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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oso6Zv0ri1_v"
      },
      "source": [
        "### QA. SQUAD dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KR36WdFBi7IE"
      },
      "source": [
        "You can work with Squad datasets (any other available in hugging face) in different formats. \n",
        "* via library datasets\n",
        "* via already prepared pipelines\n",
        "* directly with data loaders to classify and finetune. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bBRvZQhgNMp"
      },
      "source": [
        "!pip install datasets\n",
        "!pip install transformers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "p5eU9VYkg1uM",
        "outputId": "99f18b87-b5ec-44ec-e8f2-f46264230feb"
      },
      "source": [
        "from datasets import list_datasets, load_dataset, list_metrics, load_metric\n",
        "from pprint import pprint\n",
        "\n",
        "datasets = list_datasets()\n",
        "metrics = list_metrics()\n",
        "\n",
        "# Print all the available datasets\n",
        "pprint(datasets[:10], compact=True)\n",
        "# List all the available metrics\n",
        "pprint(metrics, compact=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['assin', 'ar_res_reviews', 'ambig_qa', 'bianet', 'ag_news', 'ajgt_twitter_ar',\n",
            " 'aeslc', 'bc2gm_corpus', 'air_dialogue', 'acronym_identification']\n",
            "['accuracy', 'bertscore', 'bleu', 'bleurt', 'cer', 'chrf', 'code_eval', 'comet',\n",
            " 'competition_math', 'coval', 'cuad', 'exact_match', 'f1', 'frugalscore',\n",
            " 'gleu', 'glue', 'google_bleu', 'indic_glue', 'mae', 'mahalanobis',\n",
            " 'matthews_correlation', 'mauve', 'mean_iou', 'meteor', 'mse', 'pearsonr',\n",
            " 'perplexity', 'precision', 'recall', 'rouge', 'sacrebleu', 'sari', 'seqeval',\n",
            " 'spearmanr', 'squad', 'squad_v2', 'super_glue', 'ter', 'wer', 'wiki_split',\n",
            " 'xnli', 'xtreme_s']\n"
          ]
        }
      ]
    },
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          "height": 66,
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      "source": [
        "# Load a dataset and print the first example in the training set\n",
        "squad_dataset = load_dataset('squad')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Reusing dataset squad (/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\n"
          ]
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "  0%|          | 0/2 [00:00<?, ?it/s]"
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    {
      "cell_type": "code",
      "source": [
        "print(squad_dataset[\"train\"])"
      ],
      "metadata": {
        "id": "NZelsUbmddRA",
        "outputId": "b5f7b2d3-5bd5-42c8-f857-40eb92a818e1",
        "colab": {
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        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset({\n",
            "    features: ['id', 'title', 'context', 'question', 'answers'],\n",
            "    num_rows: 87599\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load a metric\n",
        "squad_metric = load_metric('squad')"
      ],
      "metadata": {
        "id": "uLla1TWkdtYP"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Process the dataset - add a column with the length of the context texts\n",
        "dataset_with_length = squad_dataset.map(lambda x: {\"length\": len(x[\"context\"])})"
      ],
      "metadata": {
        "id": "s2cRSgeAdeel",
        "outputId": "b538bf70-e8d1-4bc4-c9e9-ee8d3fca18df",
        "colab": {
          "base_uri": "https://localhost:8080/"
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      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453/cache-43e42ded5a4064cc.arrow\n",
            "Loading cached processed dataset at /root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453/cache-359b86046b00fe4b.arrow\n"
          ]
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        },
        "id": "HSn_IaymIoze",
        "outputId": "f6a45f9b-5fb6-418a-902a-f8d3c1c9aa21"
      },
      "source": [
        "# Process the dataset - tokenize the context texts\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')\n",
        "tokenized_dataset = squad_dataset.map(lambda x: tokenizer(x['context']), batched=True)\n",
        "tokenized_dataset[\"train\"]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
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        },
        {
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          "metadata": {}
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        {
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          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (539 > 512). Running this sequence through the model will result in indexing errors\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['id', 'title', 'context', 'question', 'answers', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "    num_rows: 87599\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpNx2yyQhnsI",
        "outputId": "0f143c3c-5358-4f75-cc34-b54b3d6bf727"
      },
      "source": [
        "squad_dataset = list_datasets(with_details=True)[datasets.index('squad')]\n",
        "\n",
        "pprint(squad_dataset.__dict__)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'author': None,\n",
            " 'cardData': {'annotations_creators': ['crowdsourced'],\n",
            "              'language_creators': ['crowdsourced', 'found'],\n",
            "              'languages': ['en'],\n",
            "              'licenses': ['cc-by-4-0'],\n",
            "              'multilinguality': ['monolingual'],\n",
            "              'paperswithcode_id': 'squad',\n",
            "              'pretty_name': 'SQuAD',\n",
            "              'size_categories': ['10K<n<100K'],\n",
            "              'source_datasets': ['extended|wikipedia'],\n",
            "              'task_categories': ['question-answering'],\n",
            "              'task_ids': ['extractive-qa']},\n",
            " 'citation': '@article{2016arXiv160605250R,\\n'\n",
            "             '       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and '\n",
            "             '{Lopyrev},\\n'\n",
            "             '                 Konstantin and {Liang}, Percy},\\n'\n",
            "             '        title = \"{SQuAD: 100,000+ Questions for Machine '\n",
            "             'Comprehension of Text}\",\\n'\n",
            "             '      journal = {arXiv e-prints},\\n'\n",
            "             '         year = 2016,\\n'\n",
            "             '          eid = {arXiv:1606.05250},\\n'\n",
            "             '        pages = {arXiv:1606.05250},\\n'\n",
            "             'archivePrefix = {arXiv},\\n'\n",
            "             '       eprint = {1606.05250},\\n'\n",
            "             '}',\n",
            " 'description': 'Stanford Question Answering Dataset (SQuAD) is a reading '\n",
            "                'comprehension dataset, consisting of questions posed by '\n",
            "                'crowdworkers on a set of Wikipedia articles, where the answer '\n",
            "                'to every question is a segment of text, or span, from the '\n",
            "                'corresponding reading passage, or the question might be '\n",
            "                'unanswerable.',\n",
            " 'downloads': 175033,\n",
            " 'gated': False,\n",
            " 'id': 'squad',\n",
            " 'lastModified': '2022-01-25T15:51:34.000Z',\n",
            " 'likes': 23,\n",
            " 'paperswithcode_id': 'squad',\n",
            " 'private': False,\n",
            " 'sha': '7b739d9ed70d2d68642e7473dbcbf2244c2dbd82',\n",
            " 'siblings': None,\n",
            " 'tags': ['pretty_name:SQuAD',\n",
            "          'annotations_creators:crowdsourced',\n",
            "          'language_creators:crowdsourced',\n",
            "          'language_creators:found',\n",
            "          'languages:en',\n",
            "          'licenses:cc-by-4-0',\n",
            "          'multilinguality:monolingual',\n",
            "          'size_categories:10K<n<100K',\n",
            "          'source_datasets:extended|wikipedia',\n",
            "          'task_categories:question-answering',\n",
            "          'task_ids:extractive-qa']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "z3_BdGsghvX0",
        "outputId": "a1dadf40-5c29-44a4-d824-eb2ed581e9d1"
      },
      "source": [
        "import torch \n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset('squad')\n",
        "\n",
        "def get_correct_alignement(context, answer):\n",
        "    \"\"\" Some original examples in SQuAD have indices wrong by 1 or 2 character. We test and fix this here. \"\"\"\n",
        "    gold_text = answer['text'][0]\n",
        "    start_idx = answer['answer_start'][0]\n",
        "    end_idx = start_idx + len(gold_text)\n",
        "    if context[start_idx:end_idx] == gold_text:\n",
        "        return start_idx, end_idx       # When the gold label position is good\n",
        "    elif context[start_idx-1:end_idx-1] == gold_text:\n",
        "        return start_idx-1, end_idx-1   # When the gold label is off by one character\n",
        "    elif context[start_idx-2:end_idx-2] == gold_text:\n",
        "        return start_idx-2, end_idx-2   # When the gold label is off by two character\n",
        "    else:\n",
        "        raise ValueError()\n",
        "\n",
        "# Tokenize our training dataset\n",
        "def convert_to_features(example_batch):\n",
        "    # Tokenize contexts and questions (as pairs of inputs)\n",
        "    encodings = tokenizer(example_batch['context'], example_batch['question'], truncation=True)\n",
        "\n",
        "    # Compute start and end tokens for labels using Transformers's fast tokenizers alignement methods.\n",
        "    start_positions, end_positions = [], []\n",
        "    for i, (context, answer) in enumerate(zip(example_batch['context'], example_batch['answers'])):\n",
        "        start_idx, end_idx = get_correct_alignement(context, answer)\n",
        "        start_positions.append(encodings.char_to_token(i, start_idx))\n",
        "        end_positions.append(encodings.char_to_token(i, end_idx-1))\n",
        "    encodings.update({'start_positions': start_positions, 'end_positions': end_positions})\n",
        "    return encodings\n",
        "\n",
        "encoded_dataset = dataset.map(convert_to_features, batched=True)\n",
        "\n",
        "# Format our dataset to outputs torch.Tensor to train a pytorch model\n",
        "columns = ['input_ids', 'token_type_ids', 'attention_mask', 'start_positions', 'end_positions']\n",
        "encoded_dataset.set_format(type='torch', columns=columns)\n",
        "\n",
        "# Instantiate a PyTorch Dataloader around our dataset\n",
        "# Let's do dynamic batching (pad on the fly with our own collate_fn)\n",
        "def collate_fn(examples):\n",
        "    return tokenizer.pad(examples, return_tensors='pt')\n",
        "dataloader = torch.utils.data.DataLoader(encoded_dataset['train'], collate_fn=collate_fn, batch_size=8)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Reusing dataset squad (/root/.cache/huggingface/datasets/squad/plain_text/1.0.0/d6ec3ceb99ca480ce37cdd35555d6cb2511d223b9150cce08a837ef62ffea453)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4adc1a3ea01f41278a21f48a9a57d2f3"
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          },
          "metadata": {}
        },
        {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o11qelMdiKXW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "b4a429c7c8b54db6a10899500bd57c13",
            "c88d67929d814da59f85e32b98bceaba",
            "a30f64b185ab472695fd4b3814ec3549",
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        },
        "outputId": "7ecbc726-97b6-4831-862e-835c1067ecfa"
      },
      "source": [
        "from transformers import BertForQuestionAnswering\n",
        "import torch\n",
        "\n",
        "# Bert Model with a span classification head on top for extractive question-answering tasks \n",
        "# a linear layers on top of the hidden-states output to compute span start logits and span end logits\n",
        "model = BertForQuestionAnswering.from_pretrained('bert-base-cased', return_dict=True)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/416M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b4a429c7c8b54db6a10899500bd57c13"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForQuestionAnswering: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight']\n",
            "- This IS expected if you are initializing BertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForQuestionAnswering were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fPavoHIiiPas",
        "outputId": "76d90d13-1e99-48b7-a38f-4300cd9ee679",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Now let's train our model\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "model.train().to(device)\n",
        "for i, batch in enumerate(dataloader):\n",
        "    batch.to(device)\n",
        "    outputs = model(**batch)\n",
        "    loss = outputs.loss\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    model.zero_grad()\n",
        "    print(f'Step {i} - loss: {loss:.3}')\n",
        "    if i > 3:\n",
        "        break"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Step 0 - loss: 5.75\n",
            "Step 1 - loss: 5.74\n",
            "Step 2 - loss: 5.13\n",
            "Step 3 - loss: 5.68\n",
            "Step 4 - loss: 5.46\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ba8qwscGiRez"
      },
      "source": [
        "#### pipelines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194,
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        },
        "id": "pmLWWiUKfEzT",
        "outputId": "a737289a-d7d0-4c23-a5a7-a34abc356cc2"
      },
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "nlp = pipeline(\"question-answering\")\n",
        "\n",
        "context = r\"\"\"\n",
        "Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a\n",
        "question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune\n",
        "a model on a SQuAD task, you may leverage the examples/question-answering/run_squad.py script.\n",
        "\"\"\""
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert-base-cased-distilled-squad (https://huggingface.co/distilbert-base-cased-distilled-squad)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
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              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading:   0%|          | 0.00/249M [00:00<?, ?B/s]"
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        },
        {
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              "Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/208k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57d065c178b54354bbcb6609138029c5"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/426k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "795196628395445bbab868e80e116f21"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wdWNEkVTVdJO",
        "outputId": "942fd4ca-bf22-4580-ffca-cfe04de3b35e"
      },
      "source": [
        "result = nlp(question=\"What is extractive question answering?\", context=context)\n",
        "print(f\"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")\n",
        "\n",
        "result = nlp(question=\"What is a good example of a question answering dataset?\", context=context)\n",
        "print(f\"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}\")"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Answer: 'the task of extracting an answer from a text given a question', score: 0.6226, start: 34, end: 95\n",
            "Answer: 'SQuAD dataset', score: 0.5053, start: 147, end: 160\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CG2wOgefmne"
      },
      "source": [
        "Here is an example of question answering using a model and a tokenizer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "peX37rUEffcf",
        "colab": {
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          ]
        },
        "outputId": "63c1e7e0-9e4f-4e1c-da7e-9ca6f09855b9"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n",
        "import torch\n",
        "\n",
        "# Instantiate a tokenizer and a model from the checkpoint name. The model is identified as a BERT model and loads it with the weights stored in the checkpoint.\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
        "model = AutoModelForQuestionAnswering.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
        "\n",
        "\n",
        "# Define a text and a few questions.\n",
        "text = r\"\"\"\n",
        "🤗 Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides general-purpose\n",
        "architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural\n",
        "Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between\n",
        "TensorFlow 2.0 and PyTorch.\n",
        "\"\"\"\n",
        "\n",
        "questions = [\n",
        "     \"How many pretrained models are available in 🤗 Transformers?\",\n",
        "     \"What does 🤗 Transformers provide?\",\n",
        "     \"🤗 Transformers provides interoperability between which frameworks?\",\n",
        "]\n",
        "\n",
        "# Iterate over the questions and build a sequence from the text and the current question, \n",
        "# with the correct model-specific separators token type ids and attention masks.\n",
        "for question in questions:\n",
        "     inputs = tokenizer(question, text, add_special_tokens=True, return_tensors=\"pt\")\n",
        "     input_ids = inputs[\"input_ids\"].tolist()[0]\n",
        "\n",
        "     # Pass this sequence through the model. This outputs a range of scores across the entire\n",
        "     # sequence tokens (question and text), for both the start and end positions.\n",
        "     outputs = model(**inputs)\n",
        "     answer_start_scores = outputs.start_logits\n",
        "     answer_end_scores = outputs.end_logits\n",
        "\n",
        "     # Compute the softmax of the result to get probabilities over the tokens.\n",
        "     answer_start = torch.argmax(\n",
        "         answer_start_scores\n",
        "     )  # Get the most likely beginning of answer with the argmax of the score\n",
        "     answer_end = torch.argmax(answer_end_scores) + 1  # Get the most likely end of answer with the argmax of the score\n",
        "     answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))\n",
        "\n",
        "     print(f\"Question: {question}\")\n",
        "     print(f\"Answer: {answer}\")"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/443 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
            ],
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.25G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question: How many pretrained models are available in 🤗 Transformers?\n",
            "Answer: over 32 +\n",
            "Question: What does 🤗 Transformers provide?\n",
            "Answer: general - purpose architectures\n",
            "Question: 🤗 Transformers provides interoperability between which frameworks?\n",
            "Answer: tensorflow 2. 0 and pytorch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N5sUSMgDizxr"
      },
      "source": [
        "### Russian QA models\n",
        "\n",
        "You can you DeepPavlov models for Russian QA.\n",
        "* For SQAUD dataset http://docs.deeppavlov.ai/en/master/features/models/squad.html\n",
        "* For KBQA http://docs.deeppavlov.ai/en/master/features/models/kbqa.html\n",
        "* Fir ODQA http://docs.deeppavlov.ai/en/master/features/skills/odqa.html\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kta68kHEimuu"
      },
      "source": [
        "!pip install deeppavlov"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Restrt runtime here"
      ],
      "metadata": {
        "id": "C1xf6-gSf_x0"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f8tf9_EsjO2O"
      },
      "source": [
        "# Demo is here https://demo.deeppavlov.ai/#/ru/textqa\n",
        "\n",
        "!python -m deeppavlov install squad_ru_rubert_infer\n",
        "# !python -m deeppavlov download squad_ru_rubert_infer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBZk7bDomhC_"
      },
      "source": [
        "!pip install --upgrade tensorflow-gpu==2.2.0 --user\n",
        "!pip uninstall tensorflow-datasets\n",
        "!pip install tensorflow-datasets==4.0.0\n",
        "# reconnect colab"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Restrt runtime here"
      ],
      "metadata": {
        "id": "YVl_GwENgTqu"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-icJSppfjw4m"
      },
      "source": [
        "from deeppavlov import build_model, configs\n",
        "\n",
        "model = build_model(configs.squad.squad_ru_rubert_infer, download=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res = model(['DeepPavlov это библиотека для NLP задач и постороения диалоговых систем.'], ['Что такое DeepPavlov?'])"
      ],
      "metadata": {
        "id": "NwsPFSz5gXK6"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res"
      ],
      "metadata": {
        "id": "J0WlZrrjgZyM",
        "outputId": "2d6d4199-6b0f-45a8-ad37-418a21159f07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['библиотека для NLP задач и постороения диалоговых систем'],\n",
              " [15],\n",
              " [1729423.0]]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "37MKaCi21VvZ",
        "outputId": "356cc7ef-f3bb-450c-879b-3f8ef7fbc6fe"
      },
      "source": [
        "!git clone https://github.com/deepmipt/DeepPavlov.git"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'DeepPavlov'...\n",
            "remote: Enumerating objects: 62067, done.\u001b[K\n",
            "remote: Counting objects: 100% (861/861), done.\u001b[K\n",
            "remote: Compressing objects: 100% (534/534), done.\u001b[K\n",
            "remote: Total 62067 (delta 592), reused 463 (delta 320), pack-reused 61206\u001b[K\n",
            "Receiving objects: 100% (62067/62067), 38.82 MiB | 21.21 MiB/s, done.\n",
            "Resolving deltas: 100% (47741/47741), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rHFrU0umpwy",
        "outputId": "d0b5d83a-e28d-43d3-e5ce-5a226417f91e"
      },
      "source": [
        "!cat ./DeepPavlov/deeppavlov/configs/squad/squad_ru_rubert_infer.json"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"dataset_reader\": {\n",
            "    \"class_name\": \"squad_dataset_reader\",\n",
            "    \"dataset\": \"SberSQuADClean\",\n",
            "    \"url\": \"http://files.deeppavlov.ai/datasets/sber_squad_clean-v1.1.tar.gz\",\n",
            "    \"data_path\": \"{DOWNLOADS_PATH}/squad_ru_clean/\"\n",
            "  },\n",
            "  \"dataset_iterator\": {\n",
            "    \"class_name\": \"squad_iterator\",\n",
            "    \"seed\": 1337,\n",
            "    \"shuffle\": true\n",
            "  },\n",
            "  \"chainer\": {\n",
            "    \"in\": [\"context_raw\", \"question_raw\"],\n",
            "    \"in_y\": [\"ans_raw\", \"ans_raw_start\"],\n",
            "    \"pipe\": [\n",
            "      {\n",
            "        \"class_name\": \"squad_bert_infer\",\n",
            "        \"lang\": \"ru\",\n",
            "        \"batch_size\": 128,\n",
            "        \"squad_model_config\": \"{CONFIGS_PATH}/squad/squad_ru_rubert.json\",\n",
            "        \"vocab_file\": \"{DOWNLOADS_PATH}/bert_models/rubert_cased_L-12_H-768_A-12_v1/vocab.txt\",\n",
            "        \"do_lower_case\": false,\n",
            "        \"max_seq_length\": 256,\n",
            "        \"in\": [\"context_raw\", \"question_raw\"],\n",
            "        \"out\": [\"ans_predicted\", \"ans_start_predicted\", \"logits\"]\n",
            "      }\n",
            "    ],\n",
            "    \"out\": [\"ans_predicted\", \"ans_start_predicted\", \"logits\"]\n",
            "  },\n",
            "  \"train\": {\n",
            "    \"show_examples\": false,\n",
            "    \"test_best\": false,\n",
            "    \"validate_best\": true,\n",
            "    \"log_every_n_batches\": 250,\n",
            "    \"val_every_n_batches\": 500,\n",
            "    \"batch_size\": 10,\n",
            "    \"pytest_max_batches\": 2,\n",
            "    \"validation_patience\": 10,\n",
            "    \"metrics\": [\n",
            "      {\n",
            "        \"name\": \"squad_v2_f1\",\n",
            "        \"inputs\": [\"ans_raw\", \"ans_predicted\"]\n",
            "      },\n",
            "      {\n",
            "        \"name\": \"squad_v2_em\",\n",
            "        \"inputs\": [\"ans_raw\", \"ans_predicted\"]\n",
            "      },\n",
            "      {\n",
            "        \"name\": \"squad_v1_f1\",\n",
            "        \"inputs\": [\"ans_raw\", \"ans_predicted\"]\n",
            "      },\n",
            "      {\n",
            "        \"name\": \"squad_v1_em\",\n",
            "        \"inputs\": [\"ans_raw\", \"ans_predicted\"]\n",
            "      }\n",
            "    ]\n",
            "  },\n",
            "  \"metadata\": {\n",
            "    \"variables\": {\n",
            "      \"ROOT_PATH\": \"~/.deeppavlov\",\n",
            "      \"DOWNLOADS_PATH\": \"{ROOT_PATH}/downloads\",\n",
            "      \"MODELS_PATH\": \"{ROOT_PATH}/models\",\n",
            "      \"CONFIGS_PATH\": \"{DEEPPAVLOV_PATH}/configs\"\n",
            "    },\n",
            "    \"download\": [\n",
            "      {\n",
            "        \"url\": \"http://files.deeppavlov.ai/deeppavlov_data/bert/rubert_cased_L-12_H-768_A-12_v1.tar.gz\",\n",
            "        \"subdir\": \"{DOWNLOADS_PATH}/bert_models\"\n",
            "      },\n",
            "      {\n",
            "        \"url\": \"http://files.deeppavlov.ai/deeppavlov_data/squad_model_ru_rubert.tar.gz\",\n",
            "        \"subdir\": \"{MODELS_PATH}\"\n",
            "      }\n",
            "    ]\n",
            "  }\n",
            "}\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "puS1leOTLBSJ"
      },
      "source": [
        "# Training. Check config!\n",
        "from deeppavlov import train_model \n",
        "\n",
        "model = train_model(\"./DeepPavlov/deeppavlov/configs/squad/squad_ru_rubert_infer.json\", download=True)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "9oF22W1wgfYT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}