{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "zq-6s7LbPnKH"
      },
      "outputs": [],
      "source": [
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulZIBA1ZoSsV",
        "outputId": "afcdef9b-9586-4650-f828-3f794bd185d6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/292.8 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m292.8/292.8 kB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -qU langchain_openai langchain_huggingface langchain_core==0.2.38 langchain langchain_community langchain-text-splitters pypdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3GFD7B-tOCrx",
        "outputId": "5e701205-1d35-48f2-8217-8943a7e94921"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/981.5 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m32.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m21.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m472.8/472.8 kB\u001b[0m \u001b[31m18.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.5/1.5 MB\u001b[0m \u001b[31m49.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m27.0/27.0 MB\u001b[0m \u001b[31m36.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m159.9/159.9 kB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m431.4/431.4 kB\u001b[0m \u001b[31m23.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m274.7/274.7 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.4/3.4 MB\u001b[0m \u001b[31m46.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.3/45.3 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.7/82.7 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -qU faiss-cpu unstructured==0.15.7 python-pptx==1.0.2 nltk==3.9.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wA_mlurVqtrp",
        "outputId": "a6a166b9-5a5e-4840-d620-3243489c3fd7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter Your OpenAI API Key: ··········\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import getpass\n",
        "\n",
        "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"Enter Your OpenAI API Key: \")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "qzAdFsClBJ6v"
      },
      "outputs": [],
      "source": [
        "pdf_paths = [\"AI_Risk_Management_Framework.pdf\",\n",
        "\"Blueprint-for-an-AI-Bill-of-Rights.pdf\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "DRi4ZSJLBY9T"
      },
      "outputs": [],
      "source": [
        "from langchain_community.document_loaders import PyPDFLoader\n",
        "\n",
        "pdf_documents = []\n",
        "for pdf_path in pdf_paths:\n",
        "    loader = PyPDFLoader(pdf_path)\n",
        "    pdf_documents.extend(loader.load())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "NsPrOOqXOsNX"
      },
      "outputs": [],
      "source": [
        "from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    chunk_size = 750,\n",
        "    chunk_overlap  = 20,\n",
        "    length_function = len\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "OMYPX6N6Os8M"
      },
      "outputs": [],
      "source": [
        "training_documents = text_splitter.split_documents(pdf_documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PAozuMoNOvnp",
        "outputId": "57d60a5a-e6d6-494c-8237-546db0a27210"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "640"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(training_documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "AwyIForybIpo"
      },
      "outputs": [],
      "source": [
        "import uuid\n",
        "\n",
        "id_set = set()\n",
        "\n",
        "for document in training_documents:\n",
        "  id = str(uuid.uuid4())\n",
        "  while id in id_set:\n",
        "    id = uuid.uuid4()\n",
        "  id_set.add(id)\n",
        "  document.metadata[\"id\"] = id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "MTS4GTSEcnG4"
      },
      "outputs": [],
      "source": [
        "training_split_documents = training_documents[:300]\n",
        "val_split_documents = training_documents[300:350]\n",
        "test_split_documents = training_documents[350:400]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "_EWfmIscMrvg"
      },
      "outputs": [],
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "qa_chat_model = ChatOpenAI(\n",
        "    model=\"gpt-4o-mini\",\n",
        "    temperature=0\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "diEWcw00NMSj"
      },
      "outputs": [],
      "source": [
        "from langchain_core.prompts import ChatPromptTemplate\n",
        "\n",
        "qa_prompt = \"\"\"\\\n",
        "Given the following context, you must generate questions based on only the provided context.\n",
        "\n",
        "You are to generate {n_questions} questions which should be provided in the following format:\n",
        "\n",
        "1. QUESTION #1\n",
        "2. QUESTION #2\n",
        "...\n",
        "\n",
        "Context:\n",
        "{context}\n",
        "\"\"\"\n",
        "\n",
        "qa_prompt_template = ChatPromptTemplate.from_template(qa_prompt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ggl9SSjiNbpG"
      },
      "outputs": [],
      "source": [
        "question_generation_chain = qa_prompt_template | qa_chat_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "pNmybW-zZyI_"
      },
      "outputs": [],
      "source": [
        "response = question_generation_chain.invoke({\"context\": \"regulation\", \"n_questions\":2})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qmzfIaeeaL5E",
        "outputId": "09853ab7-ee69-4407-dfc7-efc0d3830c0d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AIMessage(content='1. What is the purpose of regulation in various industries?  \\n2. How do regulations impact businesses and consumers?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 23, 'prompt_tokens': 57, 'total_tokens': 80, 'completion_tokens_details': {'reasoning_tokens': 0}}, 'model_name': 'gpt-4o-mini-2024-07-18', 'system_fingerprint': 'fp_71fab63729', 'finish_reason': 'stop', 'logprobs': None}, id='run-42261cd9-dae6-4062-ba45-7c01a26ba144-0', usage_metadata={'input_tokens': 57, 'output_tokens': 23, 'total_tokens': 80})"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "response"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "0jd6Cyp7bdyf"
      },
      "outputs": [],
      "source": [
        "import tqdm\n",
        "def create_questions(documents, n_questions):\n",
        "  questions = {}\n",
        "  relevant_docs = {}\n",
        "  for document in tqdm.tqdm(documents):\n",
        "    document_content = {\"context\" : document.page_content, \"questions\" : []}\n",
        "    questions_generated = question_generation_chain.invoke({\"context\": document.page_content, \"n_questions\": n_questions})\n",
        "    for question in questions_generated.content.split(\"\\n\"):\n",
        "      question_id = str(uuid.uuid4())\n",
        "      questions[question_id] = \"\".join(question.split(\".\")[1:]).strip()\n",
        "      relevant_docs[question_id] = [document.metadata[\"id\"]]\n",
        "  return questions, relevant_docs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FSTG0bb7w73"
      },
      "source": [
        "We'll use the function to generate training, validation, and test data with `n_questions=2` for each."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "85Dq6KRqEs0F",
        "outputId": "5bd88461-82e9-4845-833b-b4b497672ba9"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 300/300 [05:40<00:00,  1.13s/it]\n"
          ]
        }
      ],
      "source": [
        "training_questions, training_relevant_contexts = create_questions(training_split_documents,2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIZm4CqGVzBx",
        "outputId": "2b30c397-21a5-422a-aebe-0f0696abc0dd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 50/50 [00:44<00:00,  1.11it/s]\n"
          ]
        }
      ],
      "source": [
        "val_questions, val_relevant_contexts = create_questions(val_split_documents,2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o6qUHg9sV2_y",
        "outputId": "c6e4560f-c718-4e1a-ebae-2ef8f27c781e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 50/50 [00:49<00:00,  1.01it/s]\n"
          ]
        }
      ],
      "source": [
        "test_questions, test_relevant_contexts = create_questions(test_split_documents,2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "iF6IFFq9VsNu"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "training_corpus = {train_item.metadata[\"id\"] : train_item.page_content for train_item in training_split_documents}\n",
        "\n",
        "train_dataset = {\n",
        "    \"questions\" : training_questions,\n",
        "    \"relevant_contexts\" : training_relevant_contexts,\n",
        "    \"corpus\" : training_corpus\n",
        "}\n",
        "\n",
        "with open(\"training_dataset.jsonl\", \"w\") as f:\n",
        "  json.dump(train_dataset, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "PqF9WaueV-V8"
      },
      "outputs": [],
      "source": [
        "val_corpus = {val_item.metadata[\"id\"] : val_item.page_content for val_item in val_split_documents}\n",
        "\n",
        "val_dataset = {\n",
        "    \"questions\" : val_questions,\n",
        "    \"relevant_contexts\" : val_relevant_contexts,\n",
        "    \"corpus\" : val_corpus\n",
        "}\n",
        "\n",
        "with open(\"val_dataset.jsonl\", \"w\") as f:\n",
        "  json.dump(val_dataset, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "0DSQ7WMnWAu6"
      },
      "outputs": [],
      "source": [
        "train_corpus = {test_item.metadata[\"id\"] : test_item.page_content for test_item in test_split_documents}\n",
        "\n",
        "test_dataset = {\n",
        "    \"questions\" : test_questions,\n",
        "    \"relevant_contexts\" : test_relevant_contexts,\n",
        "    \"corpus\" : train_corpus\n",
        "}\n",
        "\n",
        "with open(\"test_dataset.jsonl\", \"w\") as f:\n",
        "  json.dump(test_dataset, f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXzVHP3v1Cno",
        "outputId": "8c3db6f0-023d-4a49-a231-eb3417b273a6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found existing installation: pyarrow 14.0.2\n",
            "Uninstalling pyarrow-14.0.2:\n",
            "  Successfully uninstalled pyarrow-14.0.2\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.0/38.0 MB\u001b[0m \u001b[31m27.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m35.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m172.0/172.0 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.3.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip uninstall -y pyarrow\n",
        "!pip install -qU sentence_transformers datasets pyarrow==14.0.1"
      ]
    },
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        },
        "id": "G-PGsQB7Xo6V",
        "outputId": "539bd9ea-e92f-41da-95b8-5b67251745cc"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sentence_transformers/cross_encoder/CrossEncoder.py:13: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  from tqdm.autonotebook import tqdm, trange\n",
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5ee51a7f95ff4eb0bb53def4627707f8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43c2e9e387c5465eaa6c69d657121a2c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config_sentence_transformers.json:   0%|          | 0.00/252 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca535b2a4a654518a0a761b90fbf852a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/84.6k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "9b224bcb3144401d8579e32a719d21b3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "sentence_bert_config.json:   0%|          | 0.00/107 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dd79fa96509f4dbda89ce93af0f6aa19",
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e3c5d1a3c2c54f628bbf8882a0e836b3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/436M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "20e214d0cb654e76a0ce00bffc5a0fa8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.38k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3aa767354363441185173530a8c4b7c8",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "46eb742db2614250a29f4af09e41dddf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/712k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bc5e3faa57f8459a9d1ba1c9428b8b83",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/695 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b6a6dd121f61444bbc157f6ae3b7cf43",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "1_Pooling/config.json:   0%|          | 0.00/296 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "model_id = \"Snowflake/snowflake-arctic-embed-m\"\n",
        "model = SentenceTransformer(model_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "B-WbpuUWYFJr"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data import Dataset\n",
        "from sentence_transformers import InputExample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "8Lokhy6KYHAv"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 20"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "JJk37zQsYJ4P"
      },
      "outputs": [],
      "source": [
        "corpus = train_dataset['corpus']\n",
        "queries = train_dataset['questions']\n",
        "relevant_docs = train_dataset['relevant_contexts']\n",
        "\n",
        "examples = []\n",
        "for query_id, query in queries.items():\n",
        "    doc_id = relevant_docs[query_id][0]\n",
        "    text = corpus[doc_id]\n",
        "    example = InputExample(texts=[query, text])\n",
        "    examples.append(example)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "tiizmeIqZ_-w"
      },
      "outputs": [],
      "source": [
        "loader = DataLoader(\n",
        "    examples, batch_size=BATCH_SIZE\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "Uga4nnBqlVeh"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers.losses import MatryoshkaLoss, MultipleNegativesRankingLoss\n",
        "\n",
        "matryoshka_dimensions = [768, 512, 256, 128, 64]\n",
        "inner_train_loss = MultipleNegativesRankingLoss(model)\n",
        "train_loss = MatryoshkaLoss(\n",
        "    model, inner_train_loss, matryoshka_dims=matryoshka_dimensions\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "f0hAFwUyaHQG"
      },
      "outputs": [],
      "source": [
        "from sentence_transformers.evaluation import InformationRetrievalEvaluator\n",
        "\n",
        "corpus = val_dataset['corpus']\n",
        "queries = val_dataset['questions']\n",
        "relevant_docs = val_dataset['relevant_contexts']\n",
        "\n",
        "evaluator = InformationRetrievalEvaluator(queries, corpus, relevant_docs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "svZG0pBHiQr6"
      },
      "outputs": [],
      "source": [
        "EPOCHS = 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332,
          "referenced_widgets": [
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            "38e483e60dde4b6396f3350e2bad72b1"
          ]
        },
        "id": "aDhUHZY-iR09",
        "outputId": "a47c6bef-cbe9-481c-f97c-af059f4a9b72"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='150' max='150' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [150/150 02:39, Epoch 5/5]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Cosine Accuracy@1</th>\n",
              "      <th>Cosine Accuracy@3</th>\n",
              "      <th>Cosine Accuracy@5</th>\n",
              "      <th>Cosine Accuracy@10</th>\n",
              "      <th>Cosine Precision@1</th>\n",
              "      <th>Cosine Precision@3</th>\n",
              "      <th>Cosine Precision@5</th>\n",
              "      <th>Cosine Precision@10</th>\n",
              "      <th>Cosine Recall@1</th>\n",
              "      <th>Cosine Recall@3</th>\n",
              "      <th>Cosine Recall@5</th>\n",
              "      <th>Cosine Recall@10</th>\n",
              "      <th>Cosine Ndcg@10</th>\n",
              "      <th>Cosine Mrr@10</th>\n",
              "      <th>Cosine Map@100</th>\n",
              "      <th>Dot Accuracy@1</th>\n",
              "      <th>Dot Accuracy@3</th>\n",
              "      <th>Dot Accuracy@5</th>\n",
              "      <th>Dot Accuracy@10</th>\n",
              "      <th>Dot Precision@1</th>\n",
              "      <th>Dot Precision@3</th>\n",
              "      <th>Dot Precision@5</th>\n",
              "      <th>Dot Precision@10</th>\n",
              "      <th>Dot Recall@1</th>\n",
              "      <th>Dot Recall@3</th>\n",
              "      <th>Dot Recall@5</th>\n",
              "      <th>Dot Recall@10</th>\n",
              "      <th>Dot Ndcg@10</th>\n",
              "      <th>Dot Mrr@10</th>\n",
              "      <th>Dot Map@100</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>No log</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.196000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.904303</td>\n",
              "      <td>0.872179</td>\n",
              "      <td>0.872179</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.196000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.904303</td>\n",
              "      <td>0.872179</td>\n",
              "      <td>0.872179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>No log</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.912129</td>\n",
              "      <td>0.881667</td>\n",
              "      <td>0.881667</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.912129</td>\n",
              "      <td>0.881667</td>\n",
              "      <td>0.881667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>No log</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.915820</td>\n",
              "      <td>0.886667</td>\n",
              "      <td>0.886667</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.915820</td>\n",
              "      <td>0.886667</td>\n",
              "      <td>0.886667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>90</td>\n",
              "      <td>No log</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.915820</td>\n",
              "      <td>0.886667</td>\n",
              "      <td>0.886667</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.790000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.915820</td>\n",
              "      <td>0.886667</td>\n",
              "      <td>0.886667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>No log</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.919511</td>\n",
              "      <td>0.891667</td>\n",
              "      <td>0.891667</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.919511</td>\n",
              "      <td>0.891667</td>\n",
              "      <td>0.891667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>120</td>\n",
              "      <td>No log</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.919511</td>\n",
              "      <td>0.891667</td>\n",
              "      <td>0.891667</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.919511</td>\n",
              "      <td>0.891667</td>\n",
              "      <td>0.891667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>No log</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.918202</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>0.198000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.918202</td>\n",
              "      <td>0.890000</td>\n",
              "      <td>0.890000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "25a7c23f2cb24e81868320b9852addca",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Computing widget examples:   0%|          | 0/1 [00:00<?, ?example/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "warmup_steps = int(len(loader) * EPOCHS * 0.1)\n",
        "\n",
        "model.fit(\n",
        "    train_objectives=[(loader, train_loss)],\n",
        "    epochs=EPOCHS,\n",
        "    warmup_steps=warmup_steps,\n",
        "    output_path='midterm-finetuned_arctic',\n",
        "    show_progress_bar=True,\n",
        "    evaluator=evaluator,\n",
        "    evaluation_steps=50,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "23d56ca0e8d64c74af7d5eaad78bea6d",
            "07a175f25c764e1b8f1ffaa2bffa55b6",
            "0ce71bda08b54485b0cdb3ff77302d5d",
            "01eba205d9404b2fb4366d20ba2f0c67",
            "55b06a249e0142fd9ccf2403af020952",
            "a7a5f8af22b642309d3b58ba24ef3f90",
            "65b7d194ac4e48ce9690b1e0585fb9de",
            "af968f9a670e4e8682a218c1bfc98eb0",
            "cb8711e8a8b94ddda85620134085b0f4",
            "94eb637ad2b84eabbf1584f1fb9697df",
            "6a5078f74c424835900d2f833e6df9ca",
            "3a6295e3478341e5b14d11aa1e9f562f",
            "9116a97c2b414c46a55a9bfe4c0235e0",
            "69e53dfedcef499a865bc959b77a100b",
            "e24a206260d54d3fb4dd22d3623f564e",
            "0a3ea30be3d344babda8626d314bf8f1",
            "d6db5279c29d425ba5f4987c0203008e",
            "76a7c151afee449a8dbb4cdc45059ac3",
            "fd37bc977a9c4c2aa1b7ee0c84753583",
            "0f1f9bf364274c8687150e018312219f",
            "c64438185cfe4c1a8e99ed46ea8454fd",
            "547684bd781a4293b4579ab26b091bbc",
            "16f9704205eb4b4db2896b26cd0b4e24",
            "48b4992c198b4d3a8afcc6b4d16639f8",
            "91f47e8d44214b849332a85451da05a4",
            "2ac043a3aace4d218f10c4b46470d69b",
            "1fe14c9d9cc54f0b9ddcaa90e23edc18",
            "6172bf37661445dead1b24e721ada152",
            "d63fe1e1f07a4c869ce1cbacea2d81f9",
            "79fc36292ba64c7ea5cd6889a43cb690",
            "8626b67043ba4f32b1a33049ba93b6b3",
            "c5879f12efc24ae892fc7bec6c83134a"
          ]
        },
        "id": "A8b70f06EvaS",
        "outputId": "e3405fcf-be9d-48b9-c006-db1368ff22ce"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "23d56ca0e8d64c74af7d5eaad78bea6d",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122,
          "referenced_widgets": [
            "c5d84cd862c94002948785ff35514d8e",
            "16bb68e1070140bfaa8100c2864496ba",
            "95e8b92330014619a63239fd29404518",
            "b7aa5212585d4e7f8b1fa4a1356f64de",
            "9509421745ff41d8a60d22b8dee03d89",
            "101bca7f976f4ef68b7c8d0ae3292fc3",
            "4793b45655a34a9cb53931b600b6ec4f",
            "d7aab98290ad44e4ac54680f24b72ecb",
            "434cbad3bb264f1e8ce00087bc10ff4c",
            "1cafb74072f5401e9b6c05423d026d75",
            "df82dbc26e5a47cd8b8bdd3fe2bda677"
          ]
        },
        "id": "zgB3J7jqEtPo",
        "outputId": "526fdf6e-fda4-474f-807c-67bb23b3fc76"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of BertModel were not initialized from the model checkpoint at midterm-finetuned_arctic and are newly initialized: ['pooler.dense.bias', 'pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c5d84cd862c94002948785ff35514d8e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/438M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/XicoC/midterm-finetuned-arctic/commit/9d4fa34226adf1b69f4d4dbcb53a6e2d6e66da93'"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sentence_transformers import SentenceTransformer\n",
        "\n",
        "model = SentenceTransformer('midterm-finetuned_arctic')\n",
        "\n",
        "model.push_to_hub(\"midterm-finetuned-arctic\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
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            "grid_auto_rows": null,
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            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
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            "margin": null,
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            "max_width": null,
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            "min_width": null,
            "object_fit": null,
            "object_position": null,
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            "_model_module_version": "1.5.0",
            "_model_name": "CheckboxModel",
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            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "CheckboxView",
            "description": "Add token as git credential?",
            "description_tooltip": null,
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            "indent": true,
            "layout": "IPY_MODEL_3a6295e3478341e5b14d11aa1e9f562f",
            "style": "IPY_MODEL_9116a97c2b414c46a55a9bfe4c0235e0",
            "value": true
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