She found out that Elisa had been at the pool with her other cousin. She made some saunas and swims, but she got surprised by another annoying cousin who got crazy when they got to the pool. Adriana told Elisa the news and also her problems. Sadly, Elisa also cancelled because her day made her annoyed. Elisa cheered Adriana up but also failed\"\n",
            "\n",
            "[=============EXAMPLE_0_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Claire: Have you seen the news?\r\n",
            "Greg: No. What’s happened?\r\n",
            "Claire: That bitch my neighbor became an MP 🤮\r\n",
            "Kevin: Ooo, well done! You must be so jealous 🤣\r\n",
            "Claire: Stop it. I hate her so much. She killed my cat 🙈\r\n",
            "Greg: Whaaaaat???\r\n",
            "Claire: Really. It was 5 years ago. She hit it with a car.\r\n",
            "Greg: Okay. I understand 😒\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "1. What are the words / phrases that summarize the story? (e.g What happened? What was said? 5 years ago, hit it with a car)\n",
            "2. What are the parts that did not make sense / were not clearly explained?\n",
            "   - This part was left out. \"hit it with a car.\"\n",
            "   - What words / phrases should be used to describe that?\n",
            "   - Which part makes sense in this context?\n",
            "   - What words make it difficult to understand?\n",
            "\n",
            "> We have two different versions.\n",
            "\n",
            "### Version 1:\n",
            "\n",
            "This dialog discusses the death of the animal by the vehicle that came from the neighbor. It is a common discussion about the car that belongs to the neighbor and the animal that became victims. There was an animal that was hit by the vehicle. There is a victim in the incident. A neighborhood discussion has also become a hot topic of discussion, this is something that becomes a common subject that will never run out, in every neighborhood, there is always someone who is talking about the vehicle. There are also things that are a bit shocking that it is common to have a conversation when talking about a neighbor, a neighbor, this is something that was done by him. However, these things are also often heard even though it is clear that it has a vehicle. But of course, this should not be taken for granted only by those involved. Everyone deserves to know what needs to be known in order to convey information to others, in addition to these conversations, there are also discussions that come from their neighbors, such as in the afternoon there is a hot discussion, in the evening there is something that needs to be revealed, such as to tell someone that there was a hot discussion, the neighbors, when they are in\n",
            "\n",
            "[=============EXAMPLE_1_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Josh: You're a nutter!!!\n",
            "Josh: I could never do things like that\n",
            "Paul: Wouldn't you ever do the bungee jumping or skydiving? C'mon that's not so scary :)\n",
            "Josh: hahahah\n",
            "Josh: Nope, I wouldn't do that :)\n",
            "Paul: The next time you're coming with me!\n",
            "Josh: No way, mate\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "```\n",
            "Ashish: <start>\n",
            "Ted:\n",
            "    - Good Morning, Ashish\n",
            "Ashish:\n",
            "    - Good morning, Ted\n",
            "\n",
            "[During the call]\n",
            "\n",
            "<end>\n",
            "```\n",
            "\n",
            "[=============EXAMPLE_2_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Marcel: Hey\r\n",
            "Rodolfo: Hello\r\n",
            "Marcel: What's up?\r\n",
            "Rodolfo: Nothing just got back to the house\r\n",
            "Marcel: I had an exam today and I  think I will get a great grade\r\n",
            "Rodolfo: Good for you. Mine was pretty hard. Not what I was expecting. But I should pass it\r\n",
            "Marcel: Oh yeah now I remember you had an exam. Let me know if u have results\r\n",
            "Rodolfo: How nice nice of you to remember now\r\n",
            "Marcel: Haha i can't believe. I saw when one car hit another one. They were one behind another. It wasn't so much but still it was. The drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\r\n",
            "Rodolfo: I will only know in like 3 weeks\r\n",
            "Marcel: Oh long time\r\n",
            "Rodolfo: Yea rather long\r\n",
            "Marcel: Hopefully it will be fine\n",
            "\n",
            "### Synopsis:\n",
            "Marcel is in touch with Rodolfo and talks about his exam. Rodolfo also talks about his exam and they discuss they talk about a car accident.\n",
            "\n",
            "[=============EXAMPLE_3_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Don: Do you see me online?\n",
            "Alfred: Nope\n",
            "Don: Ok, something's wrong, I'll have to check my settings\n",
            "Alfred: Ok\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "\n",
            "\n",
            "### Prompt:\n",
            "```diff\n",
            "```\n",
            "\n",
            "### Difference:\n",
            "A difference that would normally appear between lines of text\n",
            "\n",
            "[=============EXAMPLE_4_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Travis: Hi, are you there?\r\n",
            "Lana: Hi Travis, good to hear from you after holiday.\r\n",
            "Travis: Yeah, long time\r\n",
            "Lana: So how was it? Greece, right?\r\n",
            "Travis: Yep, pure magic. 10 day there and 4 days in Albania\r\n",
            "Lana: Wow, Albania. Never been there.\r\n",
            "Travis: Kind of wild place, still. But I liked Greece better\r\n",
            "Lana: You stayed in one place?\r\n",
            "Travis: No, moved around. We saw almost everything there was to see\r\n",
            "Lana: Sounds exciting. \r\n",
            "Travis: Take a look. <file_photo>\r\n",
            "Lana: Wow, breathtaking. I guess you'll show us more when we meet in the office\r\n",
            "Travis: Sure thing. But I'm not back till Monday\r\n",
            "Lana: I see. Two more days of freedom.\r\n",
            "Travis: Precisely. How are things there anyway\r\n",
            "Lana: Same old, same old.\r\n",
            "Travis: Been worse?\r\n",
            "Lana: I guess it's just this time of the year when not much happens. \r\n",
            "Travis: Have you planned your holiday yet?\r\n",
            "Lana: Not really. I guess I'll just find something last minute in September.\r\n",
            "Travis: Greece perhaps\r\n",
            "Lana: I'll decide when I see the photos ha ha ha. See you around. Gotta go.\r\n",
            "Travis: Sure. See you on Monday\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Travis sent photos of his summer holiday to Lana.\n",
            "\n",
            "### Explanation:\n",
            "\n",
            "Travis and Lana are colleagues and have known each other for a while. He's had a holiday in Greece, though he also spent several days in Albania. She's wondering why he hasn't yet returned to work. Travis tells her that it was Albania which was wild. Greece was tamer but he preferred it. Then, she asks did he stay at one place or move around, Travis says that yes he did move around and saw almost all of Greece. Lana then asks if they were in one place or moved around, Travis says no, they moved around. Lana then asks what places Travis visited. Travis\n",
            "\n",
            "[=============EXAMPLE_5_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Alex: hey, I'm writing about the book :)\r\n",
            "Elizabeth: heey, thanks for the message, I'm packing it to my bag right now.\r\n",
            "Alex: thank you so much...you don't even know grateful I am\r\n",
            "Elizabeth: No problem, really. If I can help you in any other way just ask.\r\n",
            "Alex: Actually I have a few questions for you if you don't mind\r\n",
            "Elizabeth: Sure, what is it?\r\n",
            "Alex: I'm starting with the senior group next week and I'm so stressed out cause I never taught the adults...could you give me any clues how to prepare, what to do and so on?\r\n",
            "Elizabeth: Of course. First thing - relax, cause stress is destructive. I remember my first classes with the adults and I couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\r\n",
            "Alex: I get it...and how does your lesson usually look like?\r\n",
            "Elizabeth: It depends, you know. But for most of the time I'm just trying to stick to my fixed plan. At the beginning checking the attendance, asking them to give me the date in the foreing language so I can put it down in the register. Then a short conversation.\r\n",
            "Alex: and what after that?\r\n",
            "Elizabeth: Then I usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. I don't want them to do the exercices only, cause it's too automatic.\r\n",
            "Alex: sounds really cool...you are so experienced...I wish I was too :/\r\n",
            "Elizabeth: calm down, you'll just learn everything with practice. If you don't want to make your lessons too formal, you can look for some games. I recommend Kaboom.\r\n",
            "Alex: Kaboom? I don't know this game\r\n",
            "Elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. Every person takes out a single piece of paper and has to translate a word written on it. If they spot the answer, they keep it, if they don't, the card goes back to the bag.\r\n",
            "Alex: Sounds really cool! I'm sure I'll try it. Thank you so much for your help!!\r\n",
            "Elizabeth: No problem, any time.\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "\n",
            "\n",
            "[=============EXAMPLE_6_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Karen: Hi John, it's me, lost my phone\r\n",
            "Karen: On my way home, don't worry. Text me if you've got this. I'll have this phone only for couple of minutes.\r\n",
            "John: Got it, go back home safely.\n",
            "\n",
            "### Synopsis:\n",
            "I've lost mine phone. I'm at a hotel room. The hotel room has a TV, a clock, a table, three chairs and a bed. Karen, my wife, is with this phone. There is no window and there is a bed-side table lamp on the left side of the bed. The room is on the 6th floor of the hotel. There is a hallway, elevator and staircases. It's at night.\n",
            "\n",
            "[=============EXAMPLE_7_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Claire: There is a letter for Tracy from the tax office\r\n",
            "Tracy: Thanks mum. Can you open it?\r\n",
            "Natalie: It's probably regarding tax return \r\n",
            "Natalie: I got mine last week\r\n",
            "Tracy: Open and send me a photo\r\n",
            "Tracy: Better priv 🤑\r\n",
            "Claire: No problem \n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "In this dialogue, Claire sends a letter to Tracy about tax returns, which Tracy asks her mum Natalie to open. Natalie also received a similar letter from tax office last week. Once Tracy is done reading the content of the letter, he suggests his mother to share the image of the letter rather than text.\n",
            "\n",
            "## Part: 2\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Claire: I called a travel agency about the vacation \r\n",
            "Claire: And they didn't answer the phone. \r\n",
            "Tracy: Perhaps they are closed\r\n",
            "Claire: Maybe they have a different line for the bookings\r\n",
            "Claire: I'll give them one hour \n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "The Claire called the travel agency about a trip on her phone and since the travel agency hadn't answer her call, she tries the other phone line for the same.\n",
            "\n",
            "## Part: 3\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Tracy: What are you doing tonight?\r\n",
            "Claire: I'm going to my friend Rachel's birthday\r\n",
            "Natalie: I'm cooking burgers today too\r\n",
            "Claire: I will definitely have one\r\n",
            "Claire: If you call me to takeaway I will have two \n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Tracy asks what Claire is planning to do today. And Claire tells him that she is going to her friend Rachel's birthday, and then also clarifies her cooking plan for the day.\n",
            "\n",
            "## Part: 4\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Tracy: I got your message about the meeting \n",
            "Claire: I got it too. Can't attend\r\n",
            "Tracy: Me neither \n",
            "Claire: Let's see if we can get excused by the boss\r\n",
            "Tracy: We should get up early\r\n",
            "Clairé: It\n",
            "\n",
            "[=============EXAMPLE_8_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Donald: Are you playing poker tonight? It's at Dave's house\r\n",
            "Steve: Yeah I was planning to. Do we need to bring whiskey?\r\n",
            "Donald: I've got some, no worries.\r\n",
            "Steve: Ok see you\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\r\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "### Dialogue: What does this mean? \"Hey man, I’m in a bind. Can you spot me a hundred bucks?\"\n",
            "\n",
            "&#x200B;\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "\n",
            "### Dialogue: Hey man, I’m in a bind. Can you spot me a hundred bucks?\n",
            "Steve: Hey\n",
            "Dan: It’s me man it’s Dan come on\n",
            "Steve: Ok\n",
            "Dan: Hey man, I’m in a bind. Can you spot me a hundred bucks?\n",
            "Steve: I think I read on r/relationships that it is illegal in Indiana to loan money so don’t trust what they say on Reddit\n",
            "Dan: I just need some money to go get some new headlights for my car so I can pass inspection and start driving again before they give me more tickets\n",
            "Steve: You need headlights?\n",
            "Dan: Yeah\n",
            "Steve: For your car?\n",
            "Dan: Haha yeah\n",
            "Steve: Do they not come on?\n",
            "Dan: Yeah they don't\n",
            "Steve: Are you able to get them on with just you hand?\n",
            "Dan: I mean I use a combination of my hand and my car key to start my car but I’m not really that worried about getting the lights on. I just need the ticket I get now to go away and to be able to start driving on those beautiful highways that criss cross this majestic state\n",
            "Steve: You going to get in a crash then?\n",
            "Dan: Probably. I would love to hit a cyclist and do a burnout on top of him\n",
            "Steve: Just keep your license. You don’t really need to drive that much. Take public transit or ride a bike\n",
            "Dan: Yeah but\n",
            "\n",
            "[=============EXAMPLE_9_END=============]\n",
            "\n",
            "[BASE_MODEL_EVALUATION_END]\n"
          ]
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      "source": [
        "Configure and execute finetuning process."
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      "source": [
        "qlora_fine_tuning_config: dict = yaml.safe_load(\n",
        "\"\"\"\n",
        "model_type: llm\n",
        "base_model: alexsherstinsky/Mistral-7B-v0.1-sharded\n",
        "\n",
        "input_features:\n",
        "  - name: dialogue\n",
        "    type: text\n",
        "    preprocessing:\n",
        "      max_sequence_length: 1024\n",
        "\n",
        "output_features:\n",
        "  - name: summary\n",
        "    type: text\n",
        "    preprocessing:\n",
        "      max_sequence_length: 384\n",
        "\n",
        "prompt:\n",
        "  template: >-\n",
        "    Summarize this dialogue:\n",
        "\n",
        "    ### Dialogue: {dialogue}\n",
        "\n",
        "    ### Synopsis:\n",
        "\n",
        "generation:\n",
        "  temperature: 0.1\n",
        "  max_new_tokens: 512\n",
        "\n",
        "adapter:\n",
        "  type: lora\n",
        "\n",
        "quantization:\n",
        "  bits: 4\n",
        "\n",
        "preprocessing:\n",
        "  split:\n",
        "    # type: random\n",
        "    # probabilities: [0.9, 0.05, 0.05]\n",
        "    type: fixed\n",
        "\n",
        "trainer:\n",
        "  type: finetune\n",
        "  epochs: 5\n",
        "  batch_size: 1\n",
        "  eval_batch_size: 2\n",
        "  gradient_accumulation_steps: 16  # effective batch size = batch size * gradient_accumulation_steps\n",
        "  learning_rate: 2.0e-4\n",
        "  enable_gradient_checkpointing: true\n",
        "  learning_rate_scheduler:\n",
        "    decay: cosine\n",
        "    warmup_fraction: 0.03\n",
        "    reduce_on_plateau: 0\n",
        "\"\"\"\n",
        ")"
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        "id": "33GSuYmockcv"
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    {
      "cell_type": "code",
      "source": [
        "model: LudwigModel = LudwigModel(config=qlora_fine_tuning_config, logging_level=logging.INFO)"
      ],
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    {
      "cell_type": "code",
      "source": [
        "results: TrainingResults = model.train(dataset=df_dataset)"
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        "outputId": "8047cfae-42fb-44e6-ac16-0c6ab856ace5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
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          "text": [
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒════════════════════════╕\n",
            "INFO:ludwig.utils.print_utils:│ EXPERIMENT DESCRIPTION │\n",
            "INFO:ludwig.utils.print_utils:╘════════════════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.api:╒══════════════════╤═════════════════════════════════════════════════════════════════════════════════════════╕\n",
            "│ Experiment name  │ api_experiment                                                                          │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ Model name       │ run                                                                                     │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ Output directory │ /content/results/api_experiment_run                                                     │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ ludwig_version   │ '0.8'                                                                                   │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ command          │ ('/usr/local/lib/python3.10/dist-packages/colab_kernel_launcher.py -f '                 │\n",
            "│                  │  '/root/.local/share/jupyter/runtime/kernel-6011cb67-2d88-4c45-806c-70ed885defb9.json') │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ random_seed      │ 42                                                                                      │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ data_format      │ \"<class 'pandas.core.frame.DataFrame'>\"                                                 │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ torch_version    │ '2.0.1+cu118'                                                                           │\n",
            "├──────────────────┼─────────────────────────────────────────────────────────────────────────────────────────┤\n",
            "│ compute          │ {'gpu_type': 'Tesla T4', 'gpus_per_node': 1, 'num_nodes': 1}                            │\n",
            "╘══════════════════╧═════════════════════════════════════════════════════════════════════════════════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒═══════════════╕\n",
            "INFO:ludwig.utils.print_utils:│ LUDWIG CONFIG │\n",
            "INFO:ludwig.utils.print_utils:╘═══════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.api:User-specified config (with upgrades):\n",
            "\n",
            "INFO:ludwig.api:{   'adapter': {'type': 'lora'},\n",
            "    'base_model': 'alexsherstinsky/Mistral-7B-v0.1-sharded',\n",
            "    'generation': {'max_new_tokens': 512, 'temperature': 0.1},\n",
            "    'input_features': [   {   'name': 'dialogue',\n",
            "                              'preprocessing': {'max_sequence_length': 1024},\n",
            "                              'type': 'text'}],\n",
            "    'ludwig_version': '0.8',\n",
            "    'model_type': 'llm',\n",
            "    'output_features': [   {   'name': 'summary',\n",
            "                               'preprocessing': {'max_sequence_length': 384},\n",
            "                               'type': 'text'}],\n",
            "    'preprocessing': {'split': {'type': 'fixed'}},\n",
            "    'prompt': {   'template': 'Summarize this dialog:\\n'\n",
            "                              '### Dialogue: {dialogue}\\n'\n",
            "                              '### Synopsis:'},\n",
            "    'quantization': {'bits': 4},\n",
            "    'trainer': {   'batch_size': 1,\n",
            "                   'enable_gradient_checkpointing': True,\n",
            "                   'epochs': 5,\n",
            "                   'eval_batch_size': 2,\n",
            "                   'gradient_accumulation_steps': 16,\n",
            "                   'learning_rate': 0.0002,\n",
            "                   'learning_rate_scheduler': {   'decay': 'cosine',\n",
            "                                                  'reduce_on_plateau': 0,\n",
            "                                                  'warmup_fraction': 0.03},\n",
            "                   'type': 'finetune'}}\n",
            "INFO:ludwig.api:\n",
            "Full config saved to:\n",
            "/content/results/api_experiment_run/api_experiment/model/model_hyperparameters.json\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒═══════════════╕\n",
            "INFO:ludwig.utils.print_utils:│ PREPROCESSING │\n",
            "INFO:ludwig.utils.print_utils:╘═══════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.data.preprocessing:No cached dataset found at /content/40830258634811eeb9850242ac1c000c.training.hdf5. Preprocessing the dataset.\n",
            "INFO:ludwig.data.preprocessing:Using full dataframe\n",
            "INFO:ludwig.data.preprocessing:Building dataset (it may take a while)\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "INFO:ludwig.features.text_feature:Max length of feature 'None': 815 (without start and stop symbols)\n",
            "INFO:ludwig.features.text_feature:Setting max length using dataset: 817 (including start and stop symbols)\n",
            "INFO:ludwig.features.text_feature:max sequence length is 817 for feature 'None'\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "INFO:ludwig.features.text_feature:Max length of feature 'summary': 92 (without start and stop symbols)\n",
            "INFO:ludwig.features.text_feature:Setting max length using dataset: 94 (including start and stop symbols)\n",
            "INFO:ludwig.features.text_feature:max sequence length is 94 for feature 'summary'\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "INFO:ludwig.data.preprocessing:Building dataset: DONE\n",
            "INFO:ludwig.data.cache.manager:Writing preprocessed training set cache to /content/40830258634811eeb9850242ac1c000c.training.hdf5\n",
            "INFO:ludwig.data.cache.manager:Writing preprocessed validation set cache to /content/40830258634811eeb9850242ac1c000c.validation.hdf5\n",
            "INFO:ludwig.data.cache.manager:Writing preprocessed test set cache to /content/40830258634811eeb9850242ac1c000c.test.hdf5\n",
            "INFO:ludwig.data.cache.manager:Writing train set metadata to /content/40830258634811eeb9850242ac1c000c.meta.json\n",
            "INFO:ludwig.api:\n",
            "Dataset Statistics\n",
            "INFO:ludwig.api:╒════════════╤═══════════════╤════════════════════╕\n",
            "│ Dataset    │   Size (Rows) │ Size (In Memory)   │\n",
            "╞════════════╪═══════════════╪════════════════════╡\n",
            "│ Training   │           700 │ 164.19 Kb          │\n",
            "├────────────┼───────────────┼────────────────────┤\n",
            "│ Validation │           200 │ 47.00 Kb           │\n",
            "├────────────┼───────────────┼────────────────────┤\n",
            "│ Test       │           100 │ 23.56 Kb           │\n",
            "╘════════════╧═══════════════╧════════════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒═══════╕\n",
            "INFO:ludwig.utils.print_utils:│ MODEL │\n",
            "INFO:ludwig.utils.print_utils:╘═══════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.api:Warnings and other logs:\n",
            "INFO:ludwig.models.llm:Loading large language model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/8 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5963b25cb53f40eab765ddd94dd32640"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3277e3e3a0d8414abeca2552bad7eb14"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.models.llm:Done.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "INFO:ludwig.models.llm:==================================================\n",
            "INFO:ludwig.models.llm:Trainable Parameter Summary For Fine-Tuning\n",
            "INFO:ludwig.models.llm:Fine-tuning with adapter: lora\n",
            "INFO:ludwig.models.llm:==================================================\n",
            "INFO:ludwig.trainers.trainer:Gradient checkpointing enabled for training.\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒══════════╕\n",
            "INFO:ludwig.utils.print_utils:│ TRAINING │\n",
            "INFO:ludwig.utils.print_utils:╘══════════╛\n",
            "INFO:ludwig.utils.print_utils:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 3,407,872 || all params: 7,245,139,968 || trainable%: 0.04703666202518836\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Creating fresh model training run.\n",
            "INFO:ludwig.trainers.trainer:Training for 3500 step(s), approximately 5 epoch(s).\n",
            "INFO:ludwig.trainers.trainer:Early stopping policy: 5 round(s) of evaluation, or 3500 step(s), approximately 5 epoch(s).\n",
            "\n",
            "INFO:ludwig.trainers.trainer:Starting with step 0, epoch: 0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rTraining:   0%|          | 0/3500 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training:  20%|██        | 700/3500 [15:06<1:11:01,  1.52s/it, loss=0.0987]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 700, epoch: 1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:29<00:00,  1.12it/s]\n",
            "Evaluation test : 100%|██████████| 50/50 [00:42<00:00,  1.18it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 2m 12.1488s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0526 │       0.0431 │     0.0522 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     4.5959 │       4.6852 │     4.2030 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     1.3183 │       1.5221 │     1.5592 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 17578.1328 │   18488.5254 │ 18651.8906 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31897.7207 │   31923.5859 │ 31926.5098 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2677 │       0.2231 │     0.2384 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1682 │       0.1388 │     0.1500 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.7727 │       0.6756 │     0.6751 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1505 │       0.1056 │     0.1114 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.0938 │       0.0651 │     0.0692 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.4535 │       0.3302 │     0.3341 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2403 │       0.1921 │     0.2028 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1514 │       0.1196 │     0.1276 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.6879 │       0.5791 │     0.5750 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2597 │       0.2180 │     0.2309 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1633 │       0.1356 │     0.1452 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7498 │       0.6600 │     0.6557 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0001 │       0.0001 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     4.8210 │       5.0926 │     4.5175 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     1.3183 │       1.5221 │     1.5592 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Evaluation validation metric: 'summary' 'loss' improved.\n",
            "INFO:ludwig.trainers.trainer:'summary' 'loss' decreased by inf.\n",
            "INFO:ludwig.trainers.trainer:New best model saved.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training:  40%|████      | 1400/3500 [32:24<41:34,  1.19s/it, loss=0.0641]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 1400, epoch: 2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:29<00:00,  1.12it/s]\n",
            "Evaluation test : 100%|██████████| 50/50 [00:42<00:00,  1.18it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 2m 11.9027s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0566 │       0.0443 │     0.0528 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     4.4988 │       4.7199 │     4.2235 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.8732 │       1.4769 │     1.4972 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 16631.6367 │   18258.3770 │ 18365.0957 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31916.0996 │   31911.9902 │ 31920.6934 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2509 │       0.2232 │     0.2383 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1568 │       0.1385 │     0.1494 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.7644 │       0.6795 │     0.6800 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1470 │       0.1061 │     0.1164 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.0908 │       0.0653 │     0.0723 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.4891 │       0.3327 │     0.3490 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2250 │       0.1927 │     0.2028 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1406 │       0.1197 │     0.1272 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.6907 │       0.5845 │     0.5798 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2446 │       0.2180 │     0.2313 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1528 │       0.1352 │     0.1450 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7470 │       0.6639 │     0.6611 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0001 │       0.0001 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     4.9152 │       5.1475 │     4.5658 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.8732 │       1.4769 │     1.4972 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Evaluation validation metric: 'summary' 'loss' improved.\n",
            "INFO:ludwig.trainers.trainer:'summary' 'loss' decreased by 0.045.\n",
            "INFO:ludwig.trainers.trainer:New best model saved.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training:  60%|██████    | 2100/3500 [49:42<23:01,  1.01it/s, loss=0.0615]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 2100, epoch: 3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:29<00:00,  1.12it/s]\n",
            "Evaluation test : 100%|██████████| 50/50 [00:42<00:00,  1.17it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 2m 12.3435s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0647 │       0.0456 │     0.0545 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     4.4357 │       4.8174 │     4.3448 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.7807 │       1.4581 │     1.4830 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 16125.7354 │   18068.9844 │ 18175.1367 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31866.1289 │   31930.2559 │ 31938.5996 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2872 │       0.2246 │     0.2383 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1812 │       0.1389 │     0.1488 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.8058 │       0.6927 │     0.6899 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1794 │       0.1090 │     0.1226 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1126 │       0.0669 │     0.0760 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.5159 │       0.3466 │     0.3718 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2615 │       0.1945 │     0.2074 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1655 │       0.1204 │     0.1296 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.7265 │       0.5976 │     0.6007 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2829 │       0.2197 │     0.2329 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1786 │       0.1359 │     0.1454 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7922 │       0.6774 │     0.6746 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0004 │       0.0001 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     4.7413 │       5.2524 │     4.6788 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.7807 │       1.4581 │     1.4830 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Evaluation validation metric: 'summary' 'loss' improved.\n",
            "INFO:ludwig.trainers.trainer:'summary' 'loss' decreased by 0.019.\n",
            "INFO:ludwig.trainers.trainer:New best model saved.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training:  80%|████████  | 2800/3500 [1:07:01<13:00,  1.11s/it, loss=0.0555]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 2800, epoch: 4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:29<00:00,  1.12it/s]\n",
            "Evaluation test : 100%|██████████| 50/50 [00:42<00:00,  1.18it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 2m 12.4349s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0591 │       0.0454 │     0.0558 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     4.6974 │       4.8265 │     4.3404 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.7490 │       1.5032 │     1.5334 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 16013.8789 │   18042.4609 │ 18152.2734 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31925.3223 │   31918.9883 │ 31927.8184 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2688 │       0.2205 │     0.2388 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1664 │       0.1363 │     0.1490 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.8043 │       0.6821 │     0.6902 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1660 │       0.1071 │     0.1205 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1027 │       0.0657 │     0.0747 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.5056 │       0.3381 │     0.3628 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2435 │       0.1911 │     0.2038 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1510 │       0.1183 │     0.1273 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.7243 │       0.5868 │     0.5904 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2645 │       0.2152 │     0.2322 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1638 │       0.1330 │     0.1449 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.7901 │       0.6659 │     0.6721 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0001 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     4.9305 │       5.1902 │     4.6399 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.7490 │       1.5032 │     1.5334 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Last improvement of summary validation loss happened 700 step(s) ago.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training: 100%|██████████| 3500/3500 [1:24:21<00:00,  1.14s/it, loss=0.0147]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:\n",
            "Running evaluation for step: 3500, epoch: 5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluation valid: 100%|██████████| 100/100 [01:29<00:00,  1.11it/s]\n",
            "Evaluation test : 100%|██████████| 50/50 [00:42<00:00,  1.17it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.trainers.trainer:Evaluation took 2m 12.5701s\n",
            "\n",
            "INFO:ludwig.utils.metrics_printed_table:╒═══════════════════════╤════════════╤══════════════╤════════════╕\n",
            "│                       │      train │   validation │       test │\n",
            "╞═══════════════════════╪════════════╪══════════════╪════════════╡\n",
            "│ bleu                  │     0.0719 │       0.0417 │     0.0504 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ char_error_rate       │     5.5401 │       4.9449 │     4.4750 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ loss                  │     0.6371 │       1.5770 │     1.5993 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ next_token_perplexity │ 15388.7402 │   17748.3652 │ 17803.4277 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ perplexity            │ 31964.2246 │   31937.3516 │ 31940.8848 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_fmeasure       │     0.2879 │       0.2178 │     0.2338 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_precision      │     0.1778 │       0.1343 │     0.1452 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge1_recall         │     0.8522 │       0.6815 │     0.6855 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_fmeasure       │     0.1968 │       0.1024 │     0.1121 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_precision      │     0.1211 │       0.0627 │     0.0691 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rouge2_recall         │     0.5935 │       0.3284 │     0.3428 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_fmeasure       │     0.2646 │       0.1851 │     0.1969 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_precision      │     0.1637 │       0.1142 │     0.1224 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeL_recall         │     0.7773 │       0.5757 │     0.5780 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_fmeasure    │     0.2840 │       0.2124 │     0.2278 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_precision   │     0.1753 │       0.1309 │     0.1415 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ rougeLsum_recall      │     0.8419 │       0.6633 │     0.6691 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ sequence_accuracy     │     0.0000 │       0.0000 │     0.0000 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ token_accuracy        │     0.0000 │       0.0000 │     0.0001 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ word_error_rate       │     4.8637 │       5.2656 │     4.7193 │\n",
            "├───────────────────────┼────────────┼──────────────┼────────────┤\n",
            "│ combined_loss         │     0.6371 │       1.5770 │     1.5993 │\n",
            "╘═══════════════════════╧════════════╧══════════════╧════════════╛\n",
            "INFO:ludwig.trainers.trainer:Last improvement of summary validation loss happened 1400 step(s) ago.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training: 100%|██████████| 3500/3500 [1:26:34<00:00,  1.48s/it, loss=0.0147]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒═════════════════╕\n",
            "INFO:ludwig.utils.print_utils:│ TRAINING REPORT │\n",
            "INFO:ludwig.utils.print_utils:╘═════════════════╛\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.api:╒══════════════════════════════╤════════════════════╕\n",
            "│ Validation feature           │ summary            │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Validation metric            │ loss               │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model step              │ 2100               │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model epoch             │ 4                  │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model's validation loss │ 1.4580893516540527 │\n",
            "├──────────────────────────────┼────────────────────┤\n",
            "│ Best model's test loss       │ 1.4830340147018433 │\n",
            "╘══════════════════════════════╧════════════════════╛\n",
            "INFO:ludwig.api:\n",
            "Finished: api_experiment_run\n",
            "INFO:ludwig.api:Saved to: /content/results/api_experiment_run\n",
            "INFO:ludwig.utils.print_utils:\n",
            "INFO:ludwig.utils.print_utils:╒══════════╕\n",
            "INFO:ludwig.utils.print_utils:│ FINISHED │\n",
            "INFO:ludwig.utils.print_utils:╘══════════╛\n",
            "INFO:ludwig.utils.print_utils:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "WwRANm-rPZaL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D2Clph9V9AXg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jNCW85tmT2Qw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Perform Inference\n",
        "\n",
        "We can now use the model we finetuned above to make predictions on some test examples to see whether finetuning the large language model improve its ability to follow instructions/the tasks we're asking it to perform."
      ],
      "metadata": {
        "id": "1YkjcqMFMkwT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions_and_probabilities: tuple[pd.DataFrame, pd.DataFrame] = model.predict(df_evaluation)"
      ],
      "metadata": {
        "id": "5sQXLxvoMuIq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "26ec90a8-6be0-4a8f-bfec-40ce6a24277e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:ludwig.data.preprocessing:Specified split column split for fixed split strategy was not found in dataset.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "/usr/local/lib/python3.10/dist-packages/ludwig/data/preprocessing.py:1315: UserWarning: column: 'split' is required by the dataset splitter with params: {'type': 'fixed', 'column': 'split'}, but 'split' is not present in the `dataset_df` with columns: Index(['id', 'dialogue', 'summary'], dtype='object'). This is acceptable during serving setting where dataset splitting is irrelevant. You may see this warning if, for example, the model was trained with a configuration that used a stratified split on the target column, but for live predictions, a value for the target column is not to be provided.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rPrediction:   0%|          | 0/1 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.models.llm:For generating text, using: GenerationConfig {\n",
            "  \"do_sample\": true,\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"pad_token_id\": 2,\n",
            "  \"temperature\": 0.1\n",
            "}\n",
            "\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: adriana: sorry, i guess i can't make it. i'm feeling sick again.\n",
            "adriana: i planned so many things for today...\n",
            "adriana: hate it.\n",
            "elisa: what a pity...\n",
            "elisa: next time!\n",
            "elisa: hope you'll get better soon!\n",
            "adriana: hope so too.\n",
            "adriana: how was at the pool?\n",
            "elisa: the best ❤️\n",
            "elisa: 1 hour of swimming and 1 hour of sauna\n",
            "elisa: love it!\n",
            "adriana:❤️\n",
            "adriana: i'm such a bad swimmer that you couldn't stand me xd\n",
            "elisa: ahahaha\n",
            "elisa: there was such a girl today, soooo annoying 😂\n",
            "adriana: just like me 😂😂😂\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: adriana: sorry, i guess i can't make it. i'm feeling sick again.\n",
            "adriana: i planned so many things for today...\n",
            "adriana: hate it.\n",
            "elisa: what a pity...\n",
            "elisa: next time!\n",
            "elisa: hope you'll get better soon!\n",
            "adriana: hope so too.\n",
            "adriana: how was at the pool?\n",
            "elisa: the best ❤️\n",
            "elisa: 1 hour of swimming and 1 hour of sauna\n",
            "elisa: love it!\n",
            "adriana:❤️\n",
            "adriana: i'm such a bad swimmer that you couldn't stand me xd\n",
            "elisa: ahahaha\n",
            "elisa: there was such a girl today, soooo annoying 😂\n",
            "adriana: just like me 😂😂😂\n",
            "### synopsis: adriana is feeling sick again. elisa went to the pool and sauna.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: claire: have you seen the news?\n",
            "greg: no. what’s happened?\n",
            "claire: that bitch my neighbor became an mp 🤮\n",
            "kevin: ooo, well done! you must be so jealous 🤣\n",
            "claire: stop it. i hate her so much. she killed my cat 🙈\n",
            "greg: whaaaaat???\n",
            "claire: really. it was 5 years ago. she hit it with a car.\n",
            "greg: okay. i understand 😒\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: claire: have you seen the news?\n",
            "greg: no. what’s happened?\n",
            "claire: that bitch my neighbor became an mp 🤮\n",
            "kevin: ooo, well done! you must be so jealous 🤣\n",
            "claire: stop it. i hate her so much. she killed my cat 🙈\n",
            "greg: whaaaaat???\n",
            "claire: really. it was 5 years ago. she hit it with a car.\n",
            "greg: okay. i understand 😒\n",
            "### synopsis: claire's neighbour killed her cat 5 years ago. now she became an mp.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: josh: you're a nutter!!!\n",
            "josh: i could never do things like that\n",
            "paul: wouldn't you ever do the bungee jumping or skydiving? c'mon that's not so scary :)\n",
            "josh: hahahah\n",
            "josh: nope, i wouldn't do that :)\n",
            "paul: the next time you're coming with me!\n",
            "josh: no way, mate\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: josh: you're a nutter!!!\n",
            "josh: i could never do things like that\n",
            "paul: wouldn't you ever do the bungee jumping or skydiving? c'mon that's not so scary :)\n",
            "josh: hahahah\n",
            "josh: nope, i wouldn't do that :)\n",
            "paul: the next time you're coming with me!\n",
            "josh: no way, mate\n",
            "### synopsis: paul wants to do bungee jumping or skydiving. josh won't do it.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: marcel: hey\n",
            "rodolfo: hello\n",
            "marcel: what's up?\n",
            "rodolfo: nothing just got back to the house\n",
            "marcel: i had an exam today and i  think i will get a great grade\n",
            "rodolfo: good for you. mine was pretty hard. not what i was expecting. but i should pass it\n",
            "marcel: oh yeah now i remember you had an exam. let me know if u have results\n",
            "rodolfo: how nice nice of you to remember now\n",
            "marcel: haha i can't believe. i saw when one car hit another one. they were one behind another. it wasn't so much but still it was. the drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\n",
            "rodolfo: i will only know in like 3 weeks\n",
            "marcel: oh long time\n",
            "rodolfo: yea rather long\n",
            "marcel: hopefully it will be fine\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: marcel: hey\n",
            "rodolfo: hello\n",
            "marcel: what's up?\n",
            "rodolfo: nothing just got back to the house\n",
            "marcel: i had an exam today and i  think i will get a great grade\n",
            "rodolfo: good for you. mine was pretty hard. not what i was expecting. but i should pass it\n",
            "marcel: oh yeah now i remember you had an exam. let me know if u have results\n",
            "rodolfo: how nice nice of you to remember now\n",
            "marcel: haha i can't believe. i saw when one car hit another one. they were one behind another. it wasn't so much but still it was. the drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\n",
            "rodolfo: i will only know in like 3 weeks\n",
            "marcel: oh long time\n",
            "rodolfo: yea rather long\n",
            "marcel: hopefully it will be fine\n",
            "### synopsis: rodolfo had an exam today. marcel had an exam today. rodolfo and marcel will get good grades. rodolfo and marcel will know their results in 3 weeks.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: don: do you see me online?\n",
            "alfred: nope\n",
            "don: ok, something's wrong, i'll have to check my settings\n",
            "alfred: ok\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: don: do you see me online?\n",
            "alfred: nope\n",
            "don: ok, something's wrong, i'll have to check my settings\n",
            "alfred: ok\n",
            "### synopsis: don is not online.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: travis: hi, are you there?\n",
            "lana: hi travis, good to hear from you after holiday.\n",
            "travis: yeah, long time\n",
            "lana: so how was it? greece, right?\n",
            "travis: yep, pure magic. 10 day there and 4 days in albania\n",
            "lana: wow, albania. never been there.\n",
            "travis: kind of wild place, still. but i liked greece better\n",
            "lana: you stayed in one place?\n",
            "travis: no, moved around. we saw almost everything there was to see\n",
            "lana: sounds exciting. \n",
            "travis: take a look. <file_photo>\n",
            "lana: wow, breathtaking. i guess you'll show us more when we meet in the office\n",
            "travis: sure thing. but i'm not back till monday\n",
            "lana: i see. two more days of freedom.\n",
            "travis: precisely. how are things there anyway\n",
            "lana: same old, same old.\n",
            "travis: been worse?\n",
            "lana: i guess it's just this time of the year when not much happens. \n",
            "travis: have you planned your holiday yet?\n",
            "lana: not really. i guess i'll just find something last minute in september.\n",
            "travis: greece perhaps\n",
            "lana: i'll decide when i see the photos ha ha ha. see you around. gotta go.\n",
            "travis: sure. see you on monday\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: travis: hi, are you there?\n",
            "lana: hi travis, good to hear from you after holiday.\n",
            "travis: yeah, long time\n",
            "lana: so how was it? greece, right?\n",
            "travis: yep, pure magic. 10 day there and 4 days in albania\n",
            "lana: wow, albania. never been there.\n",
            "travis: kind of wild place, still. but i liked greece better\n",
            "lana: you stayed in one place?\n",
            "travis: no, moved around. we saw almost everything there was to see\n",
            "lana: sounds exciting. \n",
            "travis: take a look. <file_photo>\n",
            "lana: wow, breathtaking. i guess you'll show us more when we meet in the office\n",
            "travis: sure thing. but i'm not back till monday\n",
            "lana: i see. two more days of freedom.\n",
            "travis: precisely. how are things there anyway\n",
            "lana: same old, same old.\n",
            "travis: been worse?\n",
            "lana: i guess it's just this time of the year when not much happens. \n",
            "travis: have you planned your holiday yet?\n",
            "lana: not really. i guess i'll just find something last minute in september.\n",
            "travis: greece perhaps\n",
            "lana: i'll decide when i see the photos ha ha ha. see you around. gotta go.\n",
            "travis: sure. see you on monday\n",
            "### synopsis: traves and lana met during their holiday in greece. lana will decide about her holiday when she sees travis's photos.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: alex: hey, i'm writing about the book :)\n",
            "elizabeth: heey, thanks for the message, i'm packing it to my bag right now.\n",
            "alex: thank you so much...you don't even know grateful i am\n",
            "elizabeth: no problem, really. if i can help you in any other way just ask.\n",
            "alex: actually i have a few questions for you if you don't mind\n",
            "elizabeth: sure, what is it?\n",
            "alex: i'm starting with the senior group next week and i'm so stressed out cause i never taught the adults...could you give me any clues how to prepare, what to do and so on?\n",
            "elizabeth: of course. first thing - relax, cause stress is destructive. i remember my first classes with the adults and i couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\n",
            "alex: i get it...and how does your lesson usually look like?\n",
            "elizabeth: it depends, you know. but for most of the time i'm just trying to stick to my fixed plan. at the beginning checking the attendance, asking them to give me the date in the foreing language so i can put it down in the register. then a short conversation.\n",
            "alex: and what after that?\n",
            "elizabeth: then i usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. i don't want them to do the exercices only, cause it's too automatic.\n",
            "alex: sounds really cool...you are so experienced...i wish i was too :/\n",
            "elizabeth: calm down, you'll just learn everything with practice. if you don't want to make your lessons too formal, you can look for some games. i recommend kaboom.\n",
            "alex: kaboom? i don't know this game\n",
            "elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. every person takes out a single piece of paper and has to translate a word written on it. if they spot the answer, they keep it, if they don't, the card goes back to the bag.\n",
            "alex: sounds really cool! i'm sure i'll try it. thank you so much for your help!!\n",
            "elizabeth: no problem, any time.\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: alex: hey, i'm writing about the book :)\n",
            "elizabeth: heey, thanks for the message, i'm packing it to my bag right now.\n",
            "alex: thank you so much...you don't even know grateful i am\n",
            "elizabeth: no problem, really. if i can help you in any other way just ask.\n",
            "alex: actually i have a few questions for you if you don't mind\n",
            "elizabeth: sure, what is it?\n",
            "alex: i'm starting with the senior group next week and i'm so stressed out cause i never taught the adults...could you give me any clues how to prepare, what to do and so on?\n",
            "elizabeth: of course. first thing - relax, cause stress is destructive. i remember my first classes with the adults and i couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\n",
            "alex: i get it...and how does your lesson usually look like?\n",
            "elizabeth: it depends, you know. but for most of the time i'm just trying to stick to my fixed plan. at the beginning checking the attendance, asking them to give me the date in the foreing language so i can put it down in the register. then a short conversation.\n",
            "alex: and what after that?\n",
            "elizabeth: then i usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. i don't want them to do the exercices only, cause it's too automatic.\n",
            "alex: sounds really cool...you are so experienced...i wish i was too :/\n",
            "elizabeth: calm down, you'll just learn everything with practice. if you don't want to make your lessons too formal, you can look for some games. i recommend kaboom.\n",
            "alex: kaboom? i don't know this game\n",
            "elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. every person takes out a single piece of paper and has to translate a word written on it. if they spot the answer, they keep it, if they don't, the card goes back to the bag.\n",
            "alex: sounds really cool! i'm sure i'll try it. thank you so much for your help!!\n",
            "elizabeth: no problem, any time.\n",
            "### synopsis: elizabeth is packing the book for alex. alex is starting to teach the senior group next week. elizabeth gives alex some tips on how to prepare for the classes. elizabeth recommends the game called kaboom.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: karen: hi john, it's me, lost my phone\n",
            "karen: on my way home, don't worry. text me if you've got this. i'll have this phone only for couple of minutes.\n",
            "john: got it, go back home safely.\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: karen: hi john, it's me, lost my phone\n",
            "karen: on my way home, don't worry. text me if you've got this. i'll have this phone only for couple of minutes.\n",
            "john: got it, go back home safely.\n",
            "### synopsis: karen lost her phone. john will text her when he gets the phone.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: claire: there is a letter for tracy from the tax office\n",
            "tracy: thanks mum. can you open it?\n",
            "natalie: it's probably regarding tax return \n",
            "natalie: i got mine last week\n",
            "tracy: open and send me a photo\n",
            "tracy: better priv 🤑\n",
            "claire: no problem \n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: claire: there is a letter for tracy from the tax office\n",
            "tracy: thanks mum. can you open it?\n",
            "natalie: it's probably regarding tax return \n",
            "natalie: i got mine last week\n",
            "tracy: open and send me a photo\n",
            "tracy: better priv 🤑\n",
            "claire: no problem \n",
            "### synopsis: tracy has received a letter from the tax office.\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: donald: are you playing poker tonight? it's at dave's house\n",
            "steve: yeah i was planning to. do we need to bring whiskey?\n",
            "donald: i've got some, no worries.\n",
            "steve: ok see you\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: donald: are you playing poker tonight? it's at dave's house\n",
            "steve: yeah i was planning to. do we need to bring whiskey?\n",
            "donald: i've got some, no worries.\n",
            "steve: ok see you\n",
            "### synopsis: steve and donald are playing poker tonight at dave's house. steve will bring whiskey.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 100%|██████████| 1/1 [00:30<00:00, 30.34s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "/usr/local/lib/python3.10/dist-packages/ludwig/features/feature_utils.py:102: RuntimeWarning: divide by zero encountered in log\n",
            "  return np.sum(np.log(sequence_probabilities))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_predictions: pd.DataFrame = predictions_and_probabilities[0]"
      ],
      "metadata": {
        "id": "d9Ri3EUzM6D4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "bfe0848b-1660-404f-813b-02f682681d86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_predictions.columns"
      ],
      "metadata": {
        "id": "uaBlVTVRNElt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "5dffda97-ea15-4740-ddbc-161e608fdada"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['summary_predictions', 'summary_probabilities', 'summary_response',\n",
              "       'summary_probability'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\n\\n\")\n",
        "for dialogue_with_summary in zip(df_evaluation['dialogue'], df_predictions['summary_response']):\n",
        "  print(f\"Dialogue:\\n{dialogue_with_summary[0]}\")\n",
        "  print(f\"Generated Summary:\\n{dialogue_with_summary[1][0]}\")\n",
        "  print(\"\\n\\n\")"
      ],
      "metadata": {
        "id": "0dqwZyHLrkRb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eb621cb4-6485-4eec-f3b3-15fbef605f8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Adriana: Sorry, I guess I can't make it. I'm feeling sick again.\r\n",
            "Adriana: I planned so many things for today...\r\n",
            "Adriana: Hate it.\r\n",
            "Elisa: What a pity...\r\n",
            "Elisa: Next time!\r\n",
            "Elisa: Hope you'll get better soon!\r\n",
            "Adriana: Hope so too.\r\n",
            "Adriana: How was at the pool?\r\n",
            "Elisa: The best ❤️\r\n",
            "Elisa: 1 hour of swimming and 1 hour of sauna\r\n",
            "Elisa: Love it!\r\n",
            "Adriana:❤️\r\n",
            "Adriana: I'm such a bad swimmer that you couldn't stand me xD\r\n",
            "Elisa: Ahahaha\r\n",
            "Elisa: There was such a girl today, soooo annoying 😂\r\n",
            "Adriana: Just like me 😂😂😂\n",
            "Generated Summary:\n",
            "adriana is feeling sick again. elisa went to the pool and sauna.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Claire: Have you seen the news?\r\n",
            "Greg: No. What’s happened?\r\n",
            "Claire: That bitch my neighbor became an MP 🤮\r\n",
            "Kevin: Ooo, well done! You must be so jealous 🤣\r\n",
            "Claire: Stop it. I hate her so much. She killed my cat 🙈\r\n",
            "Greg: Whaaaaat???\r\n",
            "Claire: Really. It was 5 years ago. She hit it with a car.\r\n",
            "Greg: Okay. I understand 😒\n",
            "Generated Summary:\n",
            "claire's neighbour killed her cat 5 years ago. now she became an mp.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Josh: You're a nutter!!!\n",
            "Josh: I could never do things like that\n",
            "Paul: Wouldn't you ever do the bungee jumping or skydiving? C'mon that's not so scary :)\n",
            "Josh: hahahah\n",
            "Josh: Nope, I wouldn't do that :)\n",
            "Paul: The next time you're coming with me!\n",
            "Josh: No way, mate\n",
            "Generated Summary:\n",
            "paul wants to do bungee jumping or skydiving. josh won't do it.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Marcel: Hey\r\n",
            "Rodolfo: Hello\r\n",
            "Marcel: What's up?\r\n",
            "Rodolfo: Nothing just got back to the house\r\n",
            "Marcel: I had an exam today and I  think I will get a great grade\r\n",
            "Rodolfo: Good for you. Mine was pretty hard. Not what I was expecting. But I should pass it\r\n",
            "Marcel: Oh yeah now I remember you had an exam. Let me know if u have results\r\n",
            "Rodolfo: How nice nice of you to remember now\r\n",
            "Marcel: Haha i can't believe. I saw when one car hit another one. They were one behind another. It wasn't so much but still it was. The drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\r\n",
            "Rodolfo: I will only know in like 3 weeks\r\n",
            "Marcel: Oh long time\r\n",
            "Rodolfo: Yea rather long\r\n",
            "Marcel: Hopefully it will be fine\n",
            "Generated Summary:\n",
            "rodolfo had an exam today. marcel had an exam today. rodolfo and marcel will get good grades. rodolfo and marcel will know their results in 3 weeks.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Don: Do you see me online?\n",
            "Alfred: Nope\n",
            "Don: Ok, something's wrong, I'll have to check my settings\n",
            "Alfred: Ok\n",
            "Generated Summary:\n",
            "don is not online.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Travis: Hi, are you there?\r\n",
            "Lana: Hi Travis, good to hear from you after holiday.\r\n",
            "Travis: Yeah, long time\r\n",
            "Lana: So how was it? Greece, right?\r\n",
            "Travis: Yep, pure magic. 10 day there and 4 days in Albania\r\n",
            "Lana: Wow, Albania. Never been there.\r\n",
            "Travis: Kind of wild place, still. But I liked Greece better\r\n",
            "Lana: You stayed in one place?\r\n",
            "Travis: No, moved around. We saw almost everything there was to see\r\n",
            "Lana: Sounds exciting. \r\n",
            "Travis: Take a look. <file_photo>\r\n",
            "Lana: Wow, breathtaking. I guess you'll show us more when we meet in the office\r\n",
            "Travis: Sure thing. But I'm not back till Monday\r\n",
            "Lana: I see. Two more days of freedom.\r\n",
            "Travis: Precisely. How are things there anyway\r\n",
            "Lana: Same old, same old.\r\n",
            "Travis: Been worse?\r\n",
            "Lana: I guess it's just this time of the year when not much happens. \r\n",
            "Travis: Have you planned your holiday yet?\r\n",
            "Lana: Not really. I guess I'll just find something last minute in September.\r\n",
            "Travis: Greece perhaps\r\n",
            "Lana: I'll decide when I see the photos ha ha ha. See you around. Gotta go.\r\n",
            "Travis: Sure. See you on Monday\n",
            "Generated Summary:\n",
            "traves and lana met during their holiday in greece. lana will decide about her holiday when she sees travis's photos.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Alex: hey, I'm writing about the book :)\r\n",
            "Elizabeth: heey, thanks for the message, I'm packing it to my bag right now.\r\n",
            "Alex: thank you so much...you don't even know grateful I am\r\n",
            "Elizabeth: No problem, really. If I can help you in any other way just ask.\r\n",
            "Alex: Actually I have a few questions for you if you don't mind\r\n",
            "Elizabeth: Sure, what is it?\r\n",
            "Alex: I'm starting with the senior group next week and I'm so stressed out cause I never taught the adults...could you give me any clues how to prepare, what to do and so on?\r\n",
            "Elizabeth: Of course. First thing - relax, cause stress is destructive. I remember my first classes with the adults and I couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\r\n",
            "Alex: I get it...and how does your lesson usually look like?\r\n",
            "Elizabeth: It depends, you know. But for most of the time I'm just trying to stick to my fixed plan. At the beginning checking the attendance, asking them to give me the date in the foreing language so I can put it down in the register. Then a short conversation.\r\n",
            "Alex: and what after that?\r\n",
            "Elizabeth: Then I usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. I don't want them to do the exercices only, cause it's too automatic.\r\n",
            "Alex: sounds really cool...you are so experienced...I wish I was too :/\r\n",
            "Elizabeth: calm down, you'll just learn everything with practice. If you don't want to make your lessons too formal, you can look for some games. I recommend Kaboom.\r\n",
            "Alex: Kaboom? I don't know this game\r\n",
            "Elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. Every person takes out a single piece of paper and has to translate a word written on it. If they spot the answer, they keep it, if they don't, the card goes back to the bag.\r\n",
            "Alex: Sounds really cool! I'm sure I'll try it. Thank you so much for your help!!\r\n",
            "Elizabeth: No problem, any time.\n",
            "Generated Summary:\n",
            "elizabeth is packing the book for alex. alex is starting to teach the senior group next week. elizabeth gives alex some tips on how to prepare for the classes. elizabeth recommends the game called kaboom.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Karen: Hi John, it's me, lost my phone\r\n",
            "Karen: On my way home, don't worry. Text me if you've got this. I'll have this phone only for couple of minutes.\r\n",
            "John: Got it, go back home safely.\n",
            "Generated Summary:\n",
            "karen lost her phone. john will text her when he gets the phone.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Claire: There is a letter for Tracy from the tax office\r\n",
            "Tracy: Thanks mum. Can you open it?\r\n",
            "Natalie: It's probably regarding tax return \r\n",
            "Natalie: I got mine last week\r\n",
            "Tracy: Open and send me a photo\r\n",
            "Tracy: Better priv 🤑\r\n",
            "Claire: No problem \n",
            "Generated Summary:\n",
            "tracy has received a letter from the tax office.\n",
            "\n",
            "\n",
            "\n",
            "Dialogue:\n",
            "Donald: Are you playing poker tonight? It's at Dave's house\r\n",
            "Steve: Yeah I was planning to. Do we need to bring whiskey?\r\n",
            "Donald: I've got some, no worries.\r\n",
            "Steve: Ok see you\n",
            "Generated Summary:\n",
            "steve and donald are playing poker tonight at dave's house. steve will bring whiskey.\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_control_example: pd.DataFrame = pd.DataFrame(\n",
        "    data={\n",
        "      \"dialogue\": [samsum_test_dialogue,],\n",
        "    }\n",
        ")\n",
        "df_control_example"
      ],
      "metadata": {
        "id": "vLP6o23ggNFi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "aa2f98b2-faa4-4c24-ba7a-beb36635acfe"
      },
      "execution_count": null,
      "outputs": [
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            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n\"\\nA: Hi Tom, are you busy tomorrow\\u2019s afternoon?\\nB: I\\u2019m pretty sure I am. What\\u2019s up?\\nA: Can you go with me to the animal shelter?.\\nB: What do you want to do?\\nA: I want to get a puppy for my son.\\nB: That will make him so happy.\\nA: Yeah, we\\u2019ve discussed it many times. I think he\\u2019s ready now.\\nB: That\\u2019s good. Raising a dog is a tough issue. Like having a baby ;-)\\nA: I'll get him one of those little dogs.\\nB: One that won't grow up too big;-)\\nA: And eat too much;-))\\nB: Do you know which one he would like?\\nA: Oh, yes, I took him there last Monday. He showed me one that he really liked.\\nB: I bet you had to drag him away.\\nA: He wanted to take it home right away ;-).\\nB: I wonder what he'll name it.\\nA: He said he\\u2019d name it after his dead hamster \\u2013 Lemmy  - he's  a great Motorhead fan :-)))\\n\"]],\n        columns: [[\"number\", \"index\"], [\"string\", \"dialogue\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-e58b2f50-340e-47a4-a6c4-2d62c93b685a\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e58b2f50-340e-47a4-a6c4-2d62c93b685a')\"\n            title=\"Suggest charts.\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n    </g>\n</svg>\n  </button>\n  \n<style>\n  .colab-df-quickchart {\n      --bg-color: #E8F0FE;\n      --fill-color: #1967D2;\n      --hover-bg-color: #E2EBFA;\n      --hover-fill-color: #174EA6;\n      --disabled-fill-color: #AAA;\n      --disabled-bg-color: #DDD;\n  }\n\n  [theme=dark] .colab-df-quickchart {\n      --bg-color: #3B4455;\n      --fill-color: #D2E3FC;\n      --hover-bg-color: #434B5C;\n      --hover-fill-color: #FFFFFF;\n      --disabled-bg-color: #3B4455;\n      --disabled-fill-color: #666;\n  }\n\n  .colab-df-quickchart {\n    background-color: var(--bg-color);\n    border: none;\n    border-radius: 50%;\n    cursor: pointer;\n    display: none;\n    fill: var(--fill-color);\n    height: 32px;\n    padding: 0;\n    width: 32px;\n  }\n\n  .colab-df-quickchart:hover {\n    background-color: var(--hover-bg-color);\n    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n    fill: var(--button-hover-fill-color);\n  }\n\n  .colab-df-quickchart-complete:disabled,\n  .colab-df-quickchart-complete:disabled:hover {\n    background-color: var(--disabled-bg-color);\n    fill: var(--disabled-fill-color);\n    box-shadow: none;\n  }\n\n  .colab-df-spinner {\n    border: 2px solid var(--fill-color);\n    border-color: transparent;\n    border-bottom-color: var(--fill-color);\n    animation:\n      spin 1s steps(1) infinite;\n  }\n\n  @keyframes spin {\n    0% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n      border-left-color: var(--fill-color);\n    }\n    20% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    30% {\n      border-color: transparent;\n      border-left-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n      border-right-color: var(--fill-color);\n    }\n    40% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-top-color: var(--fill-color);\n    }\n    60% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n    }\n    80% {\n      border-color: transparent;\n      border-right-color: var(--fill-color);\n      border-bottom-color: var(--fill-color);\n    }\n    90% {\n      border-color: transparent;\n      border-bottom-color: var(--fill-color);\n    }\n  }\n</style>\n\n  <script>\n    async function quickchart(key) {\n      const quickchartButtonEl =\n        document.querySelector('#' + key + ' button');\n      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n      quickchartButtonEl.classList.add('colab-df-spinner');\n      try {\n        const charts = await google.colab.kernel.invokeFunction(\n            'suggestCharts', [key], {});\n      } catch (error) {\n        console.error('Error during call to suggestCharts:', error);\n      }\n      quickchartButtonEl.classList.remove('colab-df-spinner');\n      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n    }\n    (() => {\n      let quickchartButtonEl =\n        document.querySelector('#df-e58b2f50-340e-47a4-a6c4-2d62c93b685a button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 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          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions: list[list[str]] = predict(model=model, df_test=df_control_example)"
      ],
      "metadata": {
        "id": "IWq96fOff6E0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "578f5ae4-8838-41f0-bd6f-c135b8535848"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<IPython.core.display.HTML object>"
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            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:ludwig.data.preprocessing:Specified split column split for fixed split strategy was not found in dataset.\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n",
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n",
            "/usr/local/lib/python3.10/dist-packages/ludwig/data/preprocessing.py:1315: UserWarning: column: 'split' is required by the dataset splitter with params: {'type': 'fixed', 'column': 'split'}, but 'split' is not present in the `dataset_df` with columns: Index(['dialogue'], dtype='object'). This is acceptable during serving setting where dataset splitting is irrelevant. You may see this warning if, for example, the model was trained with a configuration that used a stratified split on the target column, but for live predictions, a value for the target column is not to be provided.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\rPrediction:   0%|          | 0/1 [00:00<?, ?it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:ludwig.models.llm:For generating text, using: GenerationConfig {\n",
            "  \"do_sample\": true,\n",
            "  \"max_length\": 32,\n",
            "  \"max_new_tokens\": 512,\n",
            "  \"pad_token_id\": 2,\n",
            "  \"temperature\": 0.1\n",
            "}\n",
            "\n",
            "INFO:ludwig.models.llm:Decoded text inputs for the first example in batch: summarize this dialog:\n",
            "### dialogue: \n",
            "a: hi tom, are you busy tomorrow’s afternoon?\n",
            "b: i’m pretty sure i am. what’s up?\n",
            "a: can you go with me to the animal shelter?.\n",
            "b: what do you want to do?\n",
            "a: i want to get a puppy for my son.\n",
            "b: that will make him so happy.\n",
            "a: yeah, we’ve discussed it many times. i think he’s ready now.\n",
            "b: that’s good. raising a dog is a tough issue. like having a baby ;-)\n",
            "a: i'll get him one of those little dogs.\n",
            "b: one that won't grow up too big;-)\n",
            "a: and eat too much;-))\n",
            "b: do you know which one he would like?\n",
            "a: oh, yes, i took him there last monday. he showed me one that he really liked.\n",
            "b: i bet you had to drag him away.\n",
            "a: he wanted to take it home right away ;-).\n",
            "b: i wonder what he'll name it.\n",
            "a: he said he’d name it after his dead hamster – lemmy  - he's  a great motorhead fan :-)))\n",
            "\n",
            "### synopsis:\n",
            "INFO:ludwig.models.llm:Decoded generated output for the first example in batch: summarize this dialog:\n",
            "### dialogue: \n",
            "a: hi tom, are you busy tomorrow’s afternoon?\n",
            "b: i’m pretty sure i am. what’s up?\n",
            "a: can you go with me to the animal shelter?.\n",
            "b: what do you want to do?\n",
            "a: i want to get a puppy for my son.\n",
            "b: that will make him so happy.\n",
            "a: yeah, we’ve discussed it many times. i think he’s ready now.\n",
            "b: that’s good. raising a dog is a tough issue. like having a baby ;-)\n",
            "a: i'll get him one of those little dogs.\n",
            "b: one that won't grow up too big;-)\n",
            "a: and eat too much;-))\n",
            "b: do you know which one he would like?\n",
            "a: oh, yes, i took him there last monday. he showed me one that he really liked.\n",
            "b: i bet you had to drag him away.\n",
            "a: he wanted to take it home right away ;-).\n",
            "b: i wonder what he'll name it.\n",
            "a: he said he’d name it after his dead hamster – lemmy  - he's  a great motorhead fan :-)))\n",
            "\n",
            "### synopsis: a wants to get a puppy for his son. b will go with him to the animal shelter tomorrow afternoon.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: 100%|██████████| 1/1 [00:03<00:00,  3.24s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "INFO:ludwig.utils.tokenizers:Loaded HuggingFace implementation of alexsherstinsky/Mistral-7B-v0.1-sharded tokenizer\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
            "WARNING:ludwig.utils.tokenizers:No padding token id found. Using eos_token as pad_token.\n",
            "/usr/local/lib/python3.10/dist-packages/ludwig/features/feature_utils.py:102: RuntimeWarning: divide by zero encountered in log\n",
            "  return np.sum(np.log(sequence_probabilities))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "one_prediction: str = predictions[0][0]\n",
        "print(f'\\n[GENERATED_TEXT] PREDICTION:\\n{one_prediction}')"
      ],
      "metadata": {
        "id": "b6zapjrAz-d_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "2dbcf753-ba25-413c-ad9d-ac3af575641a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[GENERATED_TEXT] PREDICTION:\n",
            "a wants to get a puppy for his son. b will go with him to the animal shelter tomorrow afternoon.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Upload Trained Model Artifacts To HuggingFace** 🤗\n",
        "\n",
        "Now that we have a fine-tuned model, we can export the model weights to HuggingFace hub so we can use them in downstream tasks or in production. Ludwig supports uploading model weights directly to HuggingFace Hub via the `upload` Ludwig command.\n",
        "\n",
        "```\n",
        "!ludwig upload hf_hub --repo_id <hf_user_name>/<repo_name> --model_path <top_level_model_directory>\n",
        "```\n",
        "\n",
        "The `model-path` can be seen at the end of training/fine-tuning, as seen in this screenshot.\n",
        "\n",
        "<br>\n",
        "\n",
        "![Screenshot 2023-08-15 at 2.20.00 PM.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "LWxotc5kGcBD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ludwig upload hf_hub --repo_id \"alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-T4-sharded-4bit-notmerged\" --model_path /content/results/api_experiment_run"
      ],
      "metadata": {
        "id": "-Y71BStbCQ1A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "65f1b345-bced-425f-ad86-d8d3f7e964ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "adapter_model.bin: 100% 13.7M/13.7M [00:00<00:00, 22.2MB/s]\n",
            "Model uploaded to `https://huggingface.co/alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-T4-sharded-4bit-notmerged/tree/main/` with repository name `alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-T4-sharded-4bit-notmerged`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PfJjc-ZW74S7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8nUWrubnpANI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_fine_tuned_model_name: str = \"alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-T4-sharded-4bit-notmerged\""
      ],
      "metadata": {
        "id": "SCH8OyED_ZEY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "1720b787-b1c1-418b-937b-1942044a9628"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fubNdP6Yqpdw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TwVmKFKojLSu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mXF8mn9QpAf3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "v1mjIXRrEIcs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "id": "4YT5UXXvEH9-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c834e691-d9ef-48c0-da49-c16845a04563"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YVxHFguz68Ib"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Dw0BnWVW677N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load the finetuned LoRA layers for Mistral 7B LLM (adapter weights for SAMSum dataset)."
      ],
      "metadata": {
        "id": "E1MXUMpdYN2n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[LOADING_MODEL] FINE_TUNED_AND_NOT_MERGED:\\n{samsum_fine_tuned_model_name}')\n",
        "samsum_tokenizer = base_model_tokenizer\n",
        "samsum_model: PeftModelForCausalLM = PeftModel.from_pretrained(model=base_model, model_id=samsum_fine_tuned_model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "id": "bJY75vo7XOwA",
        "outputId": "2b7909a9-32c7-4477-a8dc-478140a48cc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[LOADING_MODEL] FINE_TUNED_AND_NOT_MERGED:\n",
            "alexsherstinsky/mistralai-7B-v01-based-finetuned-using-ludwig-with-samsum-T4-sharded-4bit-notmerged\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7IN-YAqjOVFz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Vl6plG8pOVKf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "id": "iPoQC0z0OVO7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0c2d8b26-c117-4014-8582-910e2045aa10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1567"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KRmyHTkp-a7E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Eu9o1PFE-Vyo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run inference on finetuned Mistral 7B LLM for SAMSum dataset to verify quality."
      ],
      "metadata": {
        "id": "JCnrqPZPXhG2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_sequences_generator: TextGenerationPipeline = transformers.pipeline(\n",
        "    task=\"text-generation\",\n",
        "    tokenizer=samsum_tokenizer,\n",
        "    model=samsum_model,\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\",\n",
        ")"
      ],
      "metadata": {
        "id": "ce4yrJVCY7_U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "outputId": "be08424a-dbef-4c1b-b048-8cf517080906"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The model 'PeftModelForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'LlamaForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'FalconForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MistralForCausalLM', 'MptForCausalLM', 'MusicgenForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PersimmonForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7Vq6v-xxC03q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_sequences: list[dict] | list[list[dict]] = samsum_sequences_generator(\n",
        "    text_inputs=samsum_test_prompt,\n",
        "    do_sample=True,\n",
        "    top_k=50,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=samsum_tokenizer.eos_token_id,\n",
        "    max_length=512,\n",
        "    return_text=True,\n",
        ")"
      ],
      "metadata": {
        "id": "gw2ahnKNZ9BR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a77f8d0f-aaf1-42d9-cee7-58f47f9ae23d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_sequence: dict = samsum_sequences[0]"
      ],
      "metadata": {
        "id": "OHp1QhUZY8CL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "448e9413-403f-455c-fa76-8a240409f1f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[GENERATED_TEXT] FINE_TUNED_MODEL_PREDICTION:\\n{samsum_sequence[\"generated_text\"]} ; TYPE: {str(type(samsum_sequence[\"generated_text\"]))}')"
      ],
      "metadata": {
        "id": "DNIJBIBVY8FU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "outputId": "9e3427e5-2a71-4457-ffba-6f6ccfe5a0bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "[GENERATED_TEXT] FINE_TUNED_MODEL_PREDICTION:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: \n",
            "A: Hi Tom, are you busy tomorrow’s afternoon?\n",
            "B: I’m pretty sure I am. What’s up?\n",
            "A: Can you go with me to the animal shelter?.\n",
            "B: What do you want to do?\n",
            "A: I want to get a puppy for my son.\n",
            "B: That will make him so happy.\n",
            "A: Yeah, we’ve discussed it many times. I think he’s ready now.\n",
            "B: That’s good. Raising a dog is a tough issue. Like having a baby ;-)\n",
            "A: I'll get him one of those little dogs.\n",
            "B: One that won't grow up too big;-)\n",
            "A: And eat too much;-))\n",
            "B: Do you know which one he would like?\n",
            "A: Oh, yes, I took him there last Monday. He showed me one that he really liked.\n",
            "B: I bet you had to drag him away.\n",
            "A: He wanted to take it home right away ;-).\n",
            "B: I wonder what he'll name it.\n",
            "A: He said he’d name it after his dead hamster – Lemmy  - he's  a great Motorhead fan :-)))\n",
            "\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "A wants to buy a puppy for his son. The puppy that he will get was pointed out to A by his son a week prior. B will go with A to the animal shelter. ; TYPE: <class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "MhjUYgzLit5T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "nN1ztfu6D9yW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gc.collect()"
      ],
      "metadata": {
        "id": "GmlEWKz1D9yW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "571b1a9a-49a6-4589-a492-859e51c25950"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2IlJMGIHa8Gl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r7NRj-eFa75l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Run inference on finetuned Mistral 7B LLM for SAMSum dataset over the evaluation dataset."
      ],
      "metadata": {
        "id": "AfeIOOkha9Iu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_samsum_evaluation: pd.DataFrame = df_evaluation.copy()"
      ],
      "metadata": {
        "id": "m6MoKuHxCk4c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "f2b41deb-5e2f-480b-eb3d-bf2dea608709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_samsum_evaluation[\"prompt\"] = df_samsum_evaluation[\"dialogue\"].apply(lambda x: samsum_prompt_template.format(**{\"dialogue\": x}))"
      ],
      "metadata": {
        "id": "vT-Z_9pdDFgl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "925d3593-8ec0-4ece-828f-d94e02ba3af0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "samsum_sequences: list[dict] | list[list[dict]] = samsum_sequences_generator(\n",
        "    text_inputs=df_samsum_evaluation[\"prompt\"].to_list(),\n",
        "    do_sample=True,\n",
        "    top_k=50,\n",
        "    num_return_sequences=1,\n",
        "    eos_token_id=samsum_tokenizer.eos_token_id,\n",
        "    max_length=512,\n",
        "    return_text=True,\n",
        ")"
      ],
      "metadata": {
        "id": "fW2wlr4dDfHN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "outputId": "1575a776-c8cd-4a1b-98c0-60db92455687"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "  <style>\n",
              "    pre {\n",
              "        white-space: pre-wrap;\n",
              "    }\n",
              "  </style>\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1268: UserWarning: Input length of input_ids is 571, but `max_length` is set to 512. This can lead to unexpected behavior. You should consider increasing `max_new_tokens`.\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
            "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'\\n[FINE_TUNED_MODEL_EVALUATION_BEGIN]')\n",
        "idx: int = 0\n",
        "print(f'\\n[=============EXAMPLE_{idx}_BEGIN=============]')\n",
        "for dialogue, synopsis in zip(df_samsum_evaluation[\"dialogue\"].to_list(), samsum_sequences):\n",
        "  print(f'\\n[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\\n{synopsis[0][\"generated_text\"]}')\n",
        "  print(f'\\n[=============EXAMPLE_{idx}_END=============]')\n",
        "  idx += 1\n",
        "\n",
        "print(f'\\n[FINE_TUNED_MODEL_EVALUATION_END]')"
      ],
      "metadata": {
        "id": "W1e56lBUaW7I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a2a3498b-711d-4c6a-bff4-ddc2f0ae452d"
      },
      "execution_count": null,
      "outputs": [
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              "<IPython.core.display.HTML object>"
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          "text": [
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION_BEGIN]\n",
            "\n",
            "[=============EXAMPLE_0_BEGIN=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Adriana: Sorry, I guess I can't make it. I'm feeling sick again.\r\n",
            "Adriana: I planned so many things for today...\r\n",
            "Adriana: Hate it.\r\n",
            "Elisa: What a pity...\r\n",
            "Elisa: Next time!\r\n",
            "Elisa: Hope you'll get better soon!\r\n",
            "Adriana: Hope so too.\r\n",
            "Adriana: How was at the pool?\r\n",
            "Elisa: The best ❤️\r\n",
            "Elisa: 1 hour of swimming and 1 hour of sauna\r\n",
            "Elisa: Love it!\r\n",
            "Adriana:❤️\r\n",
            "Adriana: I'm such a bad swimmer that you couldn't stand me xD\r\n",
            "Elisa: Ahahaha\r\n",
            "Elisa: There was such a girl today, soooo annoying 😂\r\n",
            "Adriana: Just like me 😂😂😂\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Because Adriana feels sick today, she can't go to the pool with Elisa. They'll meet another time.\n",
            "\n",
            "[=============EXAMPLE_0_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Claire: Have you seen the news?\r\n",
            "Greg: No. What’s happened?\r\n",
            "Claire: That bitch my neighbor became an MP 🤮\r\n",
            "Kevin: Ooo, well done! You must be so jealous 🤣\r\n",
            "Claire: Stop it. I hate her so much. She killed my cat 🙈\r\n",
            "Greg: Whaaaaat???\r\n",
            "Claire: Really. It was 5 years ago. She hit it with a car.\r\n",
            "Greg: Okay. I understand 😒\n",
            "\n",
            "### Synopsis:\n",
            "* Claire's neighbor killed Claire's cat.\n",
            "\n",
            "[=============EXAMPLE_1_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Josh: You're a nutter!!!\n",
            "Josh: I could never do things like that\n",
            "Paul: Wouldn't you ever do the bungee jumping or skydiving? C'mon that's not so scary :)\n",
            "Josh: hahahah\n",
            "Josh: Nope, I wouldn't do that :)\n",
            "Paul: The next time you're coming with me!\n",
            "Josh: No way, mate\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Paul and Josh have different opinions about bungee jumping and skydiving.\n",
            "\n",
            "[=============EXAMPLE_2_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Marcel: Hey\r\n",
            "Rodolfo: Hello\r\n",
            "Marcel: What's up?\r\n",
            "Rodolfo: Nothing just got back to the house\r\n",
            "Marcel: I had an exam today and I  think I will get a great grade\r\n",
            "Rodolfo: Good for you. Mine was pretty hard. Not what I was expecting. But I should pass it\r\n",
            "Marcel: Oh yeah now I remember you had an exam. Let me know if u have results\r\n",
            "Rodolfo: How nice nice of you to remember now\r\n",
            "Marcel: Haha i can't believe. I saw when one car hit another one. They were one behind another. It wasn't so much but still it was. The drivers went out, spoke  maybe 15 seconds , smiled and came back to their cars hahha and that's all\r\n",
            "Rodolfo: I will only know in like 3 weeks\r\n",
            "Marcel: Oh long time\r\n",
            "Rodolfo: Yea rather long\r\n",
            "Marcel: Hopefully it will be fine\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            " rodolfo was thinking about an exam and his grade. they both will know the results in 3 weeks.\n",
            "\n",
            "[=============EXAMPLE_3_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Don: Do you see me online?\n",
            "Alfred: Nope\n",
            "Don: Ok, something's wrong, I'll have to check my settings\n",
            "Alfred: Ok\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Alfred can't see Don online.\n",
            "\n",
            "[=============EXAMPLE_4_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Travis: Hi, are you there?\r\n",
            "Lana: Hi Travis, good to hear from you after holiday.\r\n",
            "Travis: Yeah, long time\r\n",
            "Lana: So how was it? Greece, right?\r\n",
            "Travis: Yep, pure magic. 10 day there and 4 days in Albania\r\n",
            "Lana: Wow, Albania. Never been there.\r\n",
            "Travis: Kind of wild place, still. But I liked Greece better\r\n",
            "Lana: You stayed in one place?\r\n",
            "Travis: No, moved around. We saw almost everything there was to see\r\n",
            "Lana: Sounds exciting. \r\n",
            "Travis: Take a look. <file_photo>\r\n",
            "Lana: Wow, breathtaking. I guess you'll show us more when we meet in the office\r\n",
            "Travis: Sure thing. But I'm not back till Monday\r\n",
            "Lana: I see. Two more days of freedom.\r\n",
            "Travis: Precisely. How are things there anyway\r\n",
            "Lana: Same old, same old.\r\n",
            "Travis: Been worse?\r\n",
            "Lana: I guess it's just this time of the year when not much happens. \r\n",
            "Travis: Have you planned your holiday yet?\r\n",
            "Lana: Not really. I guess I'll just find something last minute in September.\r\n",
            "Travis: Greece perhaps\r\n",
            "Lana: I'll decide when I see the photos ha ha ha. See you around. Gotta go.\r\n",
            "Travis: Sure. See you on Monday\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Travis just returned from his holiday to Greece where he also visited Albania. He went around and saw almost everything. Lana still doesn't know what she is going to decide for her holiday.\n",
            "\n",
            "[=============EXAMPLE_5_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Alex: hey, I'm writing about the book :)\r\n",
            "Elizabeth: heey, thanks for the message, I'm packing it to my bag right now.\r\n",
            "Alex: thank you so much...you don't even know grateful I am\r\n",
            "Elizabeth: No problem, really. If I can help you in any other way just ask.\r\n",
            "Alex: Actually I have a few questions for you if you don't mind\r\n",
            "Elizabeth: Sure, what is it?\r\n",
            "Alex: I'm starting with the senior group next week and I'm so stressed out cause I never taught the adults...could you give me any clues how to prepare, what to do and so on?\r\n",
            "Elizabeth: Of course. First thing - relax, cause stress is destructive. I remember my first classes with the adults and I couldn't even breath, but the point is to devise you own work schedule, and in order to do it right you just gotta observe them. their knowledge, their needs and so on.\r\n",
            "Alex: I get it...and how does your lesson usually look like?\r\n",
            "Elizabeth: It depends, you know. But for most of the time I'm just trying to stick to my fixed plan. At the beginning checking the attendance, asking them to give me the date in the foreing language so I can put it down in the register. Then a short conversation.\r\n",
            "Alex: and what after that?\r\n",
            "Elizabeth: Then I usually start a new chapter, the vocabulary, sometimes grammar, a few exercices and then work in groups. I don't want them to do the exercices only, cause it's too automatic.\r\n",
            "Alex: sounds really cool...you are so experienced...I wish I was too :/\r\n",
            "Elizabeth: calm down, you'll just learn everything with practice. If you don't want to make your lessons too formal, you can look for some games. I recommend Kaboom.\r\n",
            "Alex: Kaboom? I don't know this game\r\n",
            "Elizabeth: you just write the words on little pieces of paper, for example names of fruit or vegetables and put it all in a little bag. Every person takes out a single piece of paper and has to translate a word written on it. If they spot the answer, they keep it, if they don't, the card goes back to the bag.\r\n",
            "Alex: Sounds really cool! I'm sure I'll try it. Thank you so much for your help!!\r\n",
            "Elizabeth: No problem, any time.\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "\n",
            "\n",
            "[=============EXAMPLE_6_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Karen: Hi John, it's me, lost my phone\r\n",
            "Karen: On my way home, don't worry. Text me if you've got this. I'll have this phone only for couple of minutes.\r\n",
            "John: Got it, go back home safely.\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Karen should have her phone very soon. She's on her way home and John's got her phone right now.\n",
            "\n",
            "[=============EXAMPLE_7_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Claire: There is a letter for Tracy from the tax office\r\n",
            "Tracy: Thanks mum. Can you open it?\r\n",
            "Natalie: It's probably regarding tax return \r\n",
            "Natalie: I got mine last week\r\n",
            "Tracy: Open and send me a photo\r\n",
            "Tracy: Better priv 🤑\r\n",
            "Claire: No problem \n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "there is a tax office letter for Tracy.\n",
            "\n",
            "[=============EXAMPLE_8_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION] GENERATED_SUMMARY:\n",
            "\n",
            "Summarize this dialog:\n",
            "\n",
            "### Dialogue: Donald: Are you playing poker tonight? It's at Dave's house\r\n",
            "Steve: Yeah I was planning to. Do we need to bring whiskey?\r\n",
            "Donald: I've got some, no worries.\r\n",
            "Steve: Ok see you\n",
            "\n",
            "### Synopsis:\n",
            "\n",
            "Donald and Steve are having poker tonight at Dave's house. Donald has got some whiskey for both of them.\n",
            "\n",
            "[=============EXAMPLE_9_END=============]\n",
            "\n",
            "[FINE_TUNED_MODEL_EVALUATION_END]\n"
          ]
        }
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}