646_project_dataset2_p1.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "-pz8A1MCP7fk"
      },
      "outputs": [],
      "source": [
        "import urllib.request, json\n",
        "\n",
        "file_path = '../LaMP_2.json'\n",
        "\n",
        "\n",
        "with open(file_path, 'r') as json_file:\n",
        "    data = json.load(json_file)\n",
        "\n",
        "\n",
        "ratio = 0.6\n",
        "start = 0\n",
        "end = 5#len(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N9vXVTPnxkwv"
      },
      "source": [
        "### Download & preparing data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "J-B2DV-OwE8L"
      },
      "outputs": [],
      "source": [
        "from langchain.schema import Document"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "rPVln_zf-J6c"
      },
      "outputs": [],
      "source": [
        "def get_docs(idx):\n",
        "  content = []\n",
        "  for i in range(len(data[idx]['profile'])):\n",
        "      content.append(data[idx]['profile'][i]['text'])\n",
        "  documents = [Document(page_content=text) for text in content]\n",
        "  return documents"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzicQux_xtPN"
      },
      "source": [
        "### BM25 retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "JECyHwc9fl4W"
      },
      "outputs": [],
      "source": [
        "from rank_bm25 import BM25Okapi\n",
        "from collections import namedtuple\n",
        "\n",
        "DocumentWithScore = namedtuple('DocumentWithScore', ['document', 'score'])\n",
        "\n",
        "def bm25(documents, query, ratio):\n",
        "    tokenized_corpus = [(doc.page_content).split(\" \") for doc in documents]\n",
        "    bm25 = BM25Okapi(tokenized_corpus)\n",
        "    tokenized_query = query.split(\" \")\n",
        "    bm25_scores = bm25.get_scores(tokenized_query)\n",
        "\n",
        "    onlydocs = [x.page_content for x in documents]\n",
        "    documents_with_scores = [\n",
        "        DocumentWithScore(document=doc, score=score)\n",
        "        for doc, score in zip(onlydocs, bm25_scores)\n",
        "    ]\n",
        "\n",
        "    sorted_documents_with_scores = sorted(documents_with_scores, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    top_n_documents_with_scores = sorted_documents_with_scores[:int(len(documents)*ratio)]\n",
        "\n",
        "    document_instances = [Document(page_content=doc.document) for doc in top_n_documents_with_scores]\n",
        "    return document_instances, top_n_documents_with_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hC-mK_ZixwfV"
      },
      "source": [
        "### TF-IDF Retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "bzAye_QuG6dJ"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from collections import namedtuple\n",
        "\n",
        "DocumentWithScore = namedtuple('DocumentWithScore', ['document', 'score'])\n",
        "\n",
        "def tfidf_retrieval_sklearn(documents, query, ratio):\n",
        "    top_n = int(len(documents)*ratio)\n",
        "    docs = [(doc.page_content) for doc in documents]\n",
        "    vectorizer = TfidfVectorizer()\n",
        "\n",
        "    tfidf_matrix = vectorizer.fit_transform(docs)\n",
        "\n",
        "    query_vector = vectorizer.transform([query])\n",
        "\n",
        "    similarity_scores = cosine_similarity(query_vector, tfidf_matrix)\n",
        "\n",
        "    top_n_indices = similarity_scores.argsort()[0][-top_n:][::-1]\n",
        "\n",
        "    top_n_documents = [docs[i] for i in top_n_indices]\n",
        "    document_instances = [Document(page_content=doc) for doc in top_n_documents]\n",
        "\n",
        "    top_n_documents_with_scores = [\n",
        "        DocumentWithScore(document=docs[i], score=similarity_scores[0][i])\n",
        "        for i in top_n_indices\n",
        "    ]\n",
        "\n",
        "    return document_instances, top_n_documents_with_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFM68zc3KQO8"
      },
      "source": [
        "### Contriever msmarco"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "oj6l7sOacAUs"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "from collections import namedtuple\n",
        "\n",
        "DocumentWithScore = namedtuple('DocumentWithScore', ['document', 'score'])\n",
        "def contrievermsmarco(documents, query, ratio):\n",
        "    content = [doc.page_content for doc in documents]\n",
        "    top_n = int(len(content) * ratio)\n",
        "\n",
        "   \n",
        "    tokenizer = AutoTokenizer.from_pretrained('facebook/contriever-msmarco')\n",
        "    model = AutoModel.from_pretrained('facebook/contriever-msmarco')\n",
        "\n",
        "    \n",
        "    content_with_query = [query] + content\n",
        "\n",
        "    \n",
        "    inputs = tokenizer(content_with_query, padding=True, truncation=True, return_tensors='pt')\n",
        "\n",
        "    \n",
        "    outputs = model(**inputs)\n",
        "\n",
        "   \n",
        "    def mean_pooling(token_embeddings, mask):\n",
        "        token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)\n",
        "        sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]\n",
        "        return sentence_embeddings\n",
        "\n",
        "   \n",
        "    query_embedding = mean_pooling(outputs[0][:1], inputs['attention_mask'][:1]).squeeze()\n",
        "\n",
        "    \n",
        "    document_embeddings = mean_pooling(outputs[0][1:], inputs['attention_mask'][1:]).squeeze()\n",
        "\n",
        "    \n",
        "    similarity_scores = torch.matmul(query_embedding, document_embeddings.T).tolist()\n",
        "\n",
        "    \n",
        "    document_with_scores = list(zip(content, similarity_scores))\n",
        "\n",
        "   \n",
        "    document_with_scores.sort(key=lambda x: x[1], reverse=True)\n",
        "    document_with_scores = document_with_scores[:top_n]\n",
        "    \n",
        "    document_instances =  [Document(page_content=doc) for doc, _ in document_with_scores]\n",
        "    top_n_documents_with_scores = [DocumentWithScore(document=doc, score=score) for doc, score in document_with_scores]\n",
        "    \n",
        "    return document_instances, top_n_documents_with_scores\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57z5FNPXPZVR"
      },
      "source": [
        "### DistilBertTokenizer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "sbxGz4SQRwda"
      },
      "outputs": [],
      "source": [
        "from transformers import DistilBertTokenizer, DistilBertModel\n",
        "import torch\n",
        "\n",
        "def distil_bert(documents, query, ratio):\n",
        "  \n",
        "  tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "  model = DistilBertModel.from_pretrained('distilbert-base-uncased')\n",
        "\n",
        "  encoded_query = tokenizer(query, return_tensors='pt')\n",
        "\n",
        "  \n",
        "  with torch.no_grad():\n",
        "      query_embeddings = model(**encoded_query).last_hidden_state.mean(dim=1)\n",
        "\n",
        "\n",
        "  document_texts = [doc.page_content for doc in documents]\n",
        "\n",
        "  encoded_documents = tokenizer(document_texts, return_tensors='pt', padding=True, truncation=True)\n",
        "\n",
        "  with torch.no_grad():\n",
        "      document_embeddings = model(**encoded_documents).last_hidden_state.mean(dim=1)\n",
        "\n",
        "  similarity_scores = torch.nn.functional.cosine_similarity(query_embeddings, document_embeddings, dim=1)\n",
        "\n",
        "  ranked_documents = sorted(zip(documents, similarity_scores.tolist()), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "\n",
        "  top_n_documents_with_scores = ranked_documents[:int(len(documents)*ratio)]\n",
        "  \n",
        "  document_instances = [doc_tuple[0] for doc_tuple in top_n_documents_with_scores]\n",
        "  \n",
        "  top_n_documents_with_scores = [DocumentWithScore(document=doc.page_content, score=score)for doc, score in top_n_documents_with_scores]\n",
        "\n",
        "  return document_instances, top_n_documents_with_scores\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_1J0Cwyivbc"
      },
      "source": [
        "### Normalize Scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Cre9ORM9ixkj"
      },
      "outputs": [],
      "source": [
        "from collections import namedtuple\n",
        "\n",
        "DocumentWithScore = namedtuple('DocumentWithScore', ['document', 'score'])\n",
        "def normalize_scores(document_with_scores):\n",
        "    scores = [score for _, score in document_with_scores]\n",
        "   \n",
        "    score_range = max(scores) - min(scores)\n",
        "    if score_range == 0:\n",
        "        normalized_scores = [1.0] * len(scores)\n",
        "    else:\n",
        "        normalized_scores = [(score - min(scores)) / score_range for score in scores]\n",
        "\n",
        "    normalized_document_with_scores = [ DocumentWithScore(document=doc, score=normalized_score) for (doc, _), normalized_score in zip(document_with_scores, normalized_scores)]\n",
        "\n",
        "    return normalized_document_with_scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "egWUFjYkxz8H"
      },
      "source": [
        "### Combining retrievers and getting data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "4_l87zGLzqZY"
      },
      "outputs": [],
      "source": [
        "def filter_acc_ratio(ratio, result):\n",
        "  top_index = int(len(result) * ratio)\n",
        "  top_documents = result[:top_index]\n",
        "  return top_documents"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "sek9tmp3zYbY"
      },
      "outputs": [],
      "source": [
        "def combine(results):\n",
        "  combined_list = []\n",
        "  for sublist in results:\n",
        "    combined_list.extend(sublist)\n",
        "  return combined_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "E6RDcl2kwTzR"
      },
      "outputs": [],
      "source": [
        "from collections import OrderedDict\n",
        "def union(documents_list):\n",
        "  union_of_docs = list(OrderedDict.fromkeys(document.page_content for document in documents_list))\n",
        "  return union_of_docs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yaWBixdLymjt"
      },
      "source": [
        "### Putting retrieved profiles back in data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "ruqiFmAeyl1H"
      },
      "outputs": [],
      "source": [
        "def put_retrieved_profiles(union_of_docs, idx):\n",
        "  filtered_profiles = [profile for profile in data[idx]['profile'] if profile['text'] in union_of_docs]\n",
        "  copy_of_data = (data[idx]).copy()\n",
        "  copy_of_data['profile'] = filtered_profiles\n",
        "  return copy_of_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKN4kExIxfM7"
      },
      "source": [
        "### LM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "gqPODaVSq2_2"
      },
      "outputs": [],
      "source": [
        "\n",
        "input_text = \"Which category does this article relate to among the following categories? Just answer with the category name without further explanation. categories: [women, religion, politics, style & beauty, entertainment, culture & arts, sports, science & technology, travel, business, crime, education, healthy living, parents, food & drink] article: It's hard to find a restaurant that doesn't now place a little card at your table inquiring if the establishment was: (a) really awful; (b) tolerable; (c) sublime. You can use the following list to understand how to categorise: [{'text': 'The three make a trip of atypical opera themes, but no new opera brought the Met as much controversy as Klinghoffer.',   'category': 'culture & arts'.}, {'text': Though I might not subscribe to every prayer in the Siddur, I always use the synagogue time for my own prayer of thanks for being alive and the multitudes of blessings I enjoy.  I want to let the Lord know I haven't forgotten them.  Then, leaving the hall, the yarmulke still in place on my head, I head home feeling a little purer., 'category': 'religion', },{ 'text': 'I expected him to do well and show me some of his work. The money could be renewed for the second semester and the next year of college. Though I imagined that Mel would want to maintain periodic contact -- at least it was what I hoped -- I was wrong.', 'category': 'religion',}{ 'text': 'Although my mother swept away any feeling for her native land, I saw my trips as partly for her, maybe an effort to reconnect her to a land that only I wanted her to reconnect with.', 'category': 'travel'}]\"\n",
        "input_text_2 = \"Which category does this article relate to among the following categories? Just answer with the category name without further explanation. categories: [women, religion, politics, style & beauty, entertainment, culture & arts, sports, science & technology, travel, business, crime, education, healthy living, parents, food & drink] article: It's hard to find a restaurant that doesn't now place a little card at your table inquiring if the establishment was: (a) really awful; (b) tolerable; (c) sublime. \"\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
            "cf6c1d6737194dbc81d5d3dcea5d801f",
            "794611db3ff44bc7a8707b5329699237",
            "f75fe7736fe24546b539c85f045c67f8",
            "a6433419a1834d9c8adbc6425984161a",
            "69f5ab265bb24a7cb1ce5632dc821c7e",
            "8d820028db43471987e706abaa461eb2",
            "7545e454511541b2ab0e88d913ffd10d",
            "f8796baec2d04f5db6128a1385c8afb1",
            "433f31eb26054d979c4531567cfbeb5d",
            "ce34e466e5d34f2bad2bd68cc7e43b65",
            "c41fbff959d340d0b5cdce1100ab83a5",
            "5832b0913ba441329ecc2214b8c479ef",
            "86081549090548648680299b4e9b0d09",
            "d0f071fb5159402196b96d83bb20211c",
            "2f914891b5024cdcadce8fdaf981462d",
            "dd0ca2db20cb4aa2802d3415018efdf5",
            "a897f796f3164b3e818f22c164992854",
            "70364452a6f549f8ab26df09ba804b20",
            "2e5388ffecc3425fbde9d6f3198f4754",
            "7b61714bbf4b4da4993d04243f017bd9",
            "c2ad9ada7dd84a00824d4a760f669233",
            "3f69c1acbf574ef18a63c78af7b251d7",
            "a1a77e4b406f4684bb59dbfa7a831d41",
            "ffb76ab1948e4407962fa4a03bb41f91",
            "f624315b6c684e5bb32a7c02717ea053",
            "24ff92672d3d4c639f8657a626f8a752",
            "ae9ee2b833ce4a9abd1a1b014bcc025e",
            "0c435ffae37149ef96bd1ad178d9a194",
            "239b37eec18e440e9863ad053042fe65",
            "785450d6045e49bab5f7b43d92e8ec64",
            "b1ce115243fb4782865e27303e93fee3",
            "62f01e9667f1408dba91fcfe38b32e38",
            "80320bbbdf444d40819bf3446322459c",
            "decbaaafe3734a28acbaf12149f9e921",
            "1647c150ade543eab08c234b680bbd56",
            "a1293f6acbd1499b929bfa56453054d6",
            "921f4afaf9b14dbfa781b01a5da04315",
            "1dc80f1829274f64b1d32c9df8cc82dc",
            "eb66ac69ed5345fb8363b0e23851ac0c",
            "9e3f622f1d4b4e8c91bf3b8215fc467c",
            "3fbf62b3ddfc43078f3a9234e0daf3d5",
            "e23a854d347a4891ab225df9c96257b2",
            "cc2a4a25d6ed4a3580c34c48452a3d6b",
            "1697d3f8b89d49c8a2b47978a5117b4d",
            "75272880b5ef4509b4b1e657ef222eb5",
            "be5aa8f0baaf48d7afc3a8fdcedbe98e",
            "591f614705da46d98fa8e1ddc7d41d22",
            "98f29ee1d36c4a2ca9527229fe51a82f",
            "5dbd6894168949fb95435a5a0b68e0ce",
            "529b8191e38a419a9cc9fa0960c7374a",
            "d47f06277a7848c2a661d0c4198eaee2",
            "e3b75e7a3b8a4ec6a09199daa1123b10",
            "0c955a3c7f2c48b9a79a80b0df4cd15c",
            "fbdc2e9a16c94c189533434a6debfe6d",
            "ee2316f065694b49a4549190ea113fbc",
            "af102508091e4c2fbde9f2fd0c87a1c8",
            "9a867dde47cc4bb3ac5acc76b5ec1af7",
            "17e9b9105f4b4c159ee2933e7421ad8f",
            "80777327d4094f1db673a8ff2dd0a0a3",
            "6fd9120ce356433daf56f0a0659d386b",
            "ff07c47a361f41c0a01832570d4a2371",
            "b4fb726b88ad42318e78f98c890b3b36",
            "8c1a456dc19e4cdeb443f77f0bdb355b",
            "f3628888332e49febeac60318bcdbaba",
            "386a8a3f3a4544aabbeb4d7fb1ef546b",
            "b4bb929468584b8c8960120f038a7da0",
            "ab7cd46f093b45a099e719654e1e52f5",
            "81ed0b44fd3d4689a3df22d409288273",
            "b6d8ec1851bb41078e2ebafb5dc45368",
            "65edef749c0c41da9909475bd8819f35",
            "92c7b5b67b144a37926b4558b140d9fa",
            "e4f8169ca49c49f7924cfc713c8bad56",
            "5b4f928165be447f8a24ce95699163a8",
            "a956b59126d64e8c9fa19799af9b93a9",
            "1c6c030ec29d4c64b5591345df97a939",
            "a8e945c5e9154e5e8445fa5732bd25d6",
            "dc30551014684753a6be5c7dd7f77f96"
          ]
        },
        "id": "SZy6h_EpCBSu",
        "outputId": "96113ec0-4764-481c-c84e-8448e1e51485"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-large\")\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-large\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "gPiCP_1FB8m_"
      },
      "outputs": [],
      "source": [
        "def LM(original_data):\n",
        "\n",
        "  input_data = {\n",
        "    'input': original_data['input'],\n",
        "    'profile': [\n",
        "        {'text': profile['text'], 'category': profile['category']} for profile in original_data['profile']\n",
        "    ]\n",
        "}\n",
        "\n",
        "  question = input_data['input']\n",
        "  context = ' '.join([f\"{profile['category']}: {profile['text']}\" for profile in input_data['profile']])\n",
        "  input_text = f\"question: {question} context: {context}\"\n",
        "\n",
        " \n",
        "  inputs = tokenizer(input_text, return_tensors=\"pt\")\n",
        "  outputs = model.generate(**inputs)\n",
        "\n",
        "  \n",
        "  generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
        "  return generated_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MkY3RPp4Aun"
      },
      "source": [
        "### Majority Vote"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "-yI1L6t97Kh2"
      },
      "outputs": [],
      "source": [
        "def majority_vote(ratio, st, end):\n",
        "  mv_output = []\n",
        "  for idx in range(st, end):\n",
        "    \n",
        "    documents = get_docs(idx)\n",
        "    query = (data[idx]['input'])\n",
        "    index_of_query= (data[idx]['input']).find(\"article:\")\n",
        "\n",
        "    query = (data[idx]['input'])[index_of_query + len(\"article:\"):].strip()\n",
        "    bm25_result= bm25(documents, query, ratio)\n",
        "    bm25_result_docs = bm25_result[0]\n",
        "    tfidf_result = tfidf_retrieval_sklearn(documents, query, ratio)\n",
        "    tfidf_result_docs = tfidf_result[0]\n",
        "    contri_result = contrievermsmarco(documents,query, ratio)\n",
        "    contri_result_docs = contri_result[0]\n",
        "    all_top_docs = [bm25_result_docs, tfidf_result_docs, contri_result_docs]\n",
        "    all_top_docs = combine(all_top_docs)\n",
        "    all_top_docs = union(all_top_docs)\n",
        "    new_data_mv = put_retrieved_profiles(all_top_docs, idx)\n",
        "    val = LM(new_data_mv)\n",
        "    mv_output.append(val)\n",
        "  return mv_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cy4v7sOX3TQ0"
      },
      "source": [
        "### Borda count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "ILXIKu3Y3SRA"
      },
      "outputs": [],
      "source": [
        "def borda_count(ranked_lists):\n",
        "    borda_scores = {}\n",
        "\n",
        "   \n",
        "    for i, ranked_list in enumerate(ranked_lists):\n",
        "        for j, document in enumerate(ranked_list):\n",
        "            borda_scores[document] = borda_scores.get(document, 0) + (len(ranked_list) - j)\n",
        "\n",
        "    \n",
        "    final_ranking = sorted(borda_scores.keys(), key=lambda x: borda_scores[x], reverse=True)\n",
        "\n",
        "    return final_ranking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "Azkg7S2Y3hej"
      },
      "outputs": [],
      "source": [
        "def borda_count_fn(ratio, st, end):\n",
        "  bc_output = []\n",
        "  for idx in range(st, end):\n",
        "    documents = get_docs(idx)\n",
        "\n",
        "    query = (data[idx]['input'])\n",
        "    index_of_query= (data[idx]['input']).find(\"article:\")\n",
        "\n",
        "    query = (data[idx]['input'])[index_of_query + len(\"article:\"):].strip()\n",
        "    bm25_result= bm25(documents, query, ratio)\n",
        "    bm25_result_docs = bm25_result[0]\n",
        "    tfidf_result = tfidf_retrieval_sklearn(documents, query, ratio)\n",
        "    tfidf_result_docs = tfidf_result[0]\n",
        "    contri_result = contrievermsmarco(documents,query, ratio)\n",
        "    contri_result_docs = contri_result[0]\n",
        "    bm25_result_onlyDocs = [(doc.page_content) for doc in bm25_result_docs]\n",
        "    tfidf_result_onlyDocs = [(doc.page_content) for doc in tfidf_result_docs]\n",
        "    contri_result_onlyDocs = [(doc.page_content) for doc in contri_result_docs]\n",
        "\n",
        "    all_top_docs = [bm25_result_onlyDocs, tfidf_result_onlyDocs, contri_result_onlyDocs]\n",
        "    result = borda_count(all_top_docs)\n",
        "    \n",
        "    new_data_bc = put_retrieved_profiles(result, idx)\n",
        "    val = LM(new_data_bc)\n",
        "    bc_output.append(val)\n",
        "  return bc_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WAgj-Nbmrpa"
      },
      "source": [
        "### Scoring based"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbdNDY7ARvny"
      },
      "source": [
        "#### Parameters: average, weighted_average, max, min, product, rank_based,linear_combination\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "Fy4ORx1VpSXV"
      },
      "outputs": [],
      "source": [
        "def scoring_based(ratio, st, end, parameter):\n",
        "  scor_output = []\n",
        "  for idx in range(st, end):\n",
        "    documents = get_docs(idx)\n",
        "\n",
        "    query = (data[idx]['input'])\n",
        "    index_of_query= (data[idx]['input']).find(\"article:\")\n",
        "\n",
        "    query = (data[idx]['input'])[index_of_query + len(\"article:\"):].strip()\n",
        "    bm25_result= bm25(documents, query, ratio)\n",
        "    tfidf_result = tfidf_retrieval_sklearn(documents, query, ratio)\n",
        "    contri_result = contrievermsmarco(documents,query, ratio)\n",
        "    bm25_results_docScore = normalize_scores(bm25_result[1])\n",
        "    tfidf_results_docScore = normalize_scores(tfidf_result[1])\n",
        "    contri_results_docScore = normalize_scores(contri_result[1])\n",
        "\n",
        "    weight_bm25 = 0.4\n",
        "    weight_tfidf = 0.3\n",
        "    weight_contri = 0.3\n",
        "\n",
        "    average_scores = [(doc.score + tfidf_results_docScore[i].score + contri_results_docScore[i].score) / 3 for i, doc in enumerate(bm25_results_docScore)]\n",
        "    weighted_average_scores = [(doc.score * weight_bm25 + tfidf_results_docScore[i].score * weight_tfidf + contri_results_docScore[i].score * weight_contri) / 3 for i, doc in enumerate(bm25_results_docScore)]\n",
        "    max_scores = [max(doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score) for i, doc in enumerate(bm25_results_docScore)]\n",
        "    min_scores = [min(doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score) for i, doc in enumerate(bm25_results_docScore)]\n",
        "    product_scores = [doc.score * tfidf_results_docScore[i].score * contri_results_docScore[i].score for i, doc in enumerate(bm25_results_docScore)]\n",
        "    rank_based_scores = [\n",
        "      sum(\n",
        "          [\n",
        "              sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(doc.score),\n",
        "              sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(tfidf_results_docScore[i].score),\n",
        "              sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(contri_results_docScore[i].score)\n",
        "          ]\n",
        "      ) / 3\n",
        "      for i, doc in enumerate(bm25_results_docScore)\n",
        "  ]\n",
        "\n",
        "    linear_combination_scores = [(doc.score * weight_bm25 + tfidf_results_docScore[i].score * weight_tfidf + contri_results_docScore[i].score * weight_contri) for i, doc in enumerate(bm25_results_docScore)]\n",
        "\n",
        "    average_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, average_scores)]\n",
        "    weighted_average_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, weighted_average_scores)]\n",
        "    max_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, max_scores)]\n",
        "    min_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, min_scores)]\n",
        "    product_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, product_scores)]\n",
        "    rank_based_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, rank_based_scores)]\n",
        "    linear_combination_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, linear_combination_scores)]\n",
        "\n",
        "\n",
        "\n",
        "    sorted_average_scored_documents = sorted(average_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_weighted_average_scored_documents = sorted(weighted_average_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_max_scored_documents = sorted(max_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_min_scored_documents = sorted(min_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_product_scored_documents = sorted(product_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_rank_based_scored_documents = sorted(rank_based_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "    sorted_linear_combination_scored_documents = sorted(linear_combination_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "\n",
        "\n",
        "    sorted_average_scored_page_content = [doc_with_score.document for doc_with_score in sorted_average_scored_documents]\n",
        "\n",
        "    sorted_weighted_average_scored_page_content = [doc_with_score.document for doc_with_score in sorted_weighted_average_scored_documents]\n",
        "\n",
        "    sorted_max_scored_page_content = [doc_with_score.document for doc_with_score in sorted_max_scored_documents]\n",
        "\n",
        "    sorted_min_scored_page_content = [doc_with_score.document for doc_with_score in sorted_min_scored_documents]\n",
        "\n",
        "    sorted_product_scored_page_content = [doc_with_score.document for doc_with_score in sorted_product_scored_documents]\n",
        "\n",
        "    sorted_rank_based_scored_page_content = [doc_with_score.document for doc_with_score in sorted_rank_based_scored_documents]\n",
        "\n",
        "    sorted_linear_combination_scored_page_content = [doc_with_score.document for doc_with_score in sorted_linear_combination_scored_documents]\n",
        "\n",
        "    mapping = {\n",
        "    \"average\": sorted_average_scored_page_content,\n",
        "    \"weighted_average\": sorted_weighted_average_scored_page_content,\n",
        "    \"max\": sorted_max_scored_page_content,\n",
        "    \"min\": sorted_min_scored_page_content,\n",
        "    \"product\": sorted_product_scored_page_content,\n",
        "    \"rank_based\": sorted_rank_based_scored_page_content,\n",
        "    \"linear_combination\": sorted_linear_combination_scored_page_content\n",
        "    }\n",
        "\n",
        "    result = mapping[parameter] # change this accordingly\n",
        "    new_data_scor = put_retrieved_profiles(result, idx)\n",
        "    val = LM(new_data_scor)\n",
        "    scor_output.append(val)\n",
        "  return scor_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuowEHHhIJxi"
      },
      "source": [
        "### Re ranking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "-ZqEfs1SIJGv"
      },
      "outputs": [],
      "source": [
        "def re_rank_documents(bm25_results, query, percentage=0.5):\n",
        "  num_relevant_documents = int(len(bm25_results) * percentage)\n",
        "\n",
        "  relevant_bm25_documents = bm25_results[:num_relevant_documents]\n",
        "\n",
        "  if(len(relevant_bm25_documents) == 1):\n",
        "     re_ranked_results = relevant_bm25_documents\n",
        "  else:\n",
        "    relevant_bm25_documents = [Document(page_content=item.document) for item in relevant_bm25_documents]\n",
        "    contriever_results_for_queries = contrievermsmarco(relevant_bm25_documents, query, 1.0)\n",
        "    re_ranked_results = contriever_results_for_queries[1] \n",
        "   \n",
        "  re_ranked_results.sort(key=lambda x: x.score, reverse=True)\n",
        "  re_ranked_results_docs = [doc.document for doc in re_ranked_results]\n",
        "  return re_ranked_results_docs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "gwExZmSaXEoC"
      },
      "outputs": [],
      "source": [
        "def re_rank(ratio, st, end):\n",
        "  rr_output = []\n",
        "  for idx in range(st, end):\n",
        "    documents = get_docs(idx)\n",
        "\n",
        "    query = (data[idx]['input'])\n",
        "    index_of_query= (data[idx]['input']).find(\"article:\")\n",
        "\n",
        "    query = (data[idx]['input'])[index_of_query + len(\"article:\"):].strip()\n",
        "    bm25_result= bm25(documents, query, 0.6)\n",
        "    bm25_result_docs = bm25_result[1]\n",
        "    result = re_rank_documents(bm25_result_docs, query, 0.6)\n",
        "    \n",
        "    new_data_rr = put_retrieved_profiles(result, idx)\n",
        "    val = LM(new_data_rr)\n",
        "    rr_output.append(val)\n",
        "  return rr_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVOyzSBRFHO1"
      },
      "source": [
        "### True Output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "ELrAPGOzDvyA"
      },
      "outputs": [],
      "source": [
        "with urllib.request.urlopen(\"https://ciir.cs.umass.edu/downloads/LaMP/LaMP_2/train/train_outputs.json\") as url:\n",
        "    true_output = json.load(url)\n",
        "    true_output = true_output['golds']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Ensemble together"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {},
      "outputs": [],
      "source": [
        "def all_ensemble(rati0, st, end, parameter):\n",
        "    scor_output = []\n",
        "    bc_output = []\n",
        "    mv_output = []\n",
        "    for idx in range(st, end):\n",
        "        documents = get_docs(idx)\n",
        "\n",
        "        query = (data[idx]['input'])\n",
        "        index_of_query= (data[idx]['input']).find(\"article:\")\n",
        "\n",
        "        query = (data[idx]['input'])[index_of_query + len(\"article:\"):].strip()\n",
        "        bm25_result= bm25(documents, query, ratio)\n",
        "        tfidf_result = tfidf_retrieval_sklearn(documents, query, ratio)\n",
        "        contri_result = contrievermsmarco(documents,query, ratio)\n",
        "        bm25_results_docScore = normalize_scores(bm25_result[1])\n",
        "        tfidf_results_docScore = normalize_scores(tfidf_result[1])\n",
        "        contri_results_docScore = normalize_scores(contri_result[1])\n",
        "        bm25_result_docs = bm25_result[0]\n",
        "        tfidf_result_docs = tfidf_result[0]\n",
        "        contri_result_docs = contri_result[0]\n",
        "        bm25_result_onlyDocs = [(doc.page_content) for doc in bm25_result_docs]\n",
        "        tfidf_result_onlyDocs = [(doc.page_content) for doc in tfidf_result_docs]\n",
        "        contri_result_onlyDocs = [(doc.page_content) for doc in contri_result_docs]\n",
        "\n",
        "        all_top_docs = [bm25_result_onlyDocs, tfidf_result_onlyDocs, contri_result_onlyDocs]\n",
        "        bc_result = borda_count(all_top_docs)\n",
        "        new_data_bc = put_retrieved_profiles(bc_result, idx)\n",
        "        bc_val = LM(new_data_bc)\n",
        "        bc_output.append(bc_val)\n",
        "\n",
        "        \n",
        "        all_top_docs = [bm25_result_docs, tfidf_result_docs, contri_result_docs]\n",
        "        all_top_docs = combine(all_top_docs)\n",
        "        all_top_docs = union(all_top_docs)\n",
        "       \n",
        "        new_data_mv = put_retrieved_profiles(all_top_docs, idx)\n",
        "        mv_val = LM(new_data_mv)\n",
        "        mv_output.append(mv_val)\n",
        "\n",
        "        weight_bm25 = 0.4\n",
        "        weight_tfidf = 0.3\n",
        "        weight_contri = 0.3\n",
        "\n",
        "        average_scores = [(doc.score + tfidf_results_docScore[i].score + contri_results_docScore[i].score) / 3 for i, doc in enumerate(bm25_results_docScore)]\n",
        "        weighted_average_scores = [(doc.score * weight_bm25 + tfidf_results_docScore[i].score * weight_tfidf + contri_results_docScore[i].score * weight_contri) / 3 for i, doc in enumerate(bm25_results_docScore)]\n",
        "        max_scores = [max(doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score) for i, doc in enumerate(bm25_results_docScore)]\n",
        "        min_scores = [min(doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score) for i, doc in enumerate(bm25_results_docScore)]\n",
        "        product_scores = [doc.score * tfidf_results_docScore[i].score * contri_results_docScore[i].score for i, doc in enumerate(bm25_results_docScore)]\n",
        "        rank_based_scores = [\n",
        "        sum(\n",
        "            [\n",
        "                sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(doc.score),\n",
        "                sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(tfidf_results_docScore[i].score),\n",
        "                sorted([doc.score, tfidf_results_docScore[i].score, contri_results_docScore[i].score]).index(contri_results_docScore[i].score)\n",
        "            ]\n",
        "        ) / 3\n",
        "        for i, doc in enumerate(bm25_results_docScore)\n",
        "    ]\n",
        "\n",
        "        linear_combination_scores = [(doc.score * weight_bm25 + tfidf_results_docScore[i].score * weight_tfidf + contri_results_docScore[i].score * weight_contri) for i, doc in enumerate(bm25_results_docScore)]\n",
        "\n",
        "        average_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, average_scores)]\n",
        "        weighted_average_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, weighted_average_scores)]\n",
        "        max_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, max_scores)]\n",
        "        min_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, min_scores)]\n",
        "        product_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, product_scores)]\n",
        "        rank_based_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, rank_based_scores)]\n",
        "        linear_combination_scored_documents = [DocumentWithScore(document=doc.document, score=score) for doc, score in zip(bm25_results_docScore, linear_combination_scores)]\n",
        "\n",
        "\n",
        "\n",
        "        sorted_average_scored_documents = sorted(average_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_weighted_average_scored_documents = sorted(weighted_average_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_max_scored_documents = sorted(max_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_min_scored_documents = sorted(min_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_product_scored_documents = sorted(product_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_rank_based_scored_documents = sorted(rank_based_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "        sorted_linear_combination_scored_documents = sorted(linear_combination_scored_documents, key=lambda x: x.score, reverse=True)\n",
        "\n",
        "\n",
        "\n",
        "        sorted_average_scored_page_content = [doc_with_score.document for doc_with_score in sorted_average_scored_documents]\n",
        "\n",
        "        sorted_weighted_average_scored_page_content = [doc_with_score.document for doc_with_score in sorted_weighted_average_scored_documents]\n",
        "\n",
        "        sorted_max_scored_page_content = [doc_with_score.document for doc_with_score in sorted_max_scored_documents]\n",
        "\n",
        "        sorted_min_scored_page_content = [doc_with_score.document for doc_with_score in sorted_min_scored_documents]\n",
        "\n",
        "        sorted_product_scored_page_content = [doc_with_score.document for doc_with_score in sorted_product_scored_documents]\n",
        "\n",
        "        sorted_rank_based_scored_page_content = [doc_with_score.document for doc_with_score in sorted_rank_based_scored_documents]\n",
        "\n",
        "        sorted_linear_combination_scored_page_content = [doc_with_score.document for doc_with_score in sorted_linear_combination_scored_documents]\n",
        "\n",
        "        mapping = {\n",
        "        \"average\": sorted_average_scored_page_content,\n",
        "        \"weighted_average\": sorted_weighted_average_scored_page_content,\n",
        "        \"max\": sorted_max_scored_page_content,\n",
        "        \"min\": sorted_min_scored_page_content,\n",
        "        \"product\": sorted_product_scored_page_content,\n",
        "        \"rank_based\": sorted_rank_based_scored_page_content,\n",
        "        \"linear_combination\": sorted_linear_combination_scored_page_content\n",
        "        }\n",
        "\n",
        "        scor_result = mapping[parameter] # change this accordingly\n",
        "        new_data_scor = put_retrieved_profiles(scor_result, idx)\n",
        "        scor_val = LM(new_data_scor)\n",
        "        scor_output.append(scor_val)\n",
        "    return scor_output, bc_output, mv_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YC0bJpWqFPSl"
      },
      "source": [
        "### MAIN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "viA8FLHHDLBX",
        "outputId": "3946d5d5-eda1-42ce-88a1-93842aebcf73"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (777 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        }
      ],
      "source": [
        "scor_op, bc_op, mv_op = all_ensemble(ratio, start, end, \"average\")\n",
        "rr_op = re_rank(ratio, start, end)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {},
      "outputs": [],
      "source": [
        "scor_op_wtavg = scoring_based(ratio, start, end, \"weighted_average\")\n",
        "scor_op_max = scoring_based(ratio, start, end, \"max\")\n",
        "scor_op_min = scoring_based(ratio, start, end, \"min\")\n",
        "scor_op_prod = scoring_based(ratio, start, end, \"product\")\n",
        "scor_op_rb = scoring_based(ratio, start, end, \"rank_based\")\n",
        "scor_op_lc = scoring_based(ratio, start, end, \"linear_combination\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YS6dCtS10ItO"
      },
      "outputs": [],
      "source": [
        "dbert_output = []\n",
        "for idx in range(start, end):\n",
        "  documents = get_docs(idx)\n",
        "\n",
        "  query = (data[idx]['input'])\n",
        "  index_of_query= (data[idx]['input']).find(\"article:\")\n",
        "\n",
        "  query = (data[idx]['input'])[index_of_query + len(\"article:\"):].strip()\n",
        "  result = distil_bert(documents, query, ratio)\n",
        "  new_data_bc = put_retrieved_profiles(result, idx)\n",
        "  val = LM(new_data_bc)\n",
        "  dbert_output.append(val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score, classification_report, roc_curve, auc\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v-y48dXy1Bt2"
      },
      "outputs": [],
      "source": [
        "def process_item(item):\n",
        "    \n",
        "    processed_item = item.replace('[', '').replace(']', '')\n",
        "    return processed_item\n",
        "\n",
        "def matches(op, actual, start, end):\n",
        "  cnt = 0\n",
        "  for i in range(start, end):\n",
        "    op[i] = process_item(op[i])\n",
        "    if(op[i] == actual[i]):\n",
        "      cnt+=1\n",
        "  return cnt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "ename": "OSError",
          "evalue": "[Errno 63] File name too long: \"['religion', 'style & beauty', 'style & beauty', 'style & beauty', 'style & beauty', 'style & beauty', '[political]', 'religion', '[women]', '[women]', 'religion', 'religion', '[women]', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --']_d2_p1.json\"",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
            "\u001b[1;32m/Users/vaishnavishah/Documents/COMPSCI 646/project/646_project_dataset2_p1.ipynb Cell 59\u001b[0m line \u001b[0;36m1\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/vaishnavishah/Documents/COMPSCI%20646/project/646_project_dataset2_p1.ipynb#Y112sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39mfor\u001b[39;00m mod \u001b[39min\u001b[39;00m all_models:\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/vaishnavishah/Documents/COMPSCI%20646/project/646_project_dataset2_p1.ipynb#Y112sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m   op \u001b[39m=\u001b[39m {\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/vaishnavishah/Documents/COMPSCI%20646/project/646_project_dataset2_p1.ipynb#Y112sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m       \u001b[39m\"\u001b[39m\u001b[39mtask\u001b[39m\u001b[39m\"\u001b[39m: \u001b[39m\"\u001b[39m\u001b[39mLAMP_2\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/vaishnavishah/Documents/COMPSCI%20646/project/646_project_dataset2_p1.ipynb#Y112sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m       \u001b[39m\"\u001b[39m\u001b[39mgolds\u001b[39m\u001b[39m\"\u001b[39m: [{\u001b[39m\"\u001b[39m\u001b[39mid\u001b[39m\u001b[39m\"\u001b[39m: id_value, \u001b[39m\"\u001b[39m\u001b[39moutput\u001b[39m\u001b[39m\"\u001b[39m: output_value} \u001b[39mfor\u001b[39;00m id_value, output_value \u001b[39min\u001b[39;00m \u001b[39mzip\u001b[39m(ids, mod)]\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/vaishnavishah/Documents/COMPSCI%20646/project/646_project_dataset2_p1.ipynb#Y112sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m   }\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/vaishnavishah/Documents/COMPSCI%20646/project/646_project_dataset2_p1.ipynb#Y112sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m   \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(\u001b[39mf\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m{\u001b[39;49;00mmod\u001b[39m}\u001b[39;49;00m\u001b[39m_d2_p1.json\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39mw\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m json_file:\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/vaishnavishah/Documents/COMPSCI%20646/project/646_project_dataset2_p1.ipynb#Y112sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m       json\u001b[39m.\u001b[39mdump(op, json_file, indent\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m)\n",
            "File \u001b[0;32m~/Library/Python/3.11/lib/python/site-packages/IPython/core/interactiveshell.py:310\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[39mif\u001b[39;00m file \u001b[39min\u001b[39;00m {\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m}:\n\u001b[1;32m    304\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    305\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIPython won\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt let you open fd=\u001b[39m\u001b[39m{\u001b[39;00mfile\u001b[39m}\u001b[39;00m\u001b[39m by default \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    306\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    307\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39myou can use builtins\u001b[39m\u001b[39m'\u001b[39m\u001b[39m open.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    308\u001b[0m     )\n\u001b[0;32m--> 310\u001b[0m \u001b[39mreturn\u001b[39;00m io_open(file, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
            "\u001b[0;31mOSError\u001b[0m: [Errno 63] File name too long: \"['religion', 'style & beauty', 'style & beauty', 'style & beauty', 'style & beauty', 'style & beauty', '[political]', 'religion', '[women]', '[women]', 'religion', 'religion', '[women]', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty', 'style & beauty --', 'style & beauty --', 'style & beauty --', 'style & beauty --']_d2_p1.json\""
          ]
        }
      ],
      "source": [
        "ids = []\n",
        "for i in range(start, end):\n",
        "  ids.append(true_output[i]['id'])\n",
        "\n",
        "true_op = []\n",
        "for i in range(start, end):\n",
        "  true_op.append(true_output[i]['output'])\n",
        "  \n",
        "all_models = [mv_op, bc_op, scor_op, rr_op, dbert_output, true_op]\n",
        "all_models_str = [\"mv_op\", \"bc_op\", \"scor_op\", \"rr_op\", \"dbert_output\", \"true_op\"]\n",
        "\n",
        "for i in range(len(all_models)):\n",
        "  op = {\n",
        "      \"task\": \"LaMP_2\",\n",
        "      \"golds\": [{\"id\": id_value, \"output\": output_value} for id_value, output_value in zip(ids, all_models[i])]\n",
        "  }\n",
        "  with open(f'{all_models_str[i]}_d2_p1.json', 'w') as json_file:\n",
        "      json.dump(op, json_file, indent=2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Majority Vote Accuracy: 62.90%\n",
            "Borda Count Accuracy: 64.59%\n",
            "Re ranking Accuracy: 67.80%\n",
            "Scoring Based Accuracy: 68.16%\n",
            "Scoring Based wt avg: 70.40%\n",
            "Scoring Based max: 46.70%\n",
            "Scoring Based min: 68.90%\n",
            "Scoring Based lc: 64.20%\n",
            "Scoring Based prod: 55.84%\n",
            "Scoring Based rb: 63.70%\n",
            "Distil Bert Accuracy: 73.20\n"
          ]
        }
      ],
      "source": [
        "# print(\"Majority Vote Accuracy: \", accuracy_score(true_op, mv_op), \"%\")\n",
        "# print(\"Borda Count Accuracy: \",  accuracy_score(true_op, bc_op), \"%\")\n",
        "# print(\"Re ranking Accuracy: \", accuracy_score(true_op, rr_op), \"%\")\n",
        "# print(\"Scoring Based Accuracy: \",  accuracy_score(true_op, scor_op), \"%\")\n",
        "# print(\"Scoring Based wt avg: \", accuracy_score(true_op, scor_op_wtavg), \"%\")\n",
        "# print(\"Scoring Based max: \", accuracy_score(true_op, scor_op_max), \"%\")\n",
        "# print(\"Scoring Based min: \", accuracy_score(true_op, scor_op_min), \"%\")\n",
        "# print(\"Scoring Based lc: \",accuracy_score(true_op, scor_op_lc), \"%\")\n",
        "# print(\"Scoring Based prod: \", accuracy_score(true_op, scor_op_prod), \"%\")\n",
        "# print(\"Scoring Based rb: \", accuracy_score(true_op, scor_op_rb), \"%\")\n",
        "# print(\"Distil Bert Accuracy: \", accuracy_score(true_op, dbert_output))\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "N9vXVTPnxkwv",
        "gzicQux_xtPN",
        "hC-mK_ZixwfV",
        "YFM68zc3KQO8",
        "S_1J0Cwyivbc",
        "egWUFjYkxz8H",
        "yaWBixdLymjt",
        "VKN4kExIxfM7",
        "_MkY3RPp4Aun",
        "Cy4v7sOX3TQ0",
        "9WAgj-Nbmrpa",
        "XuowEHHhIJxi",
        "gVOyzSBRFHO1"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.6"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "0c435ffae37149ef96bd1ad178d9a194": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0c955a3c7f2c48b9a79a80b0df4cd15c": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "1647c150ade543eab08c234b680bbd56": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_eb66ac69ed5345fb8363b0e23851ac0c",
            "placeholder": "​",
            "style": "IPY_MODEL_9e3f622f1d4b4e8c91bf3b8215fc467c",
            "value": "tokenizer_config.json: 100%"
          }
        },
        "1697d3f8b89d49c8a2b47978a5117b4d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "17e9b9105f4b4c159ee2933e7421ad8f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8c1a456dc19e4cdeb443f77f0bdb355b",
            "max": 2424064,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_f3628888332e49febeac60318bcdbaba",
            "value": 2424064
          }
        },
        "1c6c030ec29d4c64b5591345df97a939": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "1dc80f1829274f64b1d32c9df8cc82dc": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "239b37eec18e440e9863ad053042fe65": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "24ff92672d3d4c639f8657a626f8a752": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_62f01e9667f1408dba91fcfe38b32e38",
            "placeholder": "​",
            "style": "IPY_MODEL_80320bbbdf444d40819bf3446322459c",
            "value": " 147/147 [00:00&lt;00:00, 8.07kB/s]"
          }
        },
        "2e5388ffecc3425fbde9d6f3198f4754": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2f914891b5024cdcadce8fdaf981462d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c2ad9ada7dd84a00824d4a760f669233",
            "placeholder": "​",
            "style": "IPY_MODEL_3f69c1acbf574ef18a63c78af7b251d7",
            "value": " 3.13G/3.13G [00:39&lt;00:00, 29.3MB/s]"
          }
        },
        "386a8a3f3a4544aabbeb4d7fb1ef546b": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3f69c1acbf574ef18a63c78af7b251d7": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "3fbf62b3ddfc43078f3a9234e0daf3d5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "433f31eb26054d979c4531567cfbeb5d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "529b8191e38a419a9cc9fa0960c7374a": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "5832b0913ba441329ecc2214b8c479ef": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_86081549090548648680299b4e9b0d09",
              "IPY_MODEL_d0f071fb5159402196b96d83bb20211c",
              "IPY_MODEL_2f914891b5024cdcadce8fdaf981462d"
            ],
            "layout": "IPY_MODEL_dd0ca2db20cb4aa2802d3415018efdf5"
          }
        },
        "591f614705da46d98fa8e1ddc7d41d22": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e3b75e7a3b8a4ec6a09199daa1123b10",
            "max": 791656,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_0c955a3c7f2c48b9a79a80b0df4cd15c",
            "value": 791656
          }
        },
        "5b4f928165be447f8a24ce95699163a8": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5dbd6894168949fb95435a5a0b68e0ce": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "62f01e9667f1408dba91fcfe38b32e38": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "65edef749c0c41da9909475bd8819f35": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a8e945c5e9154e5e8445fa5732bd25d6",
            "placeholder": "​",
            "style": "IPY_MODEL_dc30551014684753a6be5c7dd7f77f96",
            "value": " 2.20k/2.20k [00:00&lt;00:00, 162kB/s]"
          }
        },
        "69f5ab265bb24a7cb1ce5632dc821c7e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6fd9120ce356433daf56f0a0659d386b": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "70364452a6f549f8ab26df09ba804b20": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "75272880b5ef4509b4b1e657ef222eb5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_be5aa8f0baaf48d7afc3a8fdcedbe98e",
              "IPY_MODEL_591f614705da46d98fa8e1ddc7d41d22",
              "IPY_MODEL_98f29ee1d36c4a2ca9527229fe51a82f"
            ],
            "layout": "IPY_MODEL_5dbd6894168949fb95435a5a0b68e0ce"
          }
        },
        "7545e454511541b2ab0e88d913ffd10d": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "785450d6045e49bab5f7b43d92e8ec64": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "794611db3ff44bc7a8707b5329699237": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8d820028db43471987e706abaa461eb2",
            "placeholder": "​",
            "style": "IPY_MODEL_7545e454511541b2ab0e88d913ffd10d",
            "value": "config.json: 100%"
          }
        },
        "7b61714bbf4b4da4993d04243f017bd9": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "80320bbbdf444d40819bf3446322459c": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "80777327d4094f1db673a8ff2dd0a0a3": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_386a8a3f3a4544aabbeb4d7fb1ef546b",
            "placeholder": "​",
            "style": "IPY_MODEL_b4bb929468584b8c8960120f038a7da0",
            "value": " 2.42M/2.42M [00:00&lt;00:00, 18.5MB/s]"
          }
        },
        "81ed0b44fd3d4689a3df22d409288273": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e4f8169ca49c49f7924cfc713c8bad56",
            "placeholder": "​",
            "style": "IPY_MODEL_5b4f928165be447f8a24ce95699163a8",
            "value": "special_tokens_map.json: 100%"
          }
        },
        "86081549090548648680299b4e9b0d09": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a897f796f3164b3e818f22c164992854",
            "placeholder": "​",
            "style": "IPY_MODEL_70364452a6f549f8ab26df09ba804b20",
            "value": "model.safetensors: 100%"
          }
        },
        "8c1a456dc19e4cdeb443f77f0bdb355b": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "8d820028db43471987e706abaa461eb2": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "921f4afaf9b14dbfa781b01a5da04315": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cc2a4a25d6ed4a3580c34c48452a3d6b",
            "placeholder": "​",
            "style": "IPY_MODEL_1697d3f8b89d49c8a2b47978a5117b4d",
            "value": " 2.54k/2.54k [00:00&lt;00:00, 148kB/s]"
          }
        },
        "92c7b5b67b144a37926b4558b140d9fa": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "98f29ee1d36c4a2ca9527229fe51a82f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_fbdc2e9a16c94c189533434a6debfe6d",
            "placeholder": "​",
            "style": "IPY_MODEL_ee2316f065694b49a4549190ea113fbc",
            "value": " 792k/792k [00:00&lt;00:00, 42.4MB/s]"
          }
        },
        "9a867dde47cc4bb3ac5acc76b5ec1af7": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ff07c47a361f41c0a01832570d4a2371",
            "placeholder": "​",
            "style": "IPY_MODEL_b4fb726b88ad42318e78f98c890b3b36",
            "value": "tokenizer.json: 100%"
          }
        },
        "9e3f622f1d4b4e8c91bf3b8215fc467c": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "a1293f6acbd1499b929bfa56453054d6": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3fbf62b3ddfc43078f3a9234e0daf3d5",
            "max": 2539,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_e23a854d347a4891ab225df9c96257b2",
            "value": 2539
          }
        },
        "a1a77e4b406f4684bb59dbfa7a831d41": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_ffb76ab1948e4407962fa4a03bb41f91",
              "IPY_MODEL_f624315b6c684e5bb32a7c02717ea053",
              "IPY_MODEL_24ff92672d3d4c639f8657a626f8a752"
            ],
            "layout": "IPY_MODEL_ae9ee2b833ce4a9abd1a1b014bcc025e"
          }
        },
        "a6433419a1834d9c8adbc6425984161a": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_ce34e466e5d34f2bad2bd68cc7e43b65",
            "placeholder": "​",
            "style": "IPY_MODEL_c41fbff959d340d0b5cdce1100ab83a5",
            "value": " 662/662 [00:00&lt;00:00, 45.9kB/s]"
          }
        },
        "a897f796f3164b3e818f22c164992854": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a8e945c5e9154e5e8445fa5732bd25d6": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a956b59126d64e8c9fa19799af9b93a9": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ab7cd46f093b45a099e719654e1e52f5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_81ed0b44fd3d4689a3df22d409288273",
              "IPY_MODEL_b6d8ec1851bb41078e2ebafb5dc45368",
              "IPY_MODEL_65edef749c0c41da9909475bd8819f35"
            ],
            "layout": "IPY_MODEL_92c7b5b67b144a37926b4558b140d9fa"
          }
        },
        "ae9ee2b833ce4a9abd1a1b014bcc025e": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "af102508091e4c2fbde9f2fd0c87a1c8": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_9a867dde47cc4bb3ac5acc76b5ec1af7",
              "IPY_MODEL_17e9b9105f4b4c159ee2933e7421ad8f",
              "IPY_MODEL_80777327d4094f1db673a8ff2dd0a0a3"
            ],
            "layout": "IPY_MODEL_6fd9120ce356433daf56f0a0659d386b"
          }
        },
        "b1ce115243fb4782865e27303e93fee3": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "b4bb929468584b8c8960120f038a7da0": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "b4fb726b88ad42318e78f98c890b3b36": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "b6d8ec1851bb41078e2ebafb5dc45368": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a956b59126d64e8c9fa19799af9b93a9",
            "max": 2201,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_1c6c030ec29d4c64b5591345df97a939",
            "value": 2201
          }
        },
        "be5aa8f0baaf48d7afc3a8fdcedbe98e": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_529b8191e38a419a9cc9fa0960c7374a",
            "placeholder": "​",
            "style": "IPY_MODEL_d47f06277a7848c2a661d0c4198eaee2",
            "value": "spiece.model: 100%"
          }
        },
        "c2ad9ada7dd84a00824d4a760f669233": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c41fbff959d340d0b5cdce1100ab83a5": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "cc2a4a25d6ed4a3580c34c48452a3d6b": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ce34e466e5d34f2bad2bd68cc7e43b65": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cf6c1d6737194dbc81d5d3dcea5d801f": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_794611db3ff44bc7a8707b5329699237",
              "IPY_MODEL_f75fe7736fe24546b539c85f045c67f8",
              "IPY_MODEL_a6433419a1834d9c8adbc6425984161a"
            ],
            "layout": "IPY_MODEL_69f5ab265bb24a7cb1ce5632dc821c7e"
          }
        },
        "d0f071fb5159402196b96d83bb20211c": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_2e5388ffecc3425fbde9d6f3198f4754",
            "max": 3132668804,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_7b61714bbf4b4da4993d04243f017bd9",
            "value": 3132668804
          }
        },
        "d47f06277a7848c2a661d0c4198eaee2": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "dc30551014684753a6be5c7dd7f77f96": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "dd0ca2db20cb4aa2802d3415018efdf5": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "decbaaafe3734a28acbaf12149f9e921": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HBoxModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_1647c150ade543eab08c234b680bbd56",
              "IPY_MODEL_a1293f6acbd1499b929bfa56453054d6",
              "IPY_MODEL_921f4afaf9b14dbfa781b01a5da04315"
            ],
            "layout": "IPY_MODEL_1dc80f1829274f64b1d32c9df8cc82dc"
          }
        },
        "e23a854d347a4891ab225df9c96257b2": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "e3b75e7a3b8a4ec6a09199daa1123b10": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e4f8169ca49c49f7924cfc713c8bad56": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "eb66ac69ed5345fb8363b0e23851ac0c": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ee2316f065694b49a4549190ea113fbc": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f3628888332e49febeac60318bcdbaba": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "ProgressStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "f624315b6c684e5bb32a7c02717ea053": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_785450d6045e49bab5f7b43d92e8ec64",
            "max": 147,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_b1ce115243fb4782865e27303e93fee3",
            "value": 147
          }
        },
        "f75fe7736fe24546b539c85f045c67f8": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "FloatProgressModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f8796baec2d04f5db6128a1385c8afb1",
            "max": 662,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_433f31eb26054d979c4531567cfbeb5d",
            "value": 662
          }
        },
        "f8796baec2d04f5db6128a1385c8afb1": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "fbdc2e9a16c94c189533434a6debfe6d": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ff07c47a361f41c0a01832570d4a2371": {
          "model_module": "@jupyter-widgets/base",
          "model_module_version": "1.2.0",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ffb76ab1948e4407962fa4a03bb41f91": {
          "model_module": "@jupyter-widgets/controls",
          "model_module_version": "1.5.0",
          "model_name": "HTMLModel",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0c435ffae37149ef96bd1ad178d9a194",
            "placeholder": "​",
            "style": "IPY_MODEL_239b37eec18e440e9863ad053042fe65",
            "value": "generation_config.json: 100%"
          }
        }
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}