Question_Answer_Application.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wGMWbz1IcvP_"
   },
   "source": [
    "### Question Answer Application\n",
    "The goal of Question Answering is to find the answer to a question given a question and an accompanying context. The predicted answer will be either a span of text from the context or an empty string (indicating the question cannot be answered from the context)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
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     "height": 1000
    },
    "id": "GClIr_-qX6OL",
    "outputId": "b75526a4-081e-4baa-9853-8dd20c27885a"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.8.1)\n",
      "Requirement already satisfied: notebook>=4.4.1 in /usr/local/lib/python3.7/dist-packages (from widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (5.3.1)\n",
      "Requirement already satisfied: jupyter-core in /usr/local/lib/python3.7/dist-packages (from nbformat>=4.2.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (4.7.1)\n",
      "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.7/dist-packages (from jupyter-client->ipykernel>=5.1.2; python_version >= \"3.4\"->pydeck>=0.1.dev5->streamlit->simpletransformers) (22.0.3)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.7.0)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=4.0.0; python_version >= \"3.3\"->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.2.5)\n",
      "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.10.0)\n",
      "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (1.5.0)\n",
      "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (5.6.1)\n",
      "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (1.4.3)\n",
      "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (3.3.0)\n",
      "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.7.1)\n",
      "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.0)\n",
      "Requirement already satisfied: mistune<2,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.8.4)\n",
      "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.1)\n",
      "Building wheels for collected packages: seqeval, blinker, subprocess32, pathtools\n",
      "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for seqeval: filename=seqeval-1.2.2-cp37-none-any.whl size=16172 sha256=6ce447af50e56210de246f069f2da9c8161f4a5928dcd48dc961e05eee47c2bf\n",
      "  Stored in directory: /root/.cache/pip/wheels/52/df/1b/45d75646c37428f7e626214704a0e35bd3cfc32eda37e59e5f\n",
      "  Building wheel for blinker (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for blinker: filename=blinker-1.4-cp37-none-any.whl size=13448 sha256=769c30e20f601a60ead8bdf552a593b27ed09008b50ba67be92d8241b38031bd\n",
      "  Stored in directory: /root/.cache/pip/wheels/92/a0/00/8690a57883956a301d91cf4ec999cc0b258b01e3f548f86e89\n",
      "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for subprocess32: filename=subprocess32-3.5.4-cp37-none-any.whl size=6489 sha256=08d0d6af0349a4458713ddec6d01a0037ac0c3f2e2266bd25535b65465f00f81\n",
      "  Stored in directory: /root/.cache/pip/wheels/68/39/1a/5e402bdfdf004af1786c8b853fd92f8c4a04f22aad179654d1\n",
      "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pathtools: filename=pathtools-0.1.2-cp37-none-any.whl size=8786 sha256=c49e5852d18a7dc8a1617bec9549c6151bf47d4cf9947a7e1b7ce1455cd07472\n",
      "  Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n",
      "Successfully built seqeval blinker subprocess32 pathtools\n",
      "\u001b[31mERROR: google-colab 1.0.0 has requirement ipykernel~=4.10, but you'll have ipykernel 5.5.5 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: datasets 1.6.2 has requirement tqdm<4.50.0,>=4.27, but you'll have tqdm 4.61.0 which is incompatible.\u001b[0m\n",
      "\u001b[31mERROR: transformers 4.6.1 has requirement huggingface-hub==0.0.8, but you'll have huggingface-hub 0.0.9 which is incompatible.\u001b[0m\n",
      "Installing collected packages: smmap, gitdb, gitpython, ipykernel, pydeck, validators, base58, watchdog, blinker, streamlit, tokenizers, tensorboardx, sentry-sdk, configparser, subprocess32, pathtools, shortuuid, docker-pycreds, wandb, seqeval, tqdm, fsspec, xxhash, huggingface-hub, datasets, sacremoses, transformers, sentencepiece, simpletransformers\n",
      "  Found existing installation: ipykernel 4.10.1\n",
      "    Uninstalling ipykernel-4.10.1:\n",
      "      Successfully uninstalled ipykernel-4.10.1\n",
      "  Found existing installation: tqdm 4.41.1\n",
      "    Uninstalling tqdm-4.41.1:\n",
      "      Successfully uninstalled tqdm-4.41.1\n",
      "Successfully installed base58-2.1.0 blinker-1.4 configparser-5.0.2 datasets-1.6.2 docker-pycreds-0.4.0 fsspec-2021.5.0 gitdb-4.0.7 gitpython-3.1.17 huggingface-hub-0.0.9 ipykernel-5.5.5 pathtools-0.1.2 pydeck-0.6.2 sacremoses-0.0.45 sentencepiece-0.1.95 sentry-sdk-1.1.0 seqeval-1.2.2 shortuuid-1.0.1 simpletransformers-0.61.5 smmap-4.0.0 streamlit-0.82.0 subprocess32-3.5.4 tensorboardx-2.2 tokenizers-0.10.3 tqdm-4.61.0 transformers-4.6.1 validators-0.18.2 wandb-0.10.30 watchdog-2.1.2 xxhash-2.0.2\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "ipykernel"
        ]
       }
      }
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "!pip install simpletransformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "3SZo6mRnYivr"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "with open(r\"train.json\", \"r\") as read_file:\n",
    "    train = json.load(read_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zoiQlW1lZCzu",
    "outputId": "0f532876-f0e0-4ff2-dad6-789181390ca9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'context': 'Mistborn is a series of epic fantasy novels written by American author Brandon Sanderson.',\n",
       "  'qas': [{'answers': [{'answer_start': 71, 'text': 'Brandon Sanderson'}],\n",
       "    'id': '00001',\n",
       "    'is_impossible': False,\n",
       "    'question': 'Who is the author of the Mistborn series?'}]},\n",
       " {'context': 'The first series, published between 2006 and 2008, consists of The Final Empire,The Well of Ascension, and The Hero of Ages.',\n",
       "  'qas': [{'answers': [{'answer_start': 28, 'text': 'between 2006 and 2008'}],\n",
       "    'id': '00002',\n",
       "    'is_impossible': False,\n",
       "    'question': 'When was the series published?'},\n",
       "   {'answers': [{'answer_start': 63,\n",
       "      'text': 'The Final Empire, The Well of Ascension, and The Hero of Ages'}],\n",
       "    'id': '00003',\n",
       "    'is_impossible': False,\n",
       "    'question': 'What are the three books in the series?'},\n",
       "   {'answers': [],\n",
       "    'id': '00004',\n",
       "    'is_impossible': True,\n",
       "    'question': 'Who is the main character in the series?'}]}]"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "Zcf9395xZD7N"
   },
   "outputs": [],
   "source": [
    "with open(r\"test.json\", \"r\") as read_file:\n",
    "    test = json.load(read_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ENWqJL1eZH4e",
    "outputId": "2de4f972-7c17-4a47-ea45-cc72691719bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'context': 'The series primarily takes place in a region called the Final Empire on a world called Scadrial, where the sun and sky are red, vegetation is brown, and the ground is constantly being covered under black volcanic ashfalls.',\n",
       "  'qas': [{'answers': [{'answer_start': 38,\n",
       "      'text': 'region called the Final Empire'},\n",
       "     {'answer_start': 74, 'text': 'world called Scadrial'}],\n",
       "    'id': '00001',\n",
       "    'is_impossible': False,\n",
       "    'question': 'Where does the series take place?'}]},\n",
       " {'context': '\"Mistings\" have only one of the many Allomantic powers, while \"Mistborns\" have all the powers.',\n",
       "  'qas': [{'answers': [{'answer_start': 21, 'text': 'one'}],\n",
       "    'id': '00002',\n",
       "    'is_impossible': False,\n",
       "    'question': 'How many powers does a Misting possess?'},\n",
       "   {'answers': [],\n",
       "    'id': '00003',\n",
       "    'is_impossible': True,\n",
       "    'question': 'What are Allomantic powers?'}]}]"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "x-ybl95CeEwo"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "1fYyYz_cZI7v"
   },
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "from simpletransformers.question_answering import QuestionAnsweringModel, QuestionAnsweringArgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "id": "o2GXJIZshpoz"
   },
   "outputs": [],
   "source": [
    "model_type=\"bert\"\n",
    "model_name= \"bert-base-cased\"\n",
    "if model_type == \"bert\":\n",
    "    model_name = \"bert-base-cased\"\n",
    "\n",
    "elif model_type == \"roberta\":\n",
    "    model_name = \"roberta-base\"\n",
    "\n",
    "elif model_type == \"distilbert\":\n",
    "    model_name = \"distilbert-base-cased\"\n",
    "\n",
    "elif model_type == \"distilroberta\":\n",
    "    model_type = \"roberta\"\n",
    "    model_name = \"distilroberta-base\"\n",
    "\n",
    "elif model_type == \"electra-base\":\n",
    "    model_type = \"electra\"\n",
    "    model_name = \"google/electra-base-discriminator\"\n",
    "\n",
    "elif model_type == \"electra-small\":\n",
    "    model_type = \"electra\"\n",
    "    model_name = \"google/electra-small-discriminator\"\n",
    "\n",
    "elif model_type == \"xlnet\":\n",
    "    model_name = \"xlnet-base-cased\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "QRdwLRNdZWWd"
   },
   "outputs": [],
   "source": [
    "# Configure the model \n",
    "model_args = QuestionAnsweringArgs()\n",
    "model_args.train_batch_size = 16\n",
    "model_args.evaluate_during_training = True\n",
    "model_args.n_best_size=3\n",
    "model_args.num_train_epochs=5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "id": "d6BA_Dr4iIsI"
   },
   "outputs": [],
   "source": [
    "### Advanced Methodology\n",
    "train_args = {\n",
    "    \"reprocess_input_data\": True,\n",
    "    \"overwrite_output_dir\": True,\n",
    "    \"use_cached_eval_features\": True,\n",
    "    \"output_dir\": f\"outputs/{model_type}\",\n",
    "    \"best_model_dir\": f\"outputs/{model_type}/best_model\",\n",
    "    \"evaluate_during_training\": True,\n",
    "    \"max_seq_length\": 128,\n",
    "    \"num_train_epochs\": 5,\n",
    "    \"evaluate_during_training_steps\": 1000,\n",
    "    \"wandb_project\": \"Question Answer Application\",\n",
    "    \"wandb_kwargs\": {\"name\": model_name},\n",
    "    \"save_model_every_epoch\": False,\n",
    "    \"save_eval_checkpoints\": False,\n",
    "    \"n_best_size\":3\n",
    "    # \"use_early_stopping\": True,\n",
    "    # \"early_stopping_metric\": \"mcc\",\n",
    "    # \"n_gpu\": 2,\n",
    "    # \"manual_seed\": 4,\n",
    "    # \"use_multiprocessing\": False,\n",
    "    \"train_batch_size\": 128,\n",
    "    \"eval_batch_size\": 64,\n",
    "    # \"config\": {\n",
    "    #     \"output_hidden_states\": True\n",
    "    # }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l03UlJEeZdWs",
    "outputId": "9243c235-f1c1-400d-8857-cf7bf6a86e39"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-cased were not used when initializing BertForQuestionAnswering: ['cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.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"
     ]
    }
   ],
   "source": [
    "model = QuestionAnsweringModel(\n",
    "    model_type,model_name, args=train_args\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "id": "OqZs8sp-amuQ"
   },
   "outputs": [],
   "source": [
    "### Remove output folder\n",
    "!rm -rf outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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      "\n",
      "convert squad examples to features:   0%|          | 0/4 [00:00<?, ?it/s]\u001b[ACould not find answer: 'The Final Empire,The Well of Ascension, and The Hero of Ages.' vs. 'The Final Empire, The Well of Ascension, and The Hero of Ages'\n",
      "convert squad examples to features: 100%|██████████| 4/4 [00:00<00:00, 395.83it/s]\n",
      "\n",
      "add example index and unique id: 100%|██████████| 4/4 [00:00<00:00, 18275.83it/s]\n"
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       "                Project page: <a href=\"https://wandb.ai/krishnaik06/Question%20Answer%20Application\" target=\"_blank\">https://wandb.ai/krishnaik06/Question%20Answer%20Application</a><br/>\n",
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      "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
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       "Running Epoch 3 of 5:   0%|          | 0/1 [00:00<?, ?it/s]"
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       "Running Evaluation:   0%|          | 0/1 [00:00<?, ?it/s]"
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       "Running Epoch 4 of 5:   0%|          | 0/1 [00:00<?, ?it/s]"
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       "Running Evaluation:   0%|          | 0/1 [00:00<?, ?it/s]"
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    {
     "data": {
      "text/plain": [
       "(5,\n",
       " {'correct': [0, 0, 0, 0, 0],\n",
       "  'eval_loss': [-0.2003173828125,\n",
       "   -0.260009765625,\n",
       "   -0.296630859375,\n",
       "   -0.320556640625,\n",
       "   -0.33251953125],\n",
       "  'global_step': [1, 2, 3, 4, 5],\n",
       "  'incorrect': [1, 1, 1, 1, 1],\n",
       "  'similar': [2, 2, 2, 2, 2],\n",
       "  'train_loss': [5.065276145935059,\n",
       "   5.1174397468566895,\n",
       "   4.639975547790527,\n",
       "   4.307891845703125,\n",
       "   3.9419758319854736]})"
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     "execution_count": 76,
     "metadata": {
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   ],
   "source": [
    "# Train the model\n",
    "model.train_model(train, eval_data=test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
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     "height": 49,
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    "id": "ACoBHtQ1ZoKX",
    "outputId": "aefa814d-5fcb-4f0d-bb12-9a57d434cd56"
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   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fbbbe43b0b4941869ad2dfafed686be3",
       "version_major": 2,
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      "text/plain": [
       "Running Evaluation:   0%|          | 0/1 [00:00<?, ?it/s]"
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   ],
   "source": [
    "# Evaluate the model\n",
    "result, texts = model.eval_model(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "QHHaVaD_Z9mM"
   },
   "outputs": [],
   "source": [
    "# Make predictions with the model\n",
    "to_predict = [\n",
    "    {\n",
    "        \"context\": \"Vin is a Mistborn of great power and skill.\",\n",
    "        \"qas\": [\n",
    "            {\n",
    "                \"question\": \"What is Vin's speciality?\",\n",
    "                \"id\": \"0\",\n",
    "            }\n",
    "        ],\n",
    "    }\n",
    "]"
   ]
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  {
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    "id": "MHi55xYHaCzB",
    "outputId": "02fdea22-4ac2-46b3-e8ea-22284d716c8e"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "convert squad examples to features: 100%|██████████| 1/1 [00:00<00:00, 4744.69it/s]\n",
      "\n",
      "add example index and unique id: 100%|██████████| 1/1 [00:00<00:00, 658.45it/s]\n"
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