Question_Anser_Application.ipynb

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          "text": [
            "Collecting simpletransformers\n",
            "  Downloading simpletransformers-0.63.11-py3-none-any.whl (250 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m250.7/250.7 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from simpletransformers) (1.22.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from simpletransformers) (2.27.1)\n",
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            "\u001b[?25hBuilding wheels for collected packages: seqeval, validators, pathtools\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16165 sha256=b1861fffbd353ce2972c04afcd3fd2b203dfa392ba87e261477a7beedaa2af98\n",
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            "Installing collected packages: tokenizers, sentencepiece, safetensors, pathtools, xxhash, watchdog, validators, tzdata, smmap, setproctitle, sentry-sdk, pympler, docker-pycreds, dill, pytz-deprecation-shim, pydeck, multiprocess, huggingface-hub, gitdb, tzlocal, transformers, seqeval, GitPython, wandb, streamlit, datasets, simpletransformers\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open(r\"train.json\",\"r\") as read_file:\n",
        "    train = json.load(read_file)"
      ],
      "metadata": {
        "id": "JBDvZVXLZwxM"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qi0lHy_oaTd8",
        "outputId": "55095179-fa47-477e-80f8-33125e7fd501"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'context': 'Mistborn is a series of epic fantasy novels written by American author Brandon Sanderson.',\n",
              "  'qas': [{'id': '00001',\n",
              "    'is_impossible': False,\n",
              "    'question': 'Who is the author of the Mistborn series?',\n",
              "    'answers': [{'text': 'Brandon Sanderson', 'answer_start': 71}]}]},\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': [{'id': '00002',\n",
              "    'is_impossible': False,\n",
              "    'question': 'When was the series published?',\n",
              "    'answers': [{'text': 'between 2006 and 2008', 'answer_start': 28}]},\n",
              "   {'id': '00003',\n",
              "    'is_impossible': False,\n",
              "    'question': 'What are the three books in the series?',\n",
              "    'answers': [{'text': 'The Final Empire, The Well of Ascension, and The Hero of Ages',\n",
              "      'answer_start': 63}]},\n",
              "   {'id': '00004',\n",
              "    'is_impossible': True,\n",
              "    'question': 'Who is the main character in the series?',\n",
              "    'answers': []}]}]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(r\"test.json\",\"r\") as read_file:\n",
        "    test = json.load(read_file)"
      ],
      "metadata": {
        "id": "wDIFsZPvaUzm"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUPvJ4mAaYHH",
        "outputId": "f49855a0-5500-4322-92c3-f78607e55459"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "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': [{'id': '00001',\n",
              "    'is_impossible': False,\n",
              "    'question': 'Where does the series take place?',\n",
              "    'answers': [{'text': 'region called the Final Empire', 'answer_start': 38},\n",
              "     {'text': 'world called Scadrial', 'answer_start': 74}]}]},\n",
              " {'context': '\"Mistings\" have only one of the many Allomantic powers, while \"Mistborns\" have all the powers.',\n",
              "  'qas': [{'id': '00002',\n",
              "    'is_impossible': False,\n",
              "    'question': 'How many powers does a Misting possess?',\n",
              "    'answers': [{'text': 'one', 'answer_start': 21}]},\n",
              "   {'id': '00003',\n",
              "    'is_impossible': True,\n",
              "    'question': 'What are Allomantic powers?',\n",
              "    'answers': []}]}]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(r\"prediction.json\",\"r\") as read_file:\n",
        "    prediction = json.load(read_file)"
      ],
      "metadata": {
        "id": "OfYKNxEqaZ3-"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "107GblyOah5s",
        "outputId": "457a2960-9940-4b61-ff2c-9af5ba32443e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'context': 'Vin is a Mistborn of great power and skill.',\n",
              "  'qas': [{'question': \"What is Vin's speciality?\", 'id': '0'}]}]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import logging\n",
        "\n",
        "from simpletransformers.question_answering import QuestionAnsweringModel, QuestionAnsweringArgs"
      ],
      "metadata": {
        "id": "KX-DDL-XalNA"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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-discrimator\"\n",
        "\n",
        "elif model_type == \"xlnet\":\n",
        "  model_name = \"xlnet-base-cased\""
      ],
      "metadata": {
        "id": "bh2jL8NlbmPR"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Configure the model\n",
        "\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"
      ],
      "metadata": {
        "id": "nxzwZDUtrAuL"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install wandb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MmRHfqhg3G-Y",
        "outputId": "d3f4d41e-a203-4049-c789-3f008df5cdb3"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: wandb in /usr/local/lib/python3.10/dist-packages (0.15.8)\n",
            "Requirement already satisfied: Click!=8.0.0,>=7.1 in /usr/local/lib/python3.10/dist-packages (from wandb) (8.1.6)\n",
            "Requirement already satisfied: GitPython!=3.1.29,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.1.32)\n",
            "Requirement already satisfied: requests<3,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (2.27.1)\n",
            "Requirement already satisfied: psutil>=5.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (5.9.5)\n",
            "Requirement already satisfied: sentry-sdk>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (1.29.2)\n",
            "Requirement already satisfied: docker-pycreds>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (0.4.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from wandb) (6.0.1)\n",
            "Requirement already satisfied: pathtools in /usr/local/lib/python3.10/dist-packages (from wandb) (0.1.2)\n",
            "Requirement already satisfied: setproctitle in /usr/local/lib/python3.10/dist-packages (from wandb) (1.3.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from wandb) (67.7.2)\n",
            "Requirement already satisfied: appdirs>=1.4.3 in /usr/local/lib/python3.10/dist-packages (from wandb) (1.4.4)\n",
            "Requirement already satisfied: protobuf!=4.21.0,<5,>=3.19.0 in /usr/local/lib/python3.10/dist-packages (from wandb) (3.20.3)\n",
            "Requirement already satisfied: six>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from docker-pycreds>=0.4.0->wandb) (1.16.0)\n",
            "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.10/dist-packages (from GitPython!=3.1.29,>=1.0.0->wandb) (4.0.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2023.7.22)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.0.0->wandb) (3.4)\n",
            "Requirement already satisfied: smmap<6,>=3.0.1 in /usr/local/lib/python3.10/dist-packages (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb) (5.0.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "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\" : 20,\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_stopped\" : 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",
        "}"
      ],
      "metadata": {
        "id": "bWQR2q1etNoQ"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = QuestionAnsweringModel(\n",
        "    model_type, model_name, args=train_args\n",
        "\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvxS_eQP7r5g",
        "outputId": "c4d9585c-362a-41a5-e6c0-f3359562dc1d"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of BertForQuestionAnswering were not initialized from the model checkpoint at bert-base-cased and are newly initialized: ['qa_outputs.bias', 'qa_outputs.weight']\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",
      "source": [
        " ### Remove output folder\n",
        "!rm -rf outputs"
      ],
      "metadata": {
        "id": "SMD1coLhcjh5"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model.train_model(train, eval_data=test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        "id": "Dr-RTvd_eOay",
        "outputId": "80310d8a-1eae-4640-98e9-8731447b4a63"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "convert squad examples to features: 100%|██████████| 4/4 [00:00<00:00, 89.79it/s]\n",
            "add example index and unique id: 100%|██████████| 4/4 [00:00<00:00, 7799.73it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/20 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "efeedd45172344b38e4fbb2f3c25530d"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Finishing last run (ID:mpkrde5m) before initializing another..."
            ]
          },
          "metadata": {}
        },
        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style>\n",
              "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
              "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
              "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
              "    </style>\n",
              "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>correct</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>eval_loss</td><td>█▇▆▅▄▃▂▁▁▁</td></tr><tr><td>global_step</td><td>▁▂▃▃▄▅▆▆▇█</td></tr><tr><td>incorrect</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>similar</td><td>▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train_loss</td><td>██▆▄▄▂▂▂▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>correct</td><td>0</td></tr><tr><td>eval_loss</td><td>-0.85449</td></tr><tr><td>global_step</td><td>10</td></tr><tr><td>incorrect</td><td>0</td></tr><tr><td>similar</td><td>3</td></tr><tr><td>train_loss</td><td>1.0098</td></tr></table><br/></div></div>"
            ]
          },
          "metadata": {}
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        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run <strong style=\"color:#cdcd00\">bert-base-cased</strong> at: <a href='https://wandb.ai/hritikasharma4321/Question%20Answer%20Application/runs/mpkrde5m' target=\"_blank\">https://wandb.ai/hritikasharma4321/Question%20Answer%20Application/runs/mpkrde5m</a><br/>Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Find logs at: <code>./wandb/run-20230804_124027-mpkrde5m/logs</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Successfully finished last run (ID:mpkrde5m). Initializing new run:<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.15.8"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20230804_124933-lv2o6e09</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/hritikasharma4321/Question%20Answer%20Application/runs/lv2o6e09' target=\"_blank\">bert-base-cased</a></strong> to <a href='https://wandb.ai/hritikasharma4321/Question%20Answer%20Application' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/hritikasharma4321/Question%20Answer%20Application' target=\"_blank\">https://wandb.ai/hritikasharma4321/Question%20Answer%20Application</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/hritikasharma4321/Question%20Answer%20Application/runs/lv2o6e09' target=\"_blank\">https://wandb.ai/hritikasharma4321/Question%20Answer%20Application/runs/lv2o6e09</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "Running Epoch 0 of 20:   0%|          | 0/1 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
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            }
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          "metadata": {}
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            }
          },
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        {
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            }
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          "data": {
            "text/plain": [
              "Running Epoch 14 of 20:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "de8c846e4a884d2faaf47ea7154b9196"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Evaluation:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "537e31a4b61449ce9b16f7e42a0d32d9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 15 of 20:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bf3b504d6b8c495f899c6884b79981e2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "296505b2f8e446818afe6e4a9795ed7b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 16 of 20:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "81284e6bec1d4666b0bb327198e58ebc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0d01db67f0f46479083c3f79ac27da8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 17 of 20:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8acc412258754fdc97fbe81006125370"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "c3ba2fb050d042dc89c0282ce2d3bd05"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 18 of 20:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bb5717a0b1e14f2d8c9598353742e92c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "425d55bc4f754326b711da2062d648f9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 19 of 20:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "72a21d5150da4b35b717f0230d41a763"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Evaluation:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c631bba95214519b92084cf47b8fc12"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20,\n",
              " {'global_step': [1,\n",
              "   2,\n",
              "   3,\n",
              "   4,\n",
              "   5,\n",
              "   6,\n",
              "   7,\n",
              "   8,\n",
              "   9,\n",
              "   10,\n",
              "   11,\n",
              "   12,\n",
              "   13,\n",
              "   14,\n",
              "   15,\n",
              "   16,\n",
              "   17,\n",
              "   18,\n",
              "   19,\n",
              "   20],\n",
              "  'correct': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
              "  'similar': [1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
              "  'incorrect': [2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n",
              "  'train_loss': [4.80029296875,\n",
              "   4.8193359375,\n",
              "   4.56884765625,\n",
              "   4.01513671875,\n",
              "   3.562255859375,\n",
              "   3.057861328125,\n",
              "   2.586181640625,\n",
              "   2.142578125,\n",
              "   2.0701904296875,\n",
              "   1.70001220703125,\n",
              "   1.52471923828125,\n",
              "   1.48248291015625,\n",
              "   1.232269287109375,\n",
              "   1.303009033203125,\n",
              "   1.225189208984375,\n",
              "   1.1467437744140625,\n",
              "   0.995819091796875,\n",
              "   1.02581787109375,\n",
              "   0.9703369140625,\n",
              "   1.125732421875],\n",
              "  'eval_loss': [-0.1932373046875,\n",
              "   -0.214599609375,\n",
              "   -0.2705078125,\n",
              "   -0.32373046875,\n",
              "   -0.376220703125,\n",
              "   -0.435791015625,\n",
              "   -0.5029296875,\n",
              "   -0.57275390625,\n",
              "   -0.646484375,\n",
              "   -0.72119140625,\n",
              "   -0.791015625,\n",
              "   -0.85205078125,\n",
              "   -0.90625,\n",
              "   -0.9521484375,\n",
              "   -0.9912109375,\n",
              "   -1.0224609375,\n",
              "   -1.046875,\n",
              "   -1.0654296875,\n",
              "   -1.0771484375,\n",
              "   -1.0830078125]})"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "result, texts = model.eval_model(test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "13cf7ccf37d14a909bf8632641150317",
            "5da27fd200fa4de9b7001f467fb213a1",
            "927744281a334e1cb4e258b29288ef4f",
            "a6f7d995d3914a1789aa62e9d37ed3c9",
            "200ae4d722934cc481e09ccab01036c3",
            "e0d3a3c19a45483a9b2d8dccee9db802",
            "46af1cccab304cbdbabf2622e29f586a",
            "8485d86814b44401b94aa417eaeb4a56",
            "c2adff812467424aae3cdeae8f0d933d",
            "54cce8a91ff64694b1073f095f569018",
            "5f8b0a8143d143319bbfe654d0c202b7"
          ]
        },
        "id": "tCZEQh58igb_",
        "outputId": "80d6dc4e-b95a-40a7-d50c-cfd1f3541270"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Evaluation:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "13cf7ccf37d14a909bf8632641150317"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZjIx04P6jBvO",
        "outputId": "c00c381a-b9eb-46c1-a4f9-09c71e031c52"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'correct': 0, 'similar': 2, 'incorrect': 1, 'eval_loss': -1.0830078125}"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make prediction 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",
        "]"
      ],
      "metadata": {
        "id": "huSdox_xiy5B"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "answers, probabilities = model.predict(to_predict)\n",
        "\n",
        "print(answers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101,
          "referenced_widgets": [
            "f83280f663814b6785eb4e3328e791c7",
            "a264e52d3e104eb4b6141238d727bbf5",
            "af1e7ed332364142a3d26065f854d26c",
            "50645668fc5e46d8822f880c1cb057dc",
            "89f6306991104b11a38cd3bbd7a7920b",
            "2acdaba5ed2c46f88cdf945347ee10ca",
            "688da3870daa40428d572ec13f881ed9",
            "d0975de1e2214c849f2f947df12bb464",
            "611d98c653a54230b1ce42cf3ead3f08",
            "9e22c68525ca4e1caa7f611af3841252",
            "d64c1d14428e4223897524c357eb1eb6"
          ]
        },
        "id": "WZVIhvd5laxz",
        "outputId": "7d0b6fe9-9544-458b-f3a4-14131e80397a"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "convert squad examples to features: 100%|██████████| 1/1 [00:00<00:00, 1339.61it/s]\n",
            "add example index and unique id: 100%|██████████| 1/1 [00:00<00:00, 1990.65it/s]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Prediction:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f83280f663814b6785eb4e3328e791c7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'id': '0', 'answer': ['born of great power and skill', 'great power and skill', 'of great power and skill']}]\n"
          ]
        }
      ]
    }
  ]
}