q_a_with_Huggingface_transformers.ipynb

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            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "db3504cb74c443c4a492608ac093a2d9": {
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sankalp1233/Code/blob/main/q_a_with_Huggingface_transformers.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "9ClXDfFxAENi",
        "outputId": "b185ee4f-7811-44ca-c16b-585ec293829d"
      },
      "source": [
        "# !pip3 uninstall transformers\n",
        "# !pip3 install transformers\n",
        "# from transformers import pipeline\n",
        "# !pip3 uninstall transformers \n",
        "# !pip3 install git+c\n",
        "# !pip3 install sentencepiece\n",
        "# !pip3 uninstall transfomers\n",
        "# !pip3 install git+https://github.com/huggingface/transformers\n",
        "# from transformers import pipeline\n",
        "from google.colab import files\n",
        "uploaded = files.upload() \n",
        "filename = \"Spark_QA_no_new_line.txt\"\n",
        "# filename = \"context.txt\"\n",
        "# filename = \"police_fbi_question.txt\"\n",
        "new_file = uploaded[filename].decode(\"utf-8\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-be7669e5-af0c-4d93-9192-098a097a4224\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-be7669e5-af0c-4d93-9192-098a097a4224\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving Spark_QA_no_new_line.txt to Spark_QA_no_new_line.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Hdr2HktL1kX",
        "outputId": "46f307c2-9af6-4674-e95c-4ebe015a83d8"
      },
      "source": [
        "# print(new_file)\n",
        "# !pip3 uninstall transformers\n",
        "# !pip3 install PyTorch\n",
        "# !pip3 uninstall TensorFlow\n",
        "!pip3 install TensorFlow\n",
        "# !pip3 uninstall transformers "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: TensorFlow in /usr/local/lib/python3.7/dist-packages (2.4.1)\n",
            "Requirement already satisfied: six~=1.15.0 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (1.15.0)\n",
            "Requirement already satisfied: typing-extensions~=3.7.4 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (3.7.4.3)\n",
            "Requirement already satisfied: flatbuffers~=1.12.0 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (1.12)\n",
            "Requirement already satisfied: grpcio~=1.32.0 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (1.32.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.5.0,>=2.4.0 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (2.4.0)\n",
            "Requirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (1.6.3)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (0.3.3)\n",
            "Requirement already satisfied: tensorboard~=2.4 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (2.5.0)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (3.12.4)\n",
            "Requirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (0.12.0)\n",
            "Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (3.3.0)\n",
            "Requirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (0.36.2)\n",
            "Requirement already satisfied: termcolor~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (1.1.0)\n",
            "Requirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (1.12.1)\n",
            "Requirement already satisfied: h5py~=2.10.0 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (2.10.0)\n",
            "Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (1.1.2)\n",
            "Requirement already satisfied: numpy~=1.19.2 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (1.19.5)\n",
            "Requirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from TensorFlow) (0.2.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->TensorFlow) (2.23.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->TensorFlow) (0.6.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->TensorFlow) (0.4.4)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->TensorFlow) (1.8.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->TensorFlow) (3.3.4)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->TensorFlow) (54.2.0)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->TensorFlow) (1.28.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.4->TensorFlow) (1.0.1)\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->TensorFlow) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->TensorFlow) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard~=2.4->TensorFlow) (2.10)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->TensorFlow) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard~=2.4->TensorFlow) (3.10.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3.6\" in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->TensorFlow) (4.7.2)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->TensorFlow) (4.2.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.4->TensorFlow) (0.2.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->TensorFlow) (3.1.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard~=2.4->TensorFlow) (3.4.1)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3.6\"->google-auth<2,>=1.6.3->tensorboard~=2.4->TensorFlow) (0.4.8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc6HHr2_AngS",
        "outputId": "86a2c993-099e-41ff-ee68-85b3f6894496"
      },
      "source": [
        "# !pip3 uninstall sentencepiece\n",
        "# !pip3 uninstall transformers\n",
        "# !pip3 uninstall tensorflow\n",
        "# pip3 uninstall sentencepiece\n",
        "# import TensorFlow\n",
        "# !pip3 install sentencepiece\n",
        "!pip3 install git+https://github.com/huggingface/transformers\n",
        "question = \"What is the purpose of SPARK?\"\n",
        "# import tensorflow as tf\n",
        "# tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')\n",
        "# model = XLNetForQuestionAnswering.from_pretrained('xlnet-base-cased')\n",
        "# question = \"Would police or the FBI ever be able to access DNA or other information collected?\"\n",
        "# import sentencepiece\n",
        "# Generating an answer to the question in context\n",
        "from transformers import pipeline\n",
        "qa = pipeline(\"question-answering\")\n",
        "print(qa)\n",
        "answer = qa(question=question, context=new_file)\n",
        "print(answer)\n",
        "# Print the answer\n",
        "print(f\"Question: {question}\")\n",
        "print(f\"Answer: '{answer['answer']}' with score {answer['score']}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/huggingface/transformers\n",
            "  Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-fb5r5ev5\n",
            "  Running command git clone -q https://github.com/huggingface/transformers /tmp/pip-req-build-fb5r5ev5\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied (use --upgrade to upgrade): transformers==4.6.0.dev0 from git+https://github.com/huggingface/transformers in /usr/local/lib/python3.7/dist-packages\n",
            "Requirement already satisfied: huggingface-hub>=0.0.8 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (0.0.8)\n",
            "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (0.10.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (2.23.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (4.41.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (2019.12.20)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (0.0.45)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (3.0.12)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (3.10.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (20.9)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (1.19.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.6.0.dev0) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.6.0.dev0) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.6.0.dev0) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.6.0.dev0) (3.0.4)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.6.0.dev0) (1.15.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.6.0.dev0) (1.0.1)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.6.0.dev0) (7.1.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers==4.6.0.dev0) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers==4.6.0.dev0) (3.7.4.3)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers==4.6.0.dev0) (2.4.7)\n",
            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.6.0.dev0-cp37-none-any.whl size=2112959 sha256=4df64e4e1226a3301250ef8945bb2f0a905012813a4892dd3c2d59902362eb1a\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-s9nzd7f1/wheels/70/d3/52/b3fa4f8b8ef04167ac62e5bb2accb62ae764db2a378247490e\n",
            "Successfully built transformers\n",
            "<transformers.pipelines.question_answering.QuestionAnsweringPipeline object at 0x7fecb9deae10>\n",
            "{'score': 0.6045412421226501, 'start': 24, 'end': 100, 'answer': 'to help scientists find and better understand the potential causes of autism'}\n",
            "Question: What is the purpose of SPARK?\n",
            "Answer: 'to help scientists find and better understand the potential causes of autism' with score 0.6045412421226501\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icsKG43ax0VN",
        "outputId": "26e7ab94-4f79-4a9b-c0c7-f40de4823484"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModel\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "model = AutoModel.from_pretrained(\"bert-base-uncased\")\n",
        "inputs = tokenizer(\"Hello world!\", return_tensors=\"pt\")\n",
        "print(inputs)\n",
        "outputs = model(**inputs)\n",
        "print(outputs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'input_ids': tensor([[ 101, 7592, 2088,  999,  102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1]])}\n",
            "BaseModelOutputWithPoolingAndCrossAttentions(last_hidden_state=tensor([[[-0.1424,  0.1335, -0.1291,  ..., -0.3597, -0.0562,  0.3605],\n",
            "         [-0.3506,  0.1042,  0.6244,  ..., -0.1761,  0.4834,  0.0644],\n",
            "         [-0.2451, -0.1573,  0.6945,  ..., -0.5654, -0.0894, -0.1856],\n",
            "         [-0.8248, -0.9119, -0.6561,  ...,  0.5074, -0.1939, -0.1659],\n",
            "         [ 0.8767,  0.0352, -0.1233,  ...,  0.2720, -0.6369, -0.1585]]],\n",
            "       grad_fn=<NativeLayerNormBackward>), pooler_output=tensor([[-8.9756e-01, -3.3040e-01, -7.6942e-01,  7.5799e-01,  4.6678e-01,\n",
            "         -1.2035e-01,  9.1835e-01,  1.8087e-01, -7.2716e-01, -9.9991e-01,\n",
            "         -4.4723e-01,  8.9104e-01,  9.6621e-01,  5.4915e-01,  9.4344e-01,\n",
            "         -7.6605e-01, -6.0469e-01, -6.1654e-01,  4.0572e-01, -7.4644e-01,\n",
            "          6.1739e-01,  9.9974e-01,  3.2991e-02,  2.5414e-01,  4.3106e-01,\n",
            "          9.7732e-01, -8.4328e-01,  9.2297e-01,  9.4871e-01,  6.3994e-01,\n",
            "         -7.3620e-01,  9.0956e-02, -9.7607e-01, -1.9114e-01, -8.1717e-01,\n",
            "         -9.7907e-01,  3.1635e-01, -7.2013e-01,  1.6124e-01,  5.1999e-02,\n",
            "         -8.9102e-01,  2.3002e-01,  9.9967e-01,  1.1503e-02,  1.1176e-01,\n",
            "         -3.5556e-01, -1.0000e+00,  2.8487e-01, -8.3339e-01,  8.0987e-01,\n",
            "          7.4664e-01,  6.2762e-01,  1.9847e-01,  4.5185e-01,  4.7242e-01,\n",
            "         -3.9155e-03, -1.0570e-01,  9.3339e-02, -1.8906e-01, -5.1946e-01,\n",
            "         -5.7823e-01,  2.8046e-01, -8.2281e-01, -8.9764e-01,  9.1890e-01,\n",
            "          7.4117e-01, -5.9992e-02, -3.1655e-01,  2.8232e-03, -1.4652e-01,\n",
            "          9.0953e-01,  2.5587e-01,  5.4494e-02, -8.4187e-01,  5.7771e-01,\n",
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            "          5.5482e-01,  6.8866e-01,  5.5711e-01, -3.3463e-01,  4.7904e-01,\n",
            "         -1.0000e+00,  3.4197e-01, -1.1332e-01, -9.7818e-01,  2.2887e-01,\n",
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            "          6.8812e-01, -8.9320e-01, -5.4703e-01,  4.8356e-02,  2.1540e-01,\n",
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            "         -2.4163e-01, -8.0916e-01,  7.3723e-01, -9.7524e-01, -9.7020e-01,\n",
            "          8.5261e-01,  1.8727e-01, -2.7090e-01,  9.9998e-01,  4.6983e-01,\n",
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            "          7.4747e-01,  7.9880e-01,  8.0815e-01, -1.0571e-01,  9.6487e-02,\n",
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            "         -3.2831e-01,  6.3693e-01,  8.2339e-03,  5.5376e-01,  7.1281e-01,\n",
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            "         -8.5261e-01, -9.2280e-01,  9.4217e-01, -9.8578e-01,  6.6755e-01,\n",
            "          1.0000e+00,  3.4225e-01, -1.6761e-01,  7.7093e-02, -3.9836e-01,\n",
            "          2.2625e-01, -2.2041e-01,  7.3612e-01, -9.2743e-01, -3.6491e-01,\n",
            "         -1.7540e-01,  2.9468e-01, -1.1303e-01,  2.6401e-01,  6.9200e-01,\n",
            "          1.5430e-01, -4.0083e-01, -5.2640e-01,  2.4087e-02,  4.3949e-01,\n",
            "          8.6077e-01, -2.6866e-01, -1.0388e-01,  7.5584e-03, -1.2241e-01,\n",
            "         -9.1376e-01, -2.1348e-01, -2.9673e-01, -9.9988e-01,  6.9455e-01,\n",
            "         -1.0000e+00,  2.8368e-01,  8.6733e-02, -1.2220e-01,  7.9206e-01,\n",
            "          1.5489e-02,  4.9311e-01, -7.3105e-01, -8.1444e-01,  5.0827e-01,\n",
            "          7.2358e-01, -2.9349e-01, -5.1790e-01, -6.9774e-01,  2.4637e-01,\n",
            "         -1.5526e-02,  2.1339e-01, -5.1806e-01,  7.8137e-01, -1.7301e-01,\n",
            "          1.0000e+00,  1.9527e-01, -7.5410e-01, -9.8262e-01,  1.5416e-01,\n",
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            "         -6.9617e-01, -8.3260e-01,  2.6819e-01, -3.4683e-02, -7.8862e-01,\n",
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            "          2.6254e-01,  8.9137e-01,  4.2302e-01, -7.3706e-02,  9.4970e-01,\n",
            "          1.6445e-01,  6.1381e-01,  1.0270e-01,  1.0000e+00,  2.7983e-01,\n",
            "         -8.8676e-01,  1.4404e-01, -9.7244e-01, -1.6864e-01, -9.5221e-01,\n",
            "          2.6231e-01,  2.7997e-01,  8.9316e-01, -2.3383e-01,  9.4295e-01,\n",
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            "         -9.0270e-01, -9.7206e-01, -9.7301e-01,  6.8435e-01, -4.2106e-01,\n",
            "          4.4724e-02,  1.2440e-01,  7.1741e-03,  3.5916e-01,  4.4393e-01,\n",
            "         -1.0000e+00,  9.2962e-01,  3.8557e-01,  8.5492e-01,  9.2992e-01,\n",
            "          7.0625e-01,  4.8370e-01,  2.3270e-01, -9.7541e-01, -9.8218e-01,\n",
            "         -3.2932e-01, -2.1163e-01,  7.5084e-01,  6.3202e-01,  8.8856e-01,\n",
            "          4.3251e-01, -5.0404e-01, -6.7028e-02, -2.9505e-01, -3.2807e-01,\n",
            "         -9.8568e-01,  4.3446e-01, -5.8658e-01, -9.7724e-01,  9.3651e-01,\n",
            "         -1.3708e-01, -1.3665e-01, -9.2903e-02, -6.6635e-01,  9.7278e-01,\n",
            "          7.0707e-01,  4.3203e-01,  8.7915e-02,  4.7217e-01,  8.3990e-01,\n",
            "          9.4993e-01,  9.7219e-01, -6.7202e-01,  8.3279e-01, -5.4167e-01,\n",
            "          4.1125e-01,  3.4719e-01, -9.0791e-01,  9.1832e-02,  2.0505e-01,\n",
            "         -2.8610e-01,  1.5352e-01, -1.1304e-01, -9.8395e-01,  1.8631e-01,\n",
            "         -2.0863e-01,  6.1166e-01, -2.9382e-01,  5.8579e-02, -3.7076e-01,\n",
            "         -3.7402e-02, -6.2659e-01, -7.5935e-01,  5.6508e-01,  5.0258e-01,\n",
            "          8.7703e-01,  8.0735e-01, -6.9027e-02, -6.4556e-01, -2.2308e-01,\n",
            "         -7.0203e-01, -8.9239e-01,  9.4453e-01, -2.7451e-02, -3.4130e-01,\n",
            "          5.0977e-01, -1.5005e-01,  5.0155e-01,  9.2491e-02, -3.1371e-01,\n",
            "         -3.9861e-01, -6.5197e-01,  8.3609e-01,  4.2015e-02, -5.3505e-01,\n",
            "         -7.0400e-01,  5.6731e-01,  3.1090e-01,  9.9974e-01, -6.7131e-01,\n",
            "         -8.3116e-01, -1.6593e-01, -3.9197e-01,  2.8166e-01, -4.2960e-01,\n",
            "         -1.0000e+00,  3.9846e-01, -3.7210e-01,  6.3236e-01, -6.8413e-01,\n",
            "          4.8827e-01, -7.2434e-01, -9.8220e-01, -1.6252e-01,  4.2167e-02,\n",
            "          6.8658e-01, -4.7085e-01, -7.6191e-01,  4.8094e-01, -1.6688e-01,\n",
            "          9.5317e-01,  8.1554e-01, -3.6865e-01,  1.1302e-01,  6.0376e-01,\n",
            "         -6.5595e-01, -6.1998e-01,  9.0952e-01]], grad_fn=<TanhBackward>), hidden_states=None, past_key_values=None, attentions=None, cross_attentions=None)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n8YvzlrCCTk9",
        "outputId": "6bcc52e5-3605-4774-e97f-a05da0e332d1"
      },
      "source": [
        "# from transformers import XLNetForQuestionAnswering, XLNetTokenizer\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "# tokenizer =  XLNetTokenizer.from_pretrained('xlnet-base-cased')\n",
        "tokenizer =  AutoTokenizer.from_pretrained('xlnet-base-cased')\n",
        "model = AutoModel.from_pretrained('xlnet-base-cased')\n",
        "# input_ids = tokenizer.encode(question, new_file)\n",
        "inputs = tokenizer(new_file, return_tensors=\"pt\")\n",
        "print(inputs)\n",
        "outputs = model(**inputs)\n",
        "print(outputs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'input_ids': tensor([[609,  27,  18,  ...,   9,   4,   3]]), 'token_type_ids': tensor([[0, 0, 0,  ..., 0, 0, 2]]), 'attention_mask': tensor([[1, 1, 1,  ..., 1, 1, 1]])}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZ5MpQjqsQH2",
        "outputId": "8d247f98-1de4-4207-fb99-6cbc8a727f07"
      },
      "source": [
        "# !pip3 install transformers==4.5.0\n",
        "!pip3 uninstall transformers\n",
        "# !pip3 install tqdm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Uninstalling transformers-4.6.0.dev0:\n",
            "  Would remove:\n",
            "    /usr/local/bin/transformers-cli\n",
            "    /usr/local/lib/python3.7/dist-packages/transformers-4.6.0.dev0.dist-info/*\n",
            "    /usr/local/lib/python3.7/dist-packages/transformers/*\n",
            "Proceed (y/n)? y\n",
            "  Successfully uninstalled transformers-4.6.0.dev0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609,
          "referenced_widgets": [
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            "a3301cc256684d85a94cd9c33e48e907",
            "8ff6537784e54a158d2e9791650e3411",
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            "b32ab0f43d3948128d30c3da3f0eacbf",
            "b33a5d46f5f5443ca792e980711d0467"
          ]
        },
        "id": "SAJUgc7SEVc5",
        "outputId": "cfa59510-33aa-4f74-dcfa-845d52548fd9"
      },
      "source": [
        "def chatbot(new_file):\n",
        "    question = input(\"Ask a question: \")\n",
        "    while question != 'stop':     \n",
        "          modelname = \"mrm8488/bert-tiny-5-finetuned-squadv2\"\n",
        "          tokenizer = AutoTokenizer.from_pretrained(modelname)\n",
        "          model = AutoModelForQuestionAnswering.from_pretrained(modelname)\n",
        "          qa1 = pipeline('question-answering', model= model, tokenizer = tokenizer)\n",
        "          answer = qa1(question=question, context=new_file)\n",
        "          print(answer)\n",
        "          helpful = input(\"Was this helpful? If no try again: \")\n",
        "          if helpful == 'no':\n",
        "             # modelname = 'deepset/bert-base-cased-squad2'\n",
        "             modelname = 'bert-large-uncased-whole-word-masking-finetuned-squad'\n",
        "             tokenizer = AutoTokenizer.from_pretrained(modelname)\n",
        "             model = AutoModelForQuestionAnswering.from_pretrained(modelname)\n",
        "             qa1 = pipeline('question-answering', model= model, tokenizer = tokenizer)\n",
        "             answer = qa1(question=question, context=new_file)\n",
        "             print(answer['answer'])\n",
        "             helpful = input(\"Was this helpful? If no try again: \")\n",
        "             if helpful == 'no':\n",
        "                modelname = 'ktrapeznikov/albert-xlarge-v2-squad-v2'\n",
        "                tokenizer = AutoTokenizer.from_pretrained(modelname)\n",
        "                model = AutoModelForQuestionAnswering.from_pretrained(modelname)\n",
        "                qa1 = pipeline('question-answering', model= model, tokenizer = tokenizer)\n",
        "                answer = qa1(question=question, context=new_file)\n",
        "                print(answer['answer'])\n",
        "                helpful = input(\"Was this helpful? If no try again: \")\n",
        "                if helpful == 'no':\n",
        "                   modelname = 'deepset/bert-base-cased-squad2'\n",
        "                   tokenizer = AutoTokenizer.from_pretrained(modelname)\n",
        "                   model = AutoModelForQuestionAnswering.from_pretrained(modelname)\n",
        "                   qa1 = pipeline('question-answering', model= model, tokenizer = tokenizer)\n",
        "                   answer = qa1(question=question, context=new_file)\n",
        "                   print(answer['answer'])\n",
        "                   helpful = input(\"Was this helpful? If no try again: \")\n",
        "                   if helpful == 'no':\n",
        "                      modelname = \"twmkn9/albert-base-v2-squad2\"\n",
        "                      tokenizer = AutoTokenizer.from_pretrained(modelname)\n",
        "                      model = AutoModelForQuestionAnswering.from_pretrained(modelname)\n",
        "                      qa1 = pipeline('question-answering', model= model, tokenizer = tokenizer)\n",
        "                      answer = qa1(question=question, context=new_file)\n",
        "                      print(answer['answer'])\n",
        "                      helpful = input(\"Was this helpful? If no try again: \")\n",
        "                      if helpful == 'no':\n",
        "                         modelname = \"distilbert-base-cased-distilled-squad\"\n",
        "                         tokenizer = AutoTokenizer.from_pretrained(modelname)\n",
        "                         model = AutoModelForQuestionAnswering.from_pretrained(modelname)\n",
        "                         qa1 = pipeline('question-answering', model= model, tokenizer = tokenizer)\n",
        "                         answer = qa1(question=question, context=new_file)\n",
        "                         print(answer['answer'])\n",
        "                         helpful = input(\"Was this helpful? If no try again: \")\n",
        "                         if helpful == 'no':\n",
        "                            model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
        "                            tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-cased\")\n",
        "                            qa1 = pipeline('question-answering', model= 'distilbert-base-cased-distilled-squad', tokenizer = 'distilbert-base-cased')\n",
        "                            answer = qa1(question=question, context=new_file)\n",
        "                            print(answer['answer'])\n",
        "                            helpful = input(\"Was this helpful? If no try again: \")\n",
        "                            if helpful == 'no':\n",
        "                               qa1 = pipeline('question-answering')\n",
        "                               answer = qa1(question=question, context=new_file)\n",
        "                               if answer['score'] < 0.6:\n",
        "                                  print(\"If you are not satisfied with your answer please email info@SPARKforAutism.org.\")\n",
        "                               else:\n",
        "                                  print(answer['answer'])\n",
        "          else:\n",
        "              if answer['score'] < 0.6:\n",
        "                 print(\"If you are not satisfied with your answer please email info@SPARKforAutism.org.\")\n",
        "              else:\n",
        "                 print(answer['answer'])\n",
        "          question = input(\"Ask another question: or if you would like to stop asking questions type stop: \")\n",
        "chatbot(new_file)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ask a question: What is the purpose of Spark?\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=463.0, style=ProgressStyle(description_…"
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          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
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          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
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            "\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
          "text": [
            "\n",
            "{'score': 0.36136406660079956, 'start': 43, 'end': 116, 'answer': 'help scientists find and better understand the potential causes of autism'}\n",
            "Was this helpful? If no go to another model: no\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
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          "output_type": "stream",
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            "\n"
          ],
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        },
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          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          "output_type": "stream",
          "text": [
            "\n",
            "{'score': 0.9193425178527832, 'start': 27924, 'end': 27942, 'answer': 'protecting my data'}\n",
            "Was this helpful? If no go to another model: yes\n",
            "Ask another question: or if you would like to stop asking questions type stop: stop\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "id": "NmSaCZb7wuRp",
        "outputId": "fbb305c6-fd79-470c-8aa4-9b758d357a61"
      },
      "source": [
        "def chatbot(new_file):\n",
        "    question = input(\"Ask a question: \")\n",
        "    while question != 'stop': \n",
        "          models = [\"deepset/roberta-large-squad2-hp\",\"deepset/xlm-roberta-base-squad2\",\"valhalla/longformer-base-4096-finetuned-squadv1\",\"mrm8488/bert-tiny-5-finetuned-squadv2\", 'bert-large-uncased-whole-word-masking-finetuned-squad', 'ktrapeznikov/albert-xlarge-v2-squad-v2',\n",
        "                    'deepset/bert-base-cased-squad2', \"twmkn9/albert-base-v2-squad2\", \"distilbert-base-cased-distilled-squad\"]\n",
        "          for i in range(len(models)):   \n",
        "              modelname = models[i]\n",
        "              tokenizer = AutoTokenizer.from_pretrained(modelname)\n",
        "              model = AutoModelForQuestionAnswering.from_pretrained(modelname)\n",
        "              qa1 = pipeline('question-answering', model= model, tokenizer = tokenizer)\n",
        "              answer = qa1(question=question, context=new_file)\n",
        "              print(answer)\n",
        "              helpful = input(\"Was this helpful? If no try again: \")\n",
        "              if helpful != 'no':\n",
        "                 if answer['score'] < 0.6:\n",
        "                    print(\"If you are not satisfied with your answer please email info@SPARKforAutism.org.\")\n",
        "                 else:\n",
        "                    print(answer['answer'])\n",
        "                 break\n",
        "              elif i == len(models) - 1 and helpful == 'no':\n",
        "                   model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
        "                   tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-cased\")\n",
        "                   qa1 = pipeline('question-answering', model= 'distilbert-base-cased-distilled-squad', tokenizer = 'distilbert-base-cased')\n",
        "                   answer = qa1(question=question, context=new_file)\n",
        "                   print(answer['answer'])\n",
        "                   helpful = input(\"Was this helpful? If no try again: \")\n",
        "                   if helpful != 'no':\n",
        "                      break\n",
        "                   else:\n",
        "                     qa1 = pipeline('question-answering')\n",
        "                     answer = qa1(question=question, context=new_file)\n",
        "                     if answer['score'] < 0.6:\n",
        "                        print(\"If you are not satisfied with your answer please email info@SPARKforAutism.org.\")\n",
        "                     else:\n",
        "                        print(answer['answer'])\n",
        "          question = input(\"Ask another question: or if you would like to stop asking questions type stop: \")\n",
        "chatbot(new_file)   \n",
        "                   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ask a question: What is Spark?\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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            "\n"
          ],
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            "\n"
          ],
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        },
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          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1195.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4cdbea5446fb4df984eb1b8563e27f29",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1417415991.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "{'score': 8.103785376079031e-07, 'start': 2555, 'end': 2582, 'answer': 'all siblings without autism'}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    728\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 729\u001b[0;31m                 \u001b[0mident\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstdin_socket\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    730\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/jupyter_client/session.py\u001b[0m in \u001b[0;36mrecv\u001b[0;34m(self, socket, mode, content, copy)\u001b[0m\n\u001b[1;32m    802\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 803\u001b[0;31m             \u001b[0mmsg_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_multipart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    804\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mzmq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZMQError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/zmq/sugar/socket.py\u001b[0m in \u001b[0;36mrecv_multipart\u001b[0;34m(self, flags, copy, track)\u001b[0m\n\u001b[1;32m    582\u001b[0m         \"\"\"\n\u001b[0;32m--> 583\u001b[0;31m         \u001b[0mparts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m         \u001b[0;31m# have first part already, only loop while more to receive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.recv\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._recv_copy\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/zmq/backend/cython/checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-acd3cd9e7305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     35\u001b[0m                         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'answer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m           \u001b[0mquestion\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Ask another question: or if you would like to stop asking questions type stop: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0mchatbot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-8-acd3cd9e7305>\u001b[0m in \u001b[0;36mchatbot\u001b[0;34m(new_file)\u001b[0m\n\u001b[1;32m     11\u001b[0m               \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqa1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mquestion\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mquestion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m               \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m               \u001b[0mhelpful\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Was this helpful? If no try again: \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mhelpful\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'no'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m                  \u001b[0;32mif\u001b[0m \u001b[0manswer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'score'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.6\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36mraw_input\u001b[0;34m(self, prompt)\u001b[0m\n\u001b[1;32m    702\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_ident\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parent_header\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m             \u001b[0mpassword\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    705\u001b[0m         )\n\u001b[1;32m    706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\u001b[0m in \u001b[0;36m_input_request\u001b[0;34m(self, prompt, ident, parent, password)\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    733\u001b[0m                 \u001b[0;31m# re-raise KeyboardInterrupt, to truncate traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 734\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    735\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dlzpxqCMYQ74"
      },
      "source": [
        "def chatbot(new_file):\n",
        "    question = input(\"Ask a question: \")\n",
        "    while question != 'stop': \n",
        "          models = [\"deepset/roberta-large-squad2-hp\",\"deepset/xlm-roberta-base-squad2\",\"valhalla/longformer-base-4096-finetuned-squadv1\",\"mrm8488/bert-tiny-5-finetuned-squadv2\", 'bert-large-uncased-whole-word-masking-finetuned-squad', 'ktrapeznikov/albert-xlarge-v2-squad-v2',\n",
        "                    'deepset/bert-base-cased-squad2', \"twmkn9/albert-base-v2-squad2\", \"distilbert-base-cased-distilled-squad\"]\n",
        "          for i in range(len(models)):   \n",
        "              modelname = models[i]\n",
        "              tokenizer = AutoTokenizer.from_pretrained(modelname)\n",
        "              model = AutoModelForQuestionAnswering.from_pretrained(modelname)\n",
        "              qa1 = pipeline('question-answering', model= model, tokenizer = tokenizer)\n",
        "              answer = qa1(question=question, context=new_file)\n",
        "              print(answer)\n",
        "              helpful = input(\"Was this helpful? If no try again: \")\n",
        "              if helpful != 'no':\n",
        "                 if answer['score'] < 0.6:\n",
        "                    print(\"If you are not satisfied with your answer please email info@SPARKforAutism.org.\")\n",
        "                 else:\n",
        "                    print(answer['answer'])\n",
        "                 break\n",
        "              elif i == len(models) - 1 and helpful == 'no':\n",
        "                   model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
        "                   tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-cased\")\n",
        "                   qa1 = pipeline('question-answering', model= 'distilbert-base-cased-distilled-squad', tokenizer = 'distilbert-base-cased')\n",
        "                   answer = qa1(question=question, context=new_file)\n",
        "                   print(answer['answer'])\n",
        "                   helpful = input(\"Was this helpful? If no try again: \")\n",
        "                   if helpful != 'no':\n",
        "                      break\n",
        "                   else:\n",
        "                     qa1 = pipeline('question-answering')\n",
        "                     answer = qa1(question=question, context=new_file)\n",
        "                     if answer['score'] < 0.6:\n",
        "                        print(\"If you are not satisfied with your answer please email info@SPARKforAutism.org.\")\n",
        "                     else:\n",
        "                        print(answer['answer'])\n",
        "          question = input(\"Ask another question: or if you would like to stop asking questions type stop: \")\n",
        "chatbot(new_file)   \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "igOqC3wLKb17",
        "outputId": "991fc58e-6d71-4980-b09c-fe52fa3f2cd8"
      },
      "source": [
        "# !pip3 install pytorch\n",
        "# !pip3 install tensorflow\n",
        "!pip3 install sentencepiece\n",
        "!pip3 install git+https://github.com/huggingface/transformers\n",
        "from tqdm import tqdm\n",
        "from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer, AutoModelForQuestionAnswering\n",
        "from transformers import BertForQuestionAnswering\n",
        "# from transformers import pipeline\n",
        "from google.colab import files\n",
        "uploaded = files.upload() \n",
        "new_file = uploaded['context.txt'].decode(\"utf-8\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (0.1.95)\n",
            "Collecting git+https://github.com/huggingface/transformers\n",
            "  Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-3w_6md4k\n",
            "  Running command git clone -q https://github.com/huggingface/transformers /tmp/pip-req-build-3w_6md4k\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied (use --upgrade to upgrade): transformers==4.6.0.dev0 from git+https://github.com/huggingface/transformers in /usr/local/lib/python3.7/dist-packages\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (3.0.12)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (1.19.5)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (20.9)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (2.23.0)\n",
            "Requirement already satisfied: huggingface-hub==0.0.8 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (0.0.8)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (2019.12.20)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (4.41.1)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (3.10.1)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (0.0.45)\n",
            "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers==4.6.0.dev0) (0.10.2)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers==4.6.0.dev0) (2.4.7)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.6.0.dev0) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.6.0.dev0) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.6.0.dev0) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.6.0.dev0) (2020.12.5)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers==4.6.0.dev0) (3.4.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers==4.6.0.dev0) (3.7.4.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.6.0.dev0) (1.0.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.6.0.dev0) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.6.0.dev0) (7.1.2)\n",
            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.6.0.dev0-cp37-none-any.whl size=2171153 sha256=8d7d307ce3ceb2f9779e7be44e6f063df06c243dcbd2fe9f0d0d99b7da9b51d8\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-dna0tter/wheels/70/d3/52/b3fa4f8b8ef04167ac62e5bb2accb62ae764db2a378247490e\n",
            "Successfully built transformers\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-8880d9c7-252a-41fa-9a64-ac69b6cc1a3c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-8880d9c7-252a-41fa-9a64-ac69b6cc1a3c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving context.txt to context (2).txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 1000,
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        "id": "OKeqZRz3X6UO",
        "outputId": "3fb290e4-cedd-48c7-ce80-182034bd2f37"
      },
      "source": [
        "models = [\"deepset/xlm-roberta-base-squad2\",\"valhalla/longformer-base-4096-finetuned-squadv1\",\"mrm8488/bert-tiny-5-finetuned-squadv2\", \"deepset/roberta-large-squad2-hp\",\n",
        "           'bert-large-uncased-whole-word-masking-finetuned-squad', 'ktrapeznikov/albert-xlarge-v2-squad-v2',\n",
        "                    'deepset/bert-base-cased-squad2', \"twmkn9/albert-base-v2-squad2\", \"distilbert-base-cased-distilled-squad\", 'csarron/roberta-large-squad-v1']\n",
        "question = input(\"Ask a question: \")\n",
        "for i in range(len(models)):   \n",
        "         modelname = models[i]\n",
        "         tokenizer = AutoTokenizer.from_pretrained(modelname)\n",
        "         model = AutoModelForQuestionAnswering.from_pretrained(modelname)\n",
        "         qa1 = pipeline('question-answering', model= model, tokenizer = tokenizer)\n",
        "         answer = qa1(question=question, context=new_file)\n",
        "         print(answer)"
      ],
      "execution_count": null,
      "outputs": [
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            "Ask a question: What is Spark?\n",
            "{'score': 0.22977633774280548, 'start': 13641, 'end': 13684, 'answer': ' genetic differences not related to autism.'}\n",
            "{'score': 0.7524832487106323, 'start': 24, 'end': 100, 'answer': 'to help scientists find and better understand the potential causes of autism'}\n",
            "{'score': 0.35056212544441223, 'start': 25005, 'end': 25023, 'answer': 'protecting my data'}\n",
            "{'score': 8.103785376079031e-07, 'start': 2555, 'end': 2582, 'answer': 'all siblings without autism'}\n",
            "{'score': 0.8036293983459473, 'start': 14084, 'end': 14102, 'answer': 'very large studies'}\n"
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            "\n",
            "{'score': 0.4907674789428711, 'start': 572, 'end': 615, 'answer': 'to help shape the future of autism research'}\n"
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      "metadata": {
        "colab": {
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          "base_uri": "https://localhost:8080/",
          "height": 899,
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        },
        "id": "-lU9IfYfentG",
        "outputId": "750e26a4-ee88-47ea-a69b-462c4d18ec19"
      },
      "source": [
        "!pip install transformers\n",
        "import torch\n",
        "from transformers import BertForQuestionAnswering\n",
        "from transformers import BertTokenizer\n",
        "#Model\n",
        "model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')\n",
        "#Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')\n",
        "question = \"Would police or the FBI ever be able to access DNA or other information collected?\"\n",
        "from google.colab import files\n",
        "uploaded = files.upload() \n",
        "filename = \"New_Spark_Questions.txt\"\n",
        "new_file = uploaded[filename].decode(\"utf-8\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/81/91/61d69d58a1af1bd81d9ca9d62c90a6de3ab80d77f27c5df65d9a2c1f5626/transformers-4.5.0-py3-none-any.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.2MB 5.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/cd/342e584ee544d044fb573ae697404ce22ede086c9e87ce5960772084cad0/sacremoses-0.0.44.tar.gz (862kB)\n",
            "\u001b[K     |████████████████████████████████| 870kB 39.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/04/5b870f26a858552025a62f1649c20d29d2672c02ff3c3fb4c688ca46467a/tokenizers-0.10.2-cp37-cp37m-manylinux2010_x86_64.whl (3.3MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3MB 47.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.44-cp37-none-any.whl size=886084 sha256=12ea4ea44ed1bdbf3d917a8f352fd5bd5afcf046233183c74bbcbf49a82df6b4\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/fb/c0/13ab4d63d537658f448366744654323077c4d90069b6512f3c\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.44 tokenizers-0.10.2 transformers-4.5.0\n"
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            "\n"
          ],
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        },
        {
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            "\n"
          ],
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        {
          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "38d1cceffd2a404fb5ae5f30dcd1c048",
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            "\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a0fb6870-0723-4f93-949c-9ba6dac330b9\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a0fb6870-0723-4f93-949c-9ba6dac330b9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
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              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
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              "<IPython.core.display.HTML object>"
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        {
          "output_type": "stream",
          "text": [
            "Saving New_Spark_Questions.txt to New_Spark_Questions.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "b9PjnEuVhN-U",
        "outputId": "ec696008-27fb-4b81-e8ca-afc488fe850f"
      },
      "source": [
        "encoding = tokenizer.encode_plus(text=question,text_pair=new_file, add_special=True)\n",
        "inputs = encoding['input_ids']  #Token embeddings\n",
        "sentence_embedding = encoding['token_type_ids']  #Segment embeddings\n",
        "tokens = tokenizer.convert_ids_to_tokens(inputs) #input tokens\n",
        "start_scores, end_scores = model(input_ids=torch.tensor([inputs]), token_type_ids=torch.tensor([sentence_embedding]))\n",
        "start_index = torch.argmax(start_scores)\n",
        "end_index = torch.argmax(end_scores)\n",
        "answer = ' '.join(tokens[start_index:end_index+1])\n",
        "print(answer)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Keyword arguments {'add_special': True} not recognized.\n",
            "Keyword arguments {'add_special': True} not recognized.\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (7179 > 512). Running this sequence through the model will result in indexing errors\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-dd0e30fb8f61>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msentence_embedding\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoding\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'token_type_ids'\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m#Segment embeddings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_ids_to_tokens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#input tokens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mstart_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken_type_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msentence_embedding\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mstart_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mend_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mend_scores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, start_positions, end_positions, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1782\u001b[0m             \u001b[0moutput_attentions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_attentions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1783\u001b[0m             \u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_hidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1784\u001b[0;31m             \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1785\u001b[0m         )\n\u001b[1;32m   1786\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    967\u001b[0m             \u001b[0mtoken_type_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtoken_type_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    968\u001b[0m             \u001b[0minputs_embeds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs_embeds\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 969\u001b[0;31m             \u001b[0mpast_key_values_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpast_key_values_length\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    970\u001b[0m         )\n\u001b[1;32m    971\u001b[0m         encoder_outputs = self.encoder(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    887\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    891\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/models/bert/modeling_bert.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, token_type_ids, position_ids, inputs_embeds, past_key_values_length)\u001b[0m\n\u001b[1;32m    205\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mposition_embedding_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"absolute\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m             \u001b[0mposition_embeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mposition_embeddings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mposition_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 207\u001b[0;31m             \u001b[0membeddings\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mposition_embeddings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    208\u001b[0m         \u001b[0membeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLayerNorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0membeddings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mRuntimeError\u001b[0m: The size of tensor a (7179) must match the size of tensor b (512) at non-singleton dimension 1"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnvBvRjfrVLd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1abf935d-0543-438d-82c1-0ce8a4d6f8ac"
      },
      "source": [
        "# !pip3 install sentencepiece\n",
        "# !pip3 install torch\n",
        "import torch \n",
        "def divide_chunks(l, n):\n",
        "    # =looping till length l\n",
        "    for i in range(0, len(l), n): \n",
        "        yield l[i:i + n]\n",
        "n = 512\n",
        "question_answering_model = torch.hub.load('huggingface/pytorch-transformers', 'modelForQuestionAnswering', 'bert-large-uncased-whole-word-masking-finetuned-squad') # 'bert-base-uncased') #  # 'bert-large-uncased-whole-word-masking-finetuned-squad')\n",
        "question_answering_tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'tokenizer', 'bert-large-uncased-whole-word-masking-finetuned-squad') # 'bert-base-uncased') # 'bert-base-uncased') # 'bert-large-uncased-whole-word-masking-finetuned-squad')\n",
        "# The format is paragraph first and then question\n",
        "# text_1 = new_file[0:512]\n",
        "answer_list = []\n",
        "score_list = [] \n",
        "sentences = list(divide_chunks(new_file, n))\n",
        "for entry in sentences:\n",
        "    text_1 = entry\n",
        "    text_2 = \"Would police or the FBI ever be able to access DNA or other information collected?\"\n",
        "    indexed_tokens = question_answering_tokenizer.encode(text_1, text_2, add_special_tokens=True)\n",
        "    # Search the input_ids for the first instance of the `[SEP]` token.\n",
        "    sep_index = indexed_tokens.index(question_answering_tokenizer.sep_token_id)\n",
        "    # The number of segment A tokens includes the [SEP] token istelf.\n",
        "    num_seg_a = sep_index + 1\n",
        "    # The remainder are segment B.\n",
        "    num_seg_b = len(indexed_tokens) - num_seg_a\n",
        "     # Construct the list of 0s and 1s.\n",
        "    segment_ids = [0]*num_seg_a + [1]*num_seg_b\n",
        "    # There should be a segment_id for every input token.\n",
        "    # assert len(segment_ids) == len(indexed_tokens)\n",
        "    # segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]\n",
        "    segments_tensors = torch.tensor([segment_ids])\n",
        "    tokens_tensor = torch.tensor([indexed_tokens])\n",
        "    # assert len(segments_tensors) == len(tokens_tensor)\n",
        "    # Predict the start and end positions logits\n",
        "    with torch.no_grad():\n",
        "         out = question_answering_model(tokens_tensor, token_type_ids=segments_tensors)\n",
        "         # print(out.keys())\n",
        "    # print(len(out))\n",
        "    # start_logits = out.start_logits[0].cpu().numpy()\n",
        "    # print(len(start_logits))\n",
        "    # end_logits = out.end_logits[0].cpu().numpy()\n",
        "    # print(len(end_logits))\n",
        "    # n_best_size = 512\n",
        "    # import numpy as np\n",
        "    # start_indexes = np.argsort(start_logits)[-1 : -n_best_size - 1 : -1].tolist()\n",
        "    # print(len(start_indexes))\n",
        "    # end_indexes = np.argsort(end_logits)[-1 : -n_best_size - 1 : -1].tolist()\n",
        "    # print(len(end_indexes)) \n",
        "    # new_sum = start_logits[start_index] + end_logits[end_index]\n",
        "    # get the highest prediction\n",
        "    answer = question_answering_tokenizer.decode(indexed_tokens[torch.argmax(out.start_logits):torch.argmax(out.end_logits)+1])\n",
        "    answer_list.append(answer)\n",
        "    new_score = torch.max(out.start_logits) + torch.max(out.end_logits)\n",
        "    score_list.append(new_score)\n",
        "    # for start_index in start_indexes:\n",
        "        # for end_index in end_indexes:\n",
        "           #  if start_index <= end_index:\n",
        "               # score_list.append(start_logits[start_index] + end_logits[end_index])\n",
        "# assert answer == \"puppeteer\"\n",
        "\n",
        "# Or get the total loss which is the sum of the CrossEntropy loss for the start and end token positions (set model to train mode before if used for training)\n",
        "# start_positions, end_positions = torch.tensor([12]), torch.tensor([14])\n",
        "# multiple_choice_loss = question_answering_model(tokens_tensor, token_type_ids=segments_tensors, start_positions=start_positions, end_positions=end_positions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using cache found in /root/.cache/torch/hub/huggingface_pytorch-transformers_master\n",
            "Using cache found in /root/.cache/torch/hub/huggingface_pytorch-transformers_master\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "Kf-12yBC5PTq",
        "outputId": "8ce92d27-81ab-487d-d5e6-c1b3786a84a3"
      },
      "source": [
        "# print(indexed_tokens)\n",
        "# print(len(indexed_tokens))\n",
        "print(answer_list)\n",
        "# print(score_list[-1])\n",
        "# print(min(score_list))\n",
        "# print(score_list.index(min(score_list)))\n",
        "#print(score_list[16570])\n",
        "print(len(answer_list))\n",
        "print(len(score_list))\n",
        "pip install -q tensorflow-text\n",
        "pip install -q tf-models-official\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "import tensorflow_text as text\n",
        "from official.nlp import optimization  # to create AdamW optmizer\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "tf.get_logger().setLevel('ERROR')\n",
        "reloaded_results = tf.sigmoid(reloaded_model(tf.constant(answer_list)))\n",
        "original_results = tf.sigmoid(classifier_model(tf.constant(answer_list)))\n",
        "print('Results from the saved model:')\n",
        "print_my_examples(examples, reloaded_results)\n",
        "print('Results from the model in memory:')\n",
        "print_my_examples(examples, original_results)\n",
        "# print(tokens_tensor)\n",
        "# print(segments_tensor)\n",
        "# print(segment_ids)\n",
        "#print(type(tokens_tensor))\n",
        "#print(type(segments_tensors))\n",
        "#print(tokens_tensor[0:512])\n",
        "# print(tokens_tensor[513])\n",
        "#print(segments_tensors)\n",
        "#print(indexed_tokens)\n",
        "#print(len(indexed_tokens))\n",
        "# print(answer)\n",
        "# print(start_positions)\n",
        "# print(end_positions)\n",
        "# print(multiple_choice_loss)\n",
        "# print(answer_list[-2])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[]\n",
            "0\n",
            "0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-8dd6d6ef635c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mreloaded_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigmoid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreloaded_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0moriginal_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msigmoid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifier_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstant\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Results from the saved model:'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'tf' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHudSrQTu1zs",
        "outputId": "82ace067-20bb-4141-a1ca-2fa5c56a83cf"
      },
      "source": [
        "print(out)\n",
        "print(out.start_logits)\n",
        "print(out.end_logits)\n",
        "print(torch.argmax(out.start_logits))\n",
        "print(torch.argmax(out.end_logits)+1)\n",
        "print(indexed_tokens)\n",
        "print(indexed_tokens[torch.argmax(out.start_logits):torch.argmax(out.end_logits)+1])\n",
        "answer = question_answering_tokenizer.decode(indexed_tokens[torch.argmax(out.start_logits):torch.argmax(out.end_logits)+1])\n",
        "print(answer)\n",
        "new_start_logits = out.start_logits[0].cpu().numpy()\n",
        "print(new_start_logits)\n",
        "new_end_logits = out.end_logits[0].cpu().numpy()\n",
        "print(new_end_logits)\n",
        "print(len(new_start_logits))\n",
        "print(len(new_end_logits))\n",
        "print(len(sentences[-1]))\n",
        "print(sentences[-1])\n",
        "values, indices = torch.max(out.start_logits,0)\n",
        "values2 ,indices2 = torch.max(out.end_logits,0)\n",
        "print(values)\n",
        "print(indices)\n",
        "print(values2)\n",
        "print(indices2)\n",
        "print(torch.max(out.start_logits))\n",
        "print(torch.max(out.end_logits))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "QuestionAnsweringModelOutput(loss=None, start_logits=tensor([[-6.1119, -2.2338, -4.2834, -3.9698, -4.3433, -3.9813, -4.9859, -3.7087,\n",
            "         -7.5318, -5.5533, -7.4113, -6.6638, -5.7105, -7.4322, -7.7306, -6.6689,\n",
            "         -6.1843, -6.1119, -5.0952, -5.9182, -8.3120, -6.9286, -5.5286, -6.9667,\n",
            "         -7.3169, -7.1252, -7.9022, -6.5157, -4.5324, -8.8702, -8.5403, -6.7163,\n",
            "         -7.3217, -8.8831, -6.1121]]), end_logits=tensor([[-2.5755, -6.9230, -4.5566, -6.8914, -5.3548, -6.2446, -6.6634, -5.5524,\n",
            "         -6.6779, -5.2754, -2.5002, -7.3827, -7.7456, -4.6757, -6.1072, -2.4772,\n",
            "         -2.6577, -2.5755, -7.0712, -6.3381, -7.6748, -7.3669, -4.0881, -5.7402,\n",
            "         -6.8587, -6.3404, -6.5999, -5.0765, -2.7150, -7.1813, -7.0569, -4.3638,\n",
            "         -2.6892, -5.2411, -2.5758]]), hidden_states=None, attentions=None)\n",
            "tensor([[-6.1119, -2.2338, -4.2834, -3.9698, -4.3433, -3.9813, -4.9859, -3.7087,\n",
            "         -7.5318, -5.5533, -7.4113, -6.6638, -5.7105, -7.4322, -7.7306, -6.6689,\n",
            "         -6.1843, -6.1119, -5.0952, -5.9182, -8.3120, -6.9286, -5.5286, -6.9667,\n",
            "         -7.3169, -7.1252, -7.9022, -6.5157, -4.5324, -8.8702, -8.5403, -6.7163,\n",
            "         -7.3217, -8.8831, -6.1121]])\n",
            "tensor([[-2.5755, -6.9230, -4.5566, -6.8914, -5.3548, -6.2446, -6.6634, -5.5524,\n",
            "         -6.6779, -5.2754, -2.5002, -7.3827, -7.7456, -4.6757, -6.1072, -2.4772,\n",
            "         -2.6577, -2.5755, -7.0712, -6.3381, -7.6748, -7.3669, -4.0881, -5.7402,\n",
            "         -6.8587, -6.3404, -6.5999, -5.0765, -2.7150, -7.1813, -7.0569, -4.3638,\n",
            "         -2.6892, -5.2411, -2.5758]])\n",
            "tensor(1)\n",
            "tensor(16)\n",
            "[101, 1996, 14064, 2089, 2025, 2224, 1996, 8196, 1997, 18777, 3012, 2000, 2007, 12640, 2023, 2592, 1012, 102, 2052, 2610, 2030, 1996, 8495, 2412, 2022, 2583, 2000, 3229, 6064, 2030, 2060, 2592, 5067, 1029, 102]\n",
            "[1996, 14064, 2089, 2025, 2224, 1996, 8196, 1997, 18777, 3012, 2000, 2007, 12640, 2023, 2592]\n",
            "the investigator may not use the certificate of confidentiality to withhold this information\n",
            "[-6.1119356 -2.2337549 -4.283428  -3.969836  -4.3433304 -3.9812608\n",
            " -4.9859424 -3.7087038 -7.5318193 -5.5532784 -7.411287  -6.6637707\n",
            " -5.7104907 -7.4321837 -7.730595  -6.668938  -6.1843314 -6.1118712\n",
            " -5.0952315 -5.9182134 -8.311987  -6.928551  -5.528586  -6.966721\n",
            " -7.316908  -7.125172  -7.9021826 -6.5156913 -4.5323677 -8.870165\n",
            " -8.540306  -6.716334  -7.321674  -8.883093  -6.1121354]\n",
            "[-2.575523  -6.9230394 -4.5565624 -6.891363  -5.3548384 -6.2445755\n",
            " -6.6633716 -5.5523586 -6.6779404 -5.275404  -2.5001607 -7.3826866\n",
            " -7.745634  -4.67572   -6.1071577 -2.4771636 -2.657677  -2.5754733\n",
            " -7.0712113 -6.338111  -7.674796  -7.366884  -4.088084  -5.7401776\n",
            " -6.8587275 -6.340423  -6.5999327 -5.076463  -2.7149704 -7.1813107\n",
            " -7.056886  -4.3638086 -2.689244  -5.2410965 -2.575847 ]\n",
            "35\n",
            "35\n",
            "94\n",
            " the investigator may not use the Certificate of Confidentiality to withhold this information.\n",
            "tensor([-6.1119, -2.2338, -4.2834, -3.9698, -4.3433, -3.9813, -4.9859, -3.7087,\n",
            "        -7.5318, -5.5533, -7.4113, -6.6638, -5.7105, -7.4322, -7.7306, -6.6689,\n",
            "        -6.1843, -6.1119, -5.0952, -5.9182, -8.3120, -6.9286, -5.5286, -6.9667,\n",
            "        -7.3169, -7.1252, -7.9022, -6.5157, -4.5324, -8.8702, -8.5403, -6.7163,\n",
            "        -7.3217, -8.8831, -6.1121])\n",
            "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
            "tensor([-2.5755, -6.9230, -4.5566, -6.8914, -5.3548, -6.2446, -6.6634, -5.5524,\n",
            "        -6.6779, -5.2754, -2.5002, -7.3827, -7.7456, -4.6757, -6.1072, -2.4772,\n",
            "        -2.6577, -2.5755, -7.0712, -6.3381, -7.6748, -7.3669, -4.0881, -5.7402,\n",
            "        -6.8587, -6.3404, -6.5999, -5.0765, -2.7150, -7.1813, -7.0569, -4.3638,\n",
            "        -2.6892, -5.2411, -2.5758])\n",
            "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
            "tensor(-2.2338)\n",
            "tensor(-2.4772)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uJxpx0XnJbXr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b79e75d-cf3a-49a7-81c9-7d447f3ab7d5"
      },
      "source": [
        "print(score_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[tensor(12.6420), tensor(4.4581), tensor(6.3380), tensor(0.6120), tensor(2.2263), tensor(-5.4138), tensor(-3.6631), tensor(-3.0543), tensor(-2.1806), tensor(-4.8497), tensor(-2.5107), tensor(-1.0140), tensor(0.1290), tensor(2.4614), tensor(1.2054), tensor(-4.7629), tensor(5.4050), tensor(5.9394), tensor(-1.9037), tensor(4.3760), tensor(-4.0234), tensor(-4.0367), tensor(5.0838), tensor(2.5414), tensor(-2.1504), tensor(0.4389), tensor(-5.2403), tensor(-4.2690), tensor(7.2630), tensor(-2.9806), tensor(-1.1993), tensor(3.7883), tensor(-3.5536), tensor(-3.7147), tensor(-4.3964), tensor(4.8174), tensor(0.5175), tensor(-1.5771), tensor(-5.6257), tensor(5.2289), tensor(-2.5723), tensor(0.8489), tensor(-3.6427), tensor(0.2399), tensor(-2.6823), tensor(-1.3158), tensor(6.2435), tensor(1.1641), tensor(0.3709), tensor(9.8332), tensor(-3.5650), tensor(-3.1400), tensor(-4.2936), tensor(-1.4800), tensor(-4.1353), tensor(3.1545), tensor(-0.8007), tensor(-2.0695), tensor(-1.7696), tensor(12.1450), tensor(-1.4150), tensor(-2.9288), tensor(-1.9970), tensor(-5.3638), tensor(-6.0986), tensor(1.2787), tensor(5.1869), tensor(-2.6437), tensor(0.7089), tensor(-5.9861), tensor(-4.7109)]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}