1_open_source_toolkit.ipynb

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  "cells": [
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      "source": [
        "#  🧑‍💻 The open-source NLP toolkit 🧑‍💻\n",
        "\n",
        "📅 _Data Science Summer School 2023, 22.08.2023_\n",
        "\n",
        "👨‍🏫 By [Moritz Laurer](https://www.linkedin.com/in/moritz-laurer/).\n",
        "For questions, reach out to: m.laurer@vu.nl\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/MoritzLaurer/summer-school-transformers-2023/blob/main/1_open_source_toolkit.ipynb\">\n",
        "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
        "</a>"
      ],
      "metadata": {
        "id": "4UCk8A9sEo-M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Section structure**\n",
        "1. The open-source ecosystem: increasing accessibility to machine learning (ML) software and hardware\n",
        "2. Some simple code demonstrations\n",
        "3. Q&A"
      ],
      "metadata": {
        "id": "r-UgA6tdOuJF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Your profiles at the Hertie Summer School:**\n",
        "* Overall cohort: ~36% have prior coding experience; ~39% have \"a little bit\" of prior coding experience; ~25% have no prior coding experience.\n",
        "* Some know Python, some R, some both. A minority knows one of: C++, JavaScript, Java, Matlab, Stata, SPSS.\n",
        "* This workshop tries to be useful to both beginners and more experienced participants.\n",
        "    * For **beginners**: You probably will not understand every line of code in the notebooks, but that's fine. The main learning objective for you is to get an overall feeling that you can do a lot in a few lines of code and you don't need a PhD for deep learning today. Start by simply copy-pasting my code. There are also sections that do not require any coding (e.g. data-centric AI).\n",
        "    * For more **experienced participants**: Keep in mind that others might not know as much as you do and everyone is here to learn. Things will get in depth later on.\n",
        "\n",
        "=> The overall objective is for everyone to get excited about learning more on their own. The best way of learning is getting excited and intrinsically motivated to learn more.\n",
        "\n"
      ],
      "metadata": {
        "id": "E8mmPl2Yix4L"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2G9W5iwfnYZg"
      },
      "source": [
        "## 1. The open-source ML ecosystem, or the democratisation of ML software & hardware"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2LXSiPj94_7"
      },
      "source": [
        "### 1.1. What does the ML open-source ecosystem look like in 2023?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2X_hAXtG52mI"
      },
      "source": [
        "A key driver for progress in AI is the open-source community. Prominent example: [Hugging Face](https://huggingface.co) is the main platform for sharing ML models.\n",
        "* Their [Transformers library](https://huggingface.co/transformers/) provides easy-to-use code for using state-of-the-art (SOTA) transformer models.\n",
        "* Their [model hub](https://huggingface.co/models) provides 300.000~ models trained by NLP researchers - from universities, small companies or large companies like Microsoft, Facebook, Google.\n",
        "* Their [Datasets library](https://huggingface.co/datasets) provides 55.000~ datasets.\n",
        "\n",
        "=> They are the de-facto open-source standard for sharing and using transformer language models. (Besides PyTorch and TensorFlow for more advanced users)\n",
        "\n",
        "=> Let's look at the website (model hub, tasks & model cards): https://huggingface.co/"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1.2. How do people access ML hardware in 2023?"
      ],
      "metadata": {
        "id": "IUVLfhRPT8uX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Running and training Transformers requires specialised hardware, so called GPUs (Graphics Processing Units). They were originally created for expensive graphics calculations in PC gaming. Now they are also specialised in the calculations necessary for running large AI models.\n",
        "\n",
        "Several providers for cheap access to GPUs exist. A prominent example is Google Colaboratory (\"Colab\"), a programming environment which enables you to run code in the browser. The main advantages are:\n",
        "* No setup on your local machine is required. Everything runs in the cloud.\n",
        "* Free access to GPUs\n",
        "* Easy sharing of code and text. See an introduction [here](https://colab.research.google.com/notebooks/intro.ipynb?utm_source=scs-index).\n",
        "\n",
        "Colab is based on [Jupyter Notebooks](https://jupyter.org/) and has two main types of cells:\n",
        "* code cells\n",
        "* text cells"
      ],
      "metadata": {
        "id": "BA54E3j1UNFj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "1+1"
      ],
      "metadata": {
        "id": "XTjFNeazPEjA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6e296ea-84bb-4b1a-c439-2999b9e9cbe4"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Ease-of-use: Using Transformers in 3 lines of code"
      ],
      "metadata": {
        "id": "-AlHgXZCVNr0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**Overview of different tasks that can be automated with ML**\n",
        "* Key ingredients: (1) a model trained on a specific task; (2) input data (e.g. texts or images); (3) output produced by the model.\n",
        "* Transformers are currently the most popular type of deep learning algorithm. Most tasks below are solved with Transformers. There might be other types of algorithms coming up in the medium term.\n",
        "\n"
      ],
      "metadata": {
        "id": "LpOoqdeqbCI-"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QP60sINRSprG"
      },
      "source": [
        "**Install the Transformers library & dependencies**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBMBmzH0StsF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "960151d8-f350-449e-9d92-eb4e6337de91"
      },
      "source": [
        "!pip install transformers~=4.31.0  # The Transformers library from Hugging Face\n",
        "!pip install sentencepiece==0.1.96  # optional tokeniser, required for some models. e.g. machine translation\n",
        "!pip install wikipedia==1.4.0  # to download any text from wikipedia\n",
        "# running large models with accelerate https://huggingface.co/blog/accelerate-large-models\n",
        "# NOTE: we need to restart the runtime after installing accelerate\n",
        "!pip install accelerate~=0.21.0"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers~=4.31.0\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers~=4.31.0)\n",
            "  Downloading safetensors-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.2 tokenizers-0.13.3 transformers-4.31.0\n",
            "Collecting sentencepiece==0.1.96\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.96\n",
            "Collecting wikipedia==1.4.0\n",
            "  Downloading wikipedia-1.4.0.tar.gz (27 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from wikipedia==1.4.0) (4.11.2)\n",
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            "Building wheels for collected packages: wikipedia\n",
            "  Building wheel for wikipedia (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wikipedia: filename=wikipedia-1.4.0-py3-none-any.whl size=11680 sha256=aa5eaa23298d1c19e7c9c5434b65ef865ed342f7f2ea537608e3fa60d40c18c9\n",
            "  Stored in directory: /root/.cache/pip/wheels/5e/b6/c5/93f3dec388ae76edc830cb42901bb0232504dfc0df02fc50de\n",
            "Successfully built wikipedia\n",
            "Installing collected packages: wikipedia\n",
            "Successfully installed wikipedia-1.4.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k2TbwfxTVCjP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d947863-4831-42f5-9028-3dc0bc6f2916"
      },
      "source": [
        "# automatically chose CPU or GPU for inference, depending on your hardware\n",
        "#import torch\n",
        "#device_id = torch.cuda.current_device() if torch.cuda.is_available() else -1\n",
        "# -1 == CPU ; 0 == GPU\n",
        "#print(device_id)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5FYbEIBEzEu"
      },
      "source": [
        "**The Hugging Face Pipeline**\n",
        "* Makes automation of many NLP tasks possible in 3 lines of code\n",
        "* Detailed documentation is available [here](https://huggingface.co/transformers/main_classes/pipelines.html)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0U1iHK0ETBXf"
      },
      "source": [
        "from transformers import pipeline\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from pprint import pprint"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKt3KNCBQ2fe"
      },
      "source": [
        "### 2.1 Many models tailored to specific tasks\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbZWdV3LWP2l"
      },
      "source": [
        "#### 2.1.1 Text classification"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's search for a few popular text classification models in the [HF model hub](https://huggingface.co/models?pipeline_tag=text-classification&sort=downloads)."
      ],
      "metadata": {
        "id": "390ar7USTP30"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_classification = pipeline(\"text-classification\", model=\"cardiffnlp/twitter-roberta-base-irony\")  # cardiffnlp/twitter-roberta-base-irony, SamLowe/roberta-base-go_emotions"
      ],
      "metadata": {
        "id": "cCf4vsXrbczm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
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        "outputId": "57cf0922-664b-4c97-98d7-d0670299a9a4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Well that workshop was totally worth my time...\"  # \"Well that workshop was totally worth my time...\"  \"This smells weird, I'm not sure if I should eat this ... Yikes, it tasted like old socks!\"\n",
        "output = pipeline_classification(text, top_k=10)\n",
        "print(output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uhf7MYboRdEy",
        "outputId": "50c58e9b-63a0-41e1-93f9-490ebc249306"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'irony', 'score': 0.9424387812614441}, {'label': 'non_irony', 'score': 0.057561274617910385}]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# make output a bit cleaner\n",
        "df_output = pd.DataFrame(output)\n",
        "print(df_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zwtj5Lm8cdh8",
        "outputId": "b7d92ba0-4559-411a-a6a6-53ab58570a45"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       label     score\n",
            "0      irony  0.942439\n",
            "1  non_irony  0.057561\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HIOYqJZ9kOtk"
      },
      "source": [
        "#### 2.1.2 Machine Translation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vk69-EpwvlbH"
      },
      "source": [
        "* Open source machine translation (MT) models enable you to translate between many different languages without Google Translate.\n",
        "* [University of Helsinki](https://huggingface.co/Helsinki-NLP) uploaded models for more than 1000 language pairs to the Hugging Face hub\n",
        "* [Facebook AI](https://huggingface.co/models?search=facebook+m2m) open-sourced several multi-lingual models\n",
        "* The [EasyNMT library](https://github.com/UKPLab/EasyNMT), provides an easy wrapper for all these models\n",
        "* Most machine translation models translate between two languages in one direction (e.g. German to English, but not English to German), some can translate in multiple directions.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKtdnG1FkRQt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
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            "ef296c595f254ad0829cfe1bb6e3236b",
            "562dec60b33e44a682af4a3f1573c97c",
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          ]
        },
        "outputId": "6bde9a22-0683-447c-f614-10ef5e298c91"
      },
      "source": [
        "# translation pipeline docs: https://huggingface.co/transformers/main_classes/pipelines.html#transformers.TranslationPipeline\n",
        "pipeline_translate = pipeline(\"translation\", model=\"facebook/m2m100_418M\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/908 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2f5c8e94dfe44bcebb75a1197b3a9bfa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.94G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6aebd2f267814a0faecf37c1617ceaeb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/233 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d38e6ddb21914797ba6cac510b8a229b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/272 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1151ea3a329493588a553e7e03a4a02"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/3.71M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "afee0c08cf544df6ba138383a0ff5e49"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)tencepiece.bpe.model:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0f64bb328124b4e9f5e07b61063ea3e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/1.14k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ef296c595f254ad0829cfe1bb6e3236b"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7pra41KlFwf",
        "outputId": "d5513401-2879-42ca-d2c7-31d006e16ffe"
      },
      "source": [
        "text = \"Ich bin ein Fisch\"\n",
        "pipeline_translate(text, src_lang=\"de\", tgt_lang=\"en\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'translation_text': 'I am a fish'}]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v7w6BRcOsjt7",
        "outputId": "9d4e075b-41b4-4032-af44-b980705b3156"
      },
      "source": [
        "# download any text from wikipedia, via  https://pypi.org/project/wikipedia/\n",
        "import wikipedia\n",
        "wikipedia.set_lang(\"de\")\n",
        "\n",
        "text = wikipedia.summary(\"Donald Trump\").replace('\\n', ' ')[:318]\n",
        "print(f\"Original text:\\n{text}\\n\")\n",
        "\n",
        "# translate the text from wikipedia\n",
        "text_translated = pipeline_translate(text, src_lang=\"de\", tgt_lang=\"en\")\n",
        "print(f\"Translated text:\\n{text_translated[0]['translation_text']}\")\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original text:\n",
            "Donald John Trump [ˈdɑn.əld dʒɑn tɹɐmp] (* 14. Juni 1946 in Queens, New York City, New York) ist ein US-amerikanischer Unternehmer, Entertainer und Politiker der Republikanischen Partei, der von 2017 bis 2021 der 45. Präsident der Vereinigten Staaten war. Der Rechtspopulist gilt als einer der umstrittensten Politiker\n",
            "\n",
            "Translated text:\n",
            "Donald John Trump [ˈdɑn.əld dʒɑn tɔmp] (born 14 June 1946 in Queens, New York City, New York) is an American entrepreneur, entertainer and politician of the Republican Party, who from 2017 to 2021 was the 45th President of the United States.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YK9-BIv-WdyR"
      },
      "source": [
        "#### 2.1.3 Text Summarization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IuzEFpiQ2JuS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
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            "472fc429313447fbba698700dc0a5f5f"
          ]
        },
        "outputId": "3876203c-5200-4732-bbbd-d56591fc1c4c"
      },
      "source": [
        "# docs for summarisation pipeline: https://huggingface.co/transformers/main_classes/pipelines.html#summarizationpipeline\n",
        "pipeline_summarize = pipeline(\"summarization\", model=\"sshleifer/distilbart-cnn-12-6\")  # sshleifer/distilbart-cnn-12-6 , google/pegasus-cnn_dailymail"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.80k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "068e468f6e0f4d5c98b9c414ce729aee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/1.22G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57e8eba3744f47a08741ab2fcf7931fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f088d8b746dd4129a17ccfd02cd265ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe2b96cf83944abb9e1cd6f57ad6dd14"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a217568c8c84e848c9ea281cc25661c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VJWNGmGe2MUt",
        "outputId": "c8eef094-5a6f-4323-ac37-eae0811fde71"
      },
      "source": [
        "# download any long text from wikipedia, via  https://pypi.org/project/wikipedia/\n",
        "import wikipedia\n",
        "wikipedia.set_lang(\"en\")\n",
        "\n",
        "text_long = wikipedia.summary(\"Donald Trump\").replace('\\n', ' ')\n",
        "print(f\"Original text:\\n{text_long}\\n\")\n",
        "\n",
        "# translate the text from wikipedia\n",
        "text_summarized = pipeline_summarize(text_long, min_length=5, max_length=30)\n",
        "print(f\"Summarized text:\\n{text_summarized[0]['summary_text']}\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original text:\n",
            "Donald John Trump (born June 14, 1946) is an American politician, media personality, and businessman who served as the 45th president of the United States from 2017 to 2021. Trump graduated from the University of Pennsylvania with a bachelor's degree in economics in 1968. He became president of his father's real-estate business in 1971 and renamed it the Trump Organization. He expanded its operations to building and renovating skyscrapers, hotels, casinos, and golf courses and later started side ventures, mostly by licensing his name. From 2004 to 2015, he co-produced and hosted the reality television series The Apprentice. He and his businesses have been plaintiff or defendant in more than 4,000 state and federal legal actions, including six business bankruptcies. Trump won the 2016 presidential election as the Republican nominee against Democratic nominee Hillary Clinton while losing the popular vote. During the campaign, his political positions were described as populist, protectionist, isolationist, and nationalist. His election and policies sparked numerous protests. He was the first U.S. president with no prior military or government service. The 2017–2019 special counsel investigation established that Russia interfered in the 2016 election to favor his campaign. Trump promoted conspiracy theories and made many false and misleading statements during his campaigns and presidency, to a degree unprecedented in American politics. Many of his comments and actions have been characterized as racially charged or racist and many as misogynistic. As president, Trump ordered a travel ban on citizens from several Muslim-majority countries, diverted military funding toward building a wall on the U.S.-Mexico border, and implemented a policy of family separations for apprehended migrants. He weakened environmental protections, rolling back more than 100 environmental policies and regulations. He signed the Tax Cuts and Jobs Act of 2017, which cut taxes for individuals and businesses and rescinded the individual health insurance mandate penalty of the Affordable Care Act. He appointed Neil Gorsuch, Brett Kavanaugh, and Amy Coney Barrett to the U.S. Supreme Court. He initiated a trade war with China and withdrew the U.S. from the proposed Trans-Pacific Partnership trade agreement, the Paris Agreement on climate change, and the Iran nuclear deal. He met with North Korean leader Kim Jong Un three times but made no progress on denuclearization. He reacted slowly to the COVID-19 pandemic, ignored or contradicted many recommendations from health officials, used political pressure to interfere with testing efforts, and spread misinformation about unproven treatments. Trump lost the 2020 presidential election to Joe Biden. He refused to concede defeat, falsely claiming widespread electoral fraud, and attempted to overturn the results by pressuring government officials, mounting scores of unsuccessful legal challenges, and obstructing the presidential transition. On January 6, 2021, he urged his supporters to march to the U.S. Capitol, which many of them then attacked, resulting in multiple deaths and interrupting the electoral vote count. Trump is the only American president to have been impeached twice. After he tried to pressure Ukraine in 2019 to investigate Biden, he was impeached in December by the House of Representatives for abuse of power and obstruction of Congress; he was acquitted by the Senate in February 2020. The House impeached him a second time in January 2021, for incitement of insurrection, and the Senate acquitted him the next month. Scholars and historians rank Trump as one of the worst presidents in American history.Since leaving office, Trump has remained heavily involved in the Republican Party. In November 2022, he announced his candidacy for the Republican nomination in the 2024 presidential election. In March 2023, a Manhattan grand jury indicted him on 34 felony counts of falsifying business records. In June, a Miami federal grand jury indicted him on 37 felonies related to his handling of classified documents (with three charges added in July). In August, a Washington, D.C., federal grand jury indicted him on four felony counts of conspiracy and obstruction related to efforts to overturn the 2020 election. Trump pleaded not guilty to all charges. Later in August, a Fulton County, Georgia, grand jury indicted him for racketeering and other felonies committed in an effort to overturn the state's 2020 election results.  \n",
            "\n",
            "Summarized text:\n",
            " Donald John Trump was the 45th president of the United States from 2017 to 2021 . He won the 2016 presidential election as the Republican nominee\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.1.4 Named Entity Recognition"
      ],
      "metadata": {
        "id": "2qVNMGy4ZQMU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_ner = pipeline(\"token-classification\", model=\"dslim/bert-base-NER-uncased\", aggregation_strategy=\"simple\")"
      ],
      "metadata": {
        "id": "dObNMi5EZTCQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250,
          "referenced_widgets": [
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            "a87ecb81900d43fa80c7113dd616a188",
            "90ae45804f334d3c899b8aba3a682187"
          ]
        },
        "outputId": "cce7e688-7777-48c6-88ce-227adeaf73e0"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.26k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "999c09a07e014375b579ffbe9580a384"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "30a4a439385f45b7840fd2e13e89b46e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at dslim/bert-base-NER-uncased were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
            "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/39.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d104bc90fe74b02bbc4b403b3c2a671"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e66ed6f8328f403187c9e5d8601bd99f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7bd3f731819d4f7280295bfceb76bfba"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import wikipedia\n",
        "wikipedia.set_lang(\"en\")\n",
        "\n",
        "text_long = wikipedia.summary(\"Donald Trump\").replace('\\n', ' ')\n",
        "\n",
        "output = pipeline_ner(text_long)\n",
        "\n",
        "pd.DataFrame(output)"
      ],
      "metadata": {
        "id": "QW99M5wiZmaL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "149f26db-6829-4d5b-806b-4b9e312b2b36"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   entity_group     score                                         word  start  \\\n",
              "0           PER  0.990035                            donald john trump      0   \n",
              "1          MISC  0.993141                                     american     45   \n",
              "2           LOC  0.993848                                united states    141   \n",
              "3           PER  0.991072                                        trump    174   \n",
              "4           ORG  0.678423                   university of pennsylvania    199   \n",
              "5           ORG  0.817550                           trump organization    357   \n",
              "6          MISC  0.959859                               the apprentice    616   \n",
              "7           PER  0.994108                                        trump    776   \n",
              "8          MISC  0.994913                                   republican    824   \n",
              "9          MISC  0.995848                                   democratic    851   \n",
              "10          PER  0.994977                              hillary clinton    870   \n",
              "11          LOC  0.899475                                        u. s.   1106   \n",
              "12          LOC  0.997302                                       russia   1228   \n",
              "13          PER  0.987325                                        trump   1290   \n",
              "14         MISC  0.995848                                     american   1437   \n",
              "15          PER  0.983271                                        trump   1583   \n",
              "16         MISC  0.989682                                       muslim   1635   \n",
              "17          LOC  0.960882                                        u. s.   1718   \n",
              "18          LOC  0.994796                                       mexico   1723   \n",
              "19         MISC  0.930881                        tax cuts and jobs act   1931   \n",
              "20         MISC  0.948896                          affordable care act   2078   \n",
              "21          PER  0.997358                                 neil gorsuch   2112   \n",
              "22          PER  0.997841                              brett kavanaugh   2126   \n",
              "23          PER  0.997359                            amy coney barrett   2147   \n",
              "24          LOC  0.843684                                        u. s.   2172   \n",
              "25          ORG  0.784485                                supreme court   2177   \n",
              "26          LOC  0.994378                                        china   2222   \n",
              "27          LOC  0.992964                                         u. s   2245   \n",
              "28         MISC  0.870629  trans - pacific partnership trade agreement   2268   \n",
              "29         MISC  0.896354            paris agreement on climate change   2315   \n",
              "30         MISC  0.786488                                         iran   2358   \n",
              "31         MISC  0.981266                                 north korean   2389   \n",
              "32          PER  0.991367                                  kim jong un   2409   \n",
              "33         MISC  0.879111                                           co   2500   \n",
              "34         MISC  0.961186                                   ##vid - 19   2502   \n",
              "\n",
              "     end  \n",
              "0     17  \n",
              "1     53  \n",
              "2    154  \n",
              "3    179  \n",
              "4    225  \n",
              "5    375  \n",
              "6    630  \n",
              "7    781  \n",
              "8    834  \n",
              "9    861  \n",
              "10   885  \n",
              "11  1110  \n",
              "12  1234  \n",
              "13  1295  \n",
              "14  1445  \n",
              "15  1588  \n",
              "16  1641  \n",
              "17  1722  \n",
              "18  1729  \n",
              "19  1952  \n",
              "20  2097  \n",
              "21  2124  \n",
              "22  2141  \n",
              "23  2164  \n",
              "24  2176  \n",
              "25  2190  \n",
              "26  2227  \n",
              "27  2248  \n",
              "28  2309  \n",
              "29  2348  \n",
              "30  2362  \n",
              "31  2401  \n",
              "32  2420  \n",
              "33  2502  \n",
              "34  2508  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-693c910b-a876-427b-b183-c1200b4ef7e4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>entity_group</th>\n",
              "      <th>score</th>\n",
              "      <th>word</th>\n",
              "      <th>start</th>\n",
              "      <th>end</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.990035</td>\n",
              "      <td>donald john trump</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.993141</td>\n",
              "      <td>american</td>\n",
              "      <td>45</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LOC</td>\n",
              "      <td>0.993848</td>\n",
              "      <td>united states</td>\n",
              "      <td>141</td>\n",
              "      <td>154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.991072</td>\n",
              "      <td>trump</td>\n",
              "      <td>174</td>\n",
              "      <td>179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ORG</td>\n",
              "      <td>0.678423</td>\n",
              "      <td>university of pennsylvania</td>\n",
              "      <td>199</td>\n",
              "      <td>225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>ORG</td>\n",
              "      <td>0.817550</td>\n",
              "      <td>trump organization</td>\n",
              "      <td>357</td>\n",
              "      <td>375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.959859</td>\n",
              "      <td>the apprentice</td>\n",
              "      <td>616</td>\n",
              "      <td>630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.994108</td>\n",
              "      <td>trump</td>\n",
              "      <td>776</td>\n",
              "      <td>781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.994913</td>\n",
              "      <td>republican</td>\n",
              "      <td>824</td>\n",
              "      <td>834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.995848</td>\n",
              "      <td>democratic</td>\n",
              "      <td>851</td>\n",
              "      <td>861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.994977</td>\n",
              "      <td>hillary clinton</td>\n",
              "      <td>870</td>\n",
              "      <td>885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LOC</td>\n",
              "      <td>0.899475</td>\n",
              "      <td>u. s.</td>\n",
              "      <td>1106</td>\n",
              "      <td>1110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>LOC</td>\n",
              "      <td>0.997302</td>\n",
              "      <td>russia</td>\n",
              "      <td>1228</td>\n",
              "      <td>1234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.987325</td>\n",
              "      <td>trump</td>\n",
              "      <td>1290</td>\n",
              "      <td>1295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.995848</td>\n",
              "      <td>american</td>\n",
              "      <td>1437</td>\n",
              "      <td>1445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.983271</td>\n",
              "      <td>trump</td>\n",
              "      <td>1583</td>\n",
              "      <td>1588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.989682</td>\n",
              "      <td>muslim</td>\n",
              "      <td>1635</td>\n",
              "      <td>1641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>LOC</td>\n",
              "      <td>0.960882</td>\n",
              "      <td>u. s.</td>\n",
              "      <td>1718</td>\n",
              "      <td>1722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>LOC</td>\n",
              "      <td>0.994796</td>\n",
              "      <td>mexico</td>\n",
              "      <td>1723</td>\n",
              "      <td>1729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.930881</td>\n",
              "      <td>tax cuts and jobs act</td>\n",
              "      <td>1931</td>\n",
              "      <td>1952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.948896</td>\n",
              "      <td>affordable care act</td>\n",
              "      <td>2078</td>\n",
              "      <td>2097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.997358</td>\n",
              "      <td>neil gorsuch</td>\n",
              "      <td>2112</td>\n",
              "      <td>2124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.997841</td>\n",
              "      <td>brett kavanaugh</td>\n",
              "      <td>2126</td>\n",
              "      <td>2141</td>\n",
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              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.997359</td>\n",
              "      <td>amy coney barrett</td>\n",
              "      <td>2147</td>\n",
              "      <td>2164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>LOC</td>\n",
              "      <td>0.843684</td>\n",
              "      <td>u. s.</td>\n",
              "      <td>2172</td>\n",
              "      <td>2176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>ORG</td>\n",
              "      <td>0.784485</td>\n",
              "      <td>supreme court</td>\n",
              "      <td>2177</td>\n",
              "      <td>2190</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>LOC</td>\n",
              "      <td>0.994378</td>\n",
              "      <td>china</td>\n",
              "      <td>2222</td>\n",
              "      <td>2227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>LOC</td>\n",
              "      <td>0.992964</td>\n",
              "      <td>u. s</td>\n",
              "      <td>2245</td>\n",
              "      <td>2248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.870629</td>\n",
              "      <td>trans - pacific partnership trade agreement</td>\n",
              "      <td>2268</td>\n",
              "      <td>2309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.896354</td>\n",
              "      <td>paris agreement on climate change</td>\n",
              "      <td>2315</td>\n",
              "      <td>2348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.786488</td>\n",
              "      <td>iran</td>\n",
              "      <td>2358</td>\n",
              "      <td>2362</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.981266</td>\n",
              "      <td>north korean</td>\n",
              "      <td>2389</td>\n",
              "      <td>2401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>PER</td>\n",
              "      <td>0.991367</td>\n",
              "      <td>kim jong un</td>\n",
              "      <td>2409</td>\n",
              "      <td>2420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.879111</td>\n",
              "      <td>co</td>\n",
              "      <td>2500</td>\n",
              "      <td>2502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>MISC</td>\n",
              "      <td>0.961186</td>\n",
              "      <td>##vid - 19</td>\n",
              "      <td>2502</td>\n",
              "      <td>2508</td>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-693c910b-a876-427b-b183-c1200b4ef7e4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      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-693c910b-a876-427b-b183-c1200b4ef7e4 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-693c910b-a876-427b-b183-c1200b4ef7e4');\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-f521b191-ea63-44a6-9169-868bf99ece0b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f521b191-ea63-44a6-9169-868bf99ece0b')\"\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",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f521b191-ea63-44a6-9169-868bf99ece0b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.2. Universal models"
      ],
      "metadata": {
        "id": "TXPa9hHmP8vJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The models above are always tailored to **one specific task from one dataset**. The main advantage of these models is, that they are very good at this specific task and perform well on one specific dataset. In reality, however, he problems you will encounter in the real world will require a slightly different task, with different definitions of categories or on different types of texts.\n",
        "\n",
        "Universal models can partly address this issue. They also only one task. But this one task is to general/universal, that many other tasks can be reformulated as this universal task. Two examples for universal tasks are:\n",
        "- Natural Language Inference (NLI): a task that can solve any classification task.\n",
        "- Token generation: an even more universal task that can solve any text-related task."
      ],
      "metadata": {
        "id": "lEGnV9KabQg4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Zero-shot classification"
      ],
      "metadata": {
        "id": "ocJsfbLcQPpO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipeline_zeroshot_classification = pipeline(\"zero-shot-classification\", model=\"MoritzLaurer/mDeBERTa-v3-base-mnli-xnli\")"
      ],
      "metadata": {
        "id": "CQWu07esQPAF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265,
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        },
        "outputId": "51eff463-8b26-4536-b6f5-2dcfbcc45a1e"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.07k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e5ef7ba446bd41978c0cee3b31705b80"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/558M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cadb35b6786a4fc7875806d00d8c6bc1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/463 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bc21dbe2f48d4302a230a2e20a9a93f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading spm.model:   0%|          | 0.00/4.31M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0196114957947ba90e002407357cc5f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/18.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4bf36adde4b4a7794d5f5551943eac2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/156 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e89b45b543664e63b6fb2080ddc5c103"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/convert_slow_tokenizer.py:470: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Customer: I have not received my reimbursement yet. What the hell is going on?\"\n",
        "classes = ['payment issues', 'travel advice', 'bug report']  # \"account opening\", \"customer complaint\"\n",
        "\n",
        "#text = \"I do not think the government is trustworthy anymore. We need to mobilize and resist!\"\n",
        "#classes = [\"civil disobedience\", \"praise of the government\", \"travel advice\"]  # \"collective action\"\n",
        "\n",
        "output = pipeline_zeroshot_classification(text, classes, multi_label=True)\n",
        "\n",
        "pd.DataFrame(data=[output[\"labels\"], output[\"scores\"]], index=[\"class\", \"probability\"]).T\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "28a5d218-0655-4456-979e-210a85153b28",
        "id": "1D3E3Tp_QPAO"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            class probability\n",
              "0  payment issues    0.991133\n",
              "1      bug report    0.076115\n",
              "2   travel advice    0.018696"
            ],
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              "\n",
              "  <div id=\"df-8703f38a-3f87-4241-8e22-d53a4c656a8e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>class</th>\n",
              "      <th>probability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>payment issues</td>\n",
              "      <td>0.991133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>bug report</td>\n",
              "      <td>0.076115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>travel advice</td>\n",
              "      <td>0.018696</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      buttonEl.style.display =\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8703f38a-3f87-4241-8e22-d53a4c656a8e');\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",
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              "\n",
              "<div id=\"df-70a502be-eb6a-4bdd-bf35-ae28b94c8b8d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-70a502be-eb6a-4bdd-bf35-ae28b94c8b8d')\"\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",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    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",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-70a502be-eb6a-4bdd-bf35-ae28b94c8b8d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Zero-shot learning with large generative models and prompts (LLMs)"
      ],
      "metadata": {
        "id": "kAUxIaHSYmtN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# info on GPU\n",
        "!nvidia-smi\n",
        "# info on available ram\n",
        "from psutil import virtual_memory\n",
        "ram_gb = virtual_memory().total / 1e9\n",
        "print('\\n\\nYour runtime has {:.1f} gigabytes of available RAM\\n'.format(ram_gb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rk9ftqsnpL_2",
        "outputId": "2dd87eed-b3ca-4ae3-8f26-d4b8656c8ea3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Aug 21 16:03:18 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla V100-SXM2...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   41C    P0    25W / 300W |      0MiB / 16384MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n",
            "\n",
            "\n",
            "Your runtime has 54.8 gigabytes of available RAM\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# connecting to my google drive to load a large model from disk instead of downloading it\n",
        "from google.colab import drive\n",
        "import os\n",
        "drive.mount('/content/drive', force_remount=False)\n",
        "\n",
        "print(os.getcwd())\n",
        "os.chdir(\"/content/drive/My Drive/PhD/generative-models\")\n",
        "print(os.getcwd())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l8ZM8g4LvJi_",
        "outputId": "f26390d1-5222-4d75-f41e-f6410e816b34"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "/content\n",
            "/content/drive/My Drive/PhD/generative-models\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# if this doesn't work, try restarting the runtime\n",
        "from transformers import pipeline\n",
        "import torch\n",
        "\n",
        "model_name = \"flan-t5-xl\"  # use \"google/flan-t5-xl\" to download the model\n",
        "\n",
        "pipeline_zeroshot_prompting = pipeline(\n",
        "    \"text2text-generation\",  # \"text2text-generation\", \"text-generation\"\n",
        "    model=model_name,  device_map=\"auto\",  #device=device_id,\n",
        "    torch_dtype=torch.bfloat16,  #load_in_8bit=True,\n",
        ")"
      ],
      "metadata": {
        "id": "1qyLwwStopBA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "72396f55f4f84c3792eed6d1b7b97c2d",
            "fc8722604f8c46e29033488fc34119d3",
            "513c3d16cd2b47bcb00951c1274f5618",
            "d7e13f95353f42db8caec5464d5fcfad",
            "466285c87dab4f148269d9c7ddcdcb98",
            "e1ee02ea1c2840b3b9694c8ec8c568d0",
            "eb0680798af64a50b1607dc35a2ac0d6",
            "78cda834719e468d96f6fe807c94279e",
            "21f67b743fc3400a91a4e38d68af0d34",
            "8569a45c7c5f471687e01b10d74c9ac1",
            "05aedd736af54db6af7c0f1b8ba476c9"
          ]
        },
        "outputId": "fa3eec26-c098-4031-8b5a-d61cc16dc9fa"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72396f55f4f84c3792eed6d1b7b97c2d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**A note on hardware and using LLMs:**\n",
        "\n",
        "The main disadvantage of generative LLMs is that they are very large and inefficient. They need expensive hardware to run. You will not be able to run models larger than 7~ billion parameters on the standard T4 GPU from the free version of Google Colab. There are different solutions to this:\n",
        "* Use better **hardware**. The main factor for loading a large model is \"RAM\" (Random Access Memory). The standard T4 GPU has 13.6 GB RAM, which is often not enough. With Colab Pro, you can get A100 GPUs with 40GB RAM. This GPU can load larger models, but is more expensive\n",
        "* **Software solutions**: There are also software solutions like lowering precision or quantization. Making these solutions work takes some testing and depends on the model, so we will not cover this here.\n",
        "* Hugging Face has some **documentation** [here](https://huggingface.co/docs/transformers/performance).\n",
        "* Also consider using **smaller models**. For many use-cases, you might not need a very large LLM for your task."
      ],
      "metadata": {
        "id": "CtBrEsSDoBzX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## text classification (framed as multiple choice)\n",
        "text = '''\n",
        "Here is a quote:\n",
        "\"I do not think the government is trustworthy anymore. We need to mobilize and resist!\".\n",
        "Is this quote about either (a) \"civil disobedience\", or (b) \"praise of the government\", or (c) \"government funded mobility\"?\n",
        "Only choose one of the three options.'''\n",
        "\n",
        "output = pipeline_zeroshot_prompting(text)\n",
        "output"
      ],
      "metadata": {
        "id": "RX6pnA3blB4K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48d63faf-90be-4601-8b58-ca2b8937a803"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': '(a)'}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## question answering\n",
        "text = '''Here is a news article from Thursday 22.12.2022: \"\n",
        "European Parliament website hit by cyberattack after Russian terrorism vote\n",
        "One official blamed pro-Russian hacking group Killnet for the DDoS attack.\n",
        "The European Parliament website on Wednesday faced a \"sophisticated\" cyberattack disrupting its services moments after members voted to declare Russia a state sponsor of terrorism.\n",
        "\"I confirm that the Parliament has been subject to an external cyber attack, but the Parliamentary services are doing well to defend the Parliament,\" Dita Charanzová, Czech MEP and Parliament vice president responsible for cybersecurity, said in a statement.\n",
        "Another senior Parliament official, requesting not to be named, said “it might be the most sophisticated attack that the Parliament has known so far.”\n",
        "The attack is what's known as a distributed denial-of-service (DDoS) attack, in which massive amounts of traffic are sent to servers in an attempt to block internet users from accessing websites, Marcel Kolaja, European Parliament member for the Czech Pirate party, confirmed.\n",
        "DDoS attacks are used by hacking groups to disrupt and cause chaos. It emerged as a favorite instrument of Russian hacking groups like Killnet, notably as a way to protest against political decisions in European countries to support Ukraine in the war.\n",
        "The attack on the European Parliament website comes after the chamber voted on Wednesday to adopt a resolution declaring Russia a state sponsor of terrorism because of Moscow’s strikes on civilian targets in Ukraine.\n",
        "\"We have a strong indication that it is from Killnet, the hackers with links to Russia indeed. This is my information, but it is under control. It only cut the external access to the Parliament's website ... Unless there is extra attacks we expect it to be back and accessible very soon,\" said Eva Kaili, Greek member and vice president of the European Parliament.\n",
        "\"This morning Russia was still designated as a terrorist state in an official resolution. This afternoon the entire network collapses in [the European Parliament],\" Alexandra Geese, German Greens' MEP, tweeted.\n",
        "\".\n",
        "\n",
        "'''\n",
        "\n",
        "prompt_lst = [\n",
        "    \"Was there a cyber attack? Yes or no.\",\n",
        "    \"What is the name of the attacker?\",\n",
        "    \"Which country does the attacker come from?\",\n",
        "    \"What is the name of the victim of the cyber attack?\",\n",
        "    \"Which country does the victim of the cyber attack come from?\",\n",
        "    \"If there was a cyber attack, what type of cyber attack was it?\",\n",
        "    \"What was the date of the cyber attack?\",\n",
        "    \"What or who is the source of information on the cyber attack?\",\n",
        "    \"What damages were caused by the cyber attack?\",\n",
        "    \"What was the political response to the cyber attack?\",\n",
        "    'How certain is it that there was a cyber attack? \"Very certain\", \"moderately certain\", or \"not certain\"? Chose one of these options.'\n",
        "]\n",
        "\n",
        "# chain-of-thought tests https://arxiv.org/pdf/2210.11416.pdf\n",
        "instructions_begin = \"\"  #\"Answer the following question by reasoning step-by-step: \"\n",
        "instructions_end = \"\"  #\" Explain the answer with step-by-step reasoning\"\n",
        "other_category = ' Answer \"unknown\" if the correct answer is not explicitly mentioned in the article.'\n",
        "\n",
        "input_lst = [text + instructions_begin + prompt + other_category + instructions_end for prompt in prompt_lst]\n",
        "\n",
        "output_lst = []\n",
        "for input, prompt in zip(input_lst, prompt_lst):\n",
        "    output = pipeline_zeroshot_prompting(input)\n",
        "    output_lst.append(output)\n",
        "    print(f'{prompt:90}{output[0][\"generated_text\"]}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iI61bpxswVgf",
        "outputId": "d4bb3607-4c38-46ab-cd92-406f78a80232"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Was there a cyber attack? Yes or no.                                                      Yes\n",
            "What is the name of the attacker?                                                         Killnet\n",
            "Which country does the attacker come from?                                                Russia\n",
            "What is the name of the victim of the cyber attack?                                       European Parliament\n",
            "Which country does the victim of the cyber attack come from?                              unknown\n",
            "If there was a cyber attack, what type of cyber attack was it?                            distributed denial-of-service (DDoS) attack\n",
            "What was the date of the cyber attack?                                                    Wednesday\n",
            "What or who is the source of information on the cyber attack?                             Eva Kaili\n",
            "What damages were caused by the cyber attack?                                             unanswerable\n",
            "What was the political response to the cyber attack?                                      unanswerable\n",
            "How certain is it that there was a cyber attack? \"Very certain\", \"moderately certain\", or \"not certain\"? Chose one of these options.Very certain\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## text summarisation\n",
        "text = \"\"\"\n",
        "\"Donald John Trump (born June 14, 1946) is an American politician, media personality, and businessman who served as the 45th president of the United States from 2017 to 2021.\n",
        "Trump graduated from the Wharton School of the University of Pennsylvania with a bachelor's degree in 1968. He became president of his father's real estate business in 1971 and renamed it The Trump Organization.\n",
        "He expanded the company's operations to building and renovating skyscrapers, hotels, casinos, and golf courses. He later started side ventures, mostly by licensing his name. From 2004 to 2015, he co-produced\n",
        "and hosted the reality television series The Apprentice. Trump and his businesses have been involved in more than 4,000 state and federal legal actions, including six bankruptcies. Trump's political positions\n",
        "have been described as populist, protectionist, isolationist, and nationalist. He won the 2016 United States presidential election as the Republican nominee against Democratic nominee Hillary Clinton despite l\n",
        "osing the national popular vote. He became the first U.S. president with no prior military or government service. His election and policies sparked numerous protests. The 2017–2019 special counsel investigation\n",
        "led by Robert Mueller established that Russia interfered in the 2016 election to favor the election of Trump. Trump promoted conspiracy theories and made many false and misleading statements during his campaigns\n",
        "and presidency, to a degree unprecedented in American politics. Many of his comments and actions have been characterized as racially charged or racist, and many as misogynistic. Trump ordered a travel ban\n",
        "on citizens from several Muslim-majority countries, diverted military funding towards building a wall on the U.S.–Mexico border, and implemented a policy of family separations for apprehended migrants.\n",
        "He rolled back more than 100 environmental policies and regulations in an aggressive attempt to weaken environmental protections.\"\n",
        "Please summarize this text by providing the key information about Donald Trump. Summary:\n",
        "\"\"\"\n",
        "\n",
        "output = pipeline_zeroshot_prompting(text)\n",
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhU5J36-luwl",
        "outputId": "05076953-a72e-4801-b616-a5da6972168e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': '\"Donald Trump (born June 14, 1946) is an American politician, media personality,'}]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "vqUFtEFRBtEZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
      ],
      "metadata": {
        "id": "IrF__QdTQZR0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise  +  Q&A"
      ],
      "metadata": {
        "id": "JwM1gzfvA-Eb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "**1. Exercise:** (5 min)\n",
        "\n",
        "Browse through the Hugging Face Hub and **identify a model or dataset that could be useful for you**. Then open this Google Doc and copy-paste the model identifier and a short explanation why this model is interesting for you. Googel Doc: https://docs.google.com/document/d/1KZ6DnZDUg_sxqpS8hhF0MDohZ0IRUZaV83Ixu93n-X8/edit?usp=sharing\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "1V-hyIweBvqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**2. Reading, thinking & asking:** (5 min)\n",
        "\n",
        "a) Go through the notebook and ask any questions you might have. You can also run the notebook yourself.\n",
        "\n",
        "b) Write the answers to the following questions on a piece of paper / digital notebook in your own words:\n",
        "\n",
        "* How does open source help increase accessibility to machine learning? Where does it not help?\n",
        "\n",
        "* In your own words, write down the main difference between standard models and universal models.\n",
        "\n",
        "* **Post any questions in the chat!**\n"
      ],
      "metadata": {
        "id": "ZEtqC-Wt3Jsw"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvzcEy0DS2ge"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    }
  ]
}