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      "source": [
        "!pip install transformers\r\n",
        "!pip install torch==1.7.0+cu110 -f https://download.pytorch.org/whl/torch_stable.html\r\n",
        "!pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu110.html"
      ],
      "execution_count": null,
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          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/0c/7d5950fcd80b029be0a8891727ba21e0cd27692c407c51261c3c921f6da3/transformers-4.1.1-py3-none-any.whl (1.5MB)\n",
            "\u001b[K     |████████████████████████████████| 1.5MB 4.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.8)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
            "Collecting tokenizers==0.9.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/0f/1c/e789a8b12e28be5bc1ce2156cf87cb522b379be9cadc7ad8091a4cc107c4/tokenizers-0.9.4-cp36-cp36m-manylinux2010_x86_64.whl (2.9MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9MB 15.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.19.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 44.9MB/s \n",
            "\u001b[?25hRequirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (1.0.0)\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-0.0.43-cp36-none-any.whl size=893261 sha256=54e50dbe5ddba6f4403f2d9aadde13dda8ffe54ba452f662e1a5237f0866ceb6\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.43 tokenizers-0.9.4 transformers-4.1.1\n",
            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
            "Collecting torch==1.7.0+cu110\n",
            "\u001b[?25l  Downloading https://download.pytorch.org/whl/cu110/torch-1.7.0%2Bcu110-cp36-cp36m-linux_x86_64.whl (1137.1MB)\n",
            "\u001b[K     |███████████████████████▌        | 834.1MB 1.3MB/s eta 0:03:57tcmalloc: large alloc 1147494400 bytes == 0x3a4c6000 @  0x7fc1d347e615 0x591e47 0x4cc179 0x4cc2db 0x50a1cc 0x50beb4 0x507be4 0x509900 0x50a2fd 0x50beb4 0x507be4 0x509900 0x50a2fd 0x50cc96 0x58e683 0x50c127 0x58e683 0x50c127 0x58e683 0x50c127 0x58e683 0x5f7832 0x4e5493 0x5515c1 0x5a9dac 0x50a433 0x50cc96 0x5095c8 0x50a2fd 0x50beb4 0x507be4\n",
            "\u001b[K     |█████████████████████████████▊  | 1055.7MB 1.1MB/s eta 0:01:14tcmalloc: large alloc 1434370048 bytes == 0x7eb1c000 @  0x7fc1d347e615 0x591e47 0x4cc179 0x4cc2db 0x50a1cc 0x50beb4 0x507be4 0x509900 0x50a2fd 0x50beb4 0x507be4 0x509900 0x50a2fd 0x50cc96 0x58e683 0x50c127 0x58e683 0x50c127 0x58e683 0x50c127 0x58e683 0x5f7832 0x4e5493 0x5515c1 0x5a9dac 0x50a433 0x50cc96 0x5095c8 0x50a2fd 0x50beb4 0x507be4\n",
            "\u001b[K     |████████████████████████████████| 1137.1MB 1.2MB/s eta 0:00:01tcmalloc: large alloc 1421344768 bytes == 0xd4308000 @  0x7fc1d347e615 0x591e47 0x4cc179 0x4cc2db 0x50a1cc 0x50beb4 0x507be4 0x509900 0x50a2fd 0x50beb4 0x507be4 0x509900 0x50a2fd 0x50beb4 0x507be4 0x509900 0x50a2fd 0x50beb4 0x507be4 0x509900 0x50a2fd 0x50beb4 0x507be4 0x509900 0x50a2fd 0x50beb4 0x5095c8 0x50a2fd 0x50beb4 0x507be4 0x509900\n",
            "\u001b[K     |████████████████████████████████| 1137.1MB 16kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cu110) (1.19.4)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cu110) (0.16.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cu110) (3.7.4.3)\n",
            "Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/dist-packages (from torch==1.7.0+cu110) (0.8)\n",
            "Installing collected packages: torch\n",
            "  Found existing installation: torch 1.7.0+cu101\n",
            "    Uninstalling torch-1.7.0+cu101:\n",
            "      Successfully uninstalled torch-1.7.0+cu101\n",
            "Successfully installed torch-1.7.0+cu110\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-1.7.0+cu110.html\n",
            "Collecting torch-scatter\n",
            "\u001b[?25l  Downloading https://pytorch-geometric.com/whl/torch-1.7.0%2Bcu110/torch_scatter-2.0.5-cp36-cp36m-linux_x86_64.whl (12.0MB)\n",
            "\u001b[K     |████████████████████████████████| 12.0MB 2.6MB/s \n",
            "\u001b[?25hInstalling collected packages: torch-scatter\n",
            "Successfully installed torch-scatter-2.0.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LjdrpAKtFT2"
      },
      "source": [
        "import pandas as pd\r\n",
        "import transformers"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UAz7rSUFfOPE"
      },
      "source": [
        "from transformers import TableQuestionAnsweringPipeline\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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      "source": [
        "transformers.__version__"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
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            "text/plain": [
              "'4.1.1'"
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            "tags": []
          },
          "execution_count": 9
        }
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            "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": 107
        },
        "id": "2DmzszmLwV1W",
        "outputId": "71ee508b-fee9-4d3e-fcef-aca27538dfa0"
      },
      "source": [
        "from google.colab import files\r\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-50986948-f288-4962-8344-fb8f01daab57\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-50986948-f288-4962-8344-fb8f01daab57\">\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 Covid.json to Covid.json\n",
            "Saving faq_covidbert.csv to faq_covidbert.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Phz8527ptMNs",
        "outputId": "1a29b78d-b0d4-48ac-b9ab-adac9b946fe9"
      },
      "source": [
        "df = pd.read_csv('faq_covidbert.csv')\r\n",
        "ques = df[\"question\"][2]\r\n",
        "sentences = df['answer'].head(5)\r\n",
        "text = \" ''' \"\r\n",
        "for a in sentences:\r\n",
        "    text+='<p>'\r\n",
        "    text+=a\r\n",
        "    text+='<\\p>'\r\n",
        "text += \" ''' \"\r\n",
        "print (ques)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Why might someone blame or avoid individuals and groups (create stigma) because of COVID-19?\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyQJhBi5xB5s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315,
          "referenced_widgets": [
            "9061d62d98864b05bd902de96bc592b0",
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            "e8c1b54549ed4b16b8a8fc206c6f44dd",
            "33ed4ec67f5a4764a1d898c6babf02ef",
            "93d50374888d41c2b289ffeb9cb87396",
            "769d5a00d1504a35900ca6c836d0fd9a",
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            "a363ab4e359d483f95bb7e50adf2edb9",
            "5b6b501a2df7402384f76e72249d7263"
          ]
        },
        "outputId": "46ef6988-de76-4caa-ec9d-850da8d4bd75"
      },
      "source": [
        "from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline\r\n",
        "\r\n",
        "model_name = \"deepset/roberta-base-squad2\"\r\n",
        "model = AutoModelForQuestionAnswering.from_pretrained(model_name)\r\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\r\n",
        "\r\n",
        "task1 = 'question-answering'\r\n",
        "task2 = 'table-question-answering'\r\n",
        "task3 = 'summarization'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9061d62d98864b05bd902de96bc592b0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=571.0, 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": "93e948b105a54dfc861b10644ca2c73f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=496313727.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ca75e66be2204e0798f6908f8a1de88a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=898822.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0e51892b986446bb84df7442996fac11",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=456318.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1397143906b84e9189432dd0d092325b",
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        "id": "4uWDbvwcxE4q",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e882d1d5-d15f-43f6-b2f1-6836c8b00c63"
      },
      "source": [
        "nlp = pipeline(task1, model = model_name, tokenizer = model_name, args_parser = [])\r\n",
        "quest = ques\r\n",
        "QA_input = {\r\n",
        "    'question': quest,\r\n",
        "    'context': text,\r\n",
        "}\r\n",
        "res = nlp(QA_input, topk = 4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of RobertaModel were not initialized from the model checkpoint at deepset/roberta-base-squad2 and are newly initialized: ['roberta.pooler.dense.weight', 'roberta.pooler.dense.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oe1T0ScfxyVw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ae1d092-26e4-48c7-fd63-aa15789a0365"
      },
      "source": [
        "ans = []\r\n",
        "for x in range(0,len(res)):\r\n",
        "  ans.append(res[x]['answer'])\r\n",
        "print(ans)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['a need to blame someone', 'a need to blame', 'lack of knowledge about how COVID-19 spreads, a need to blame', 'lack of knowledge about how COVID-19 spreads']\n"
          ],
          "name": "stdout"
        }
      ]
    },
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        },
        "outputId": "50bd4f61-dbfd-4229-c756-e499cdfb1f43"
      },
      "source": [
        "import json\r\n",
        "\r\n",
        "model_name2 = 'google/tapas-medium-finetuned-wikisql-supervised'\r\n",
        "table_ans = pipeline(task2, model = model_name2, tokenizer = model_name2)\r\n",
        "f = open('Covid.json', encoding=\"UTF-8\")\r\n",
        "data1 = json.load(f)\r\n",
        "data2 = {\r\n",
        "    \"actors\": [\"brad pitt\", \"leonardo di caprio\", \"george clooney\"],\r\n",
        "    \"age\": [\"56\", \"45\", \"59\"],\r\n",
        "    \"number of movies\": [\"87\", \"53\", \"69\"],\r\n",
        "    \"date of birth\": [\"7 february 1967\", \"10 june 1996\", \"28 november 1967\"],\r\n",
        "}\r\n",
        "table = pd.DataFrame.from_dict(data1)\r\n",
        "query = \"which country has least total cases?\"\r\n",
        "res = table_ans(table, query)"
      ],
      "execution_count": null,
      "outputs": [
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      "source": [
        "res\n",
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      ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'aggregator': 'NONE', 'answer': '', 'cells': [], 'coordinates': []}"
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      "source": [
        "model_name3 = 't5-small'\r\n",
        "summary = pipeline(task3, model = model_name3)\r\n",
        "df = pd.read_csv('faq_covidbert.csv')\r\n",
        "sentences = df['answer']\r\n",
        "text = \"\"\r\n",
        "for s in sentences:\r\n",
        "    text+=a\r\n",
        "    text+=\" \"\r\n",
        "res = summary(text, min_length=5, max_length=2000)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0eb7f2b096d94554b536ac78a5efff35",
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          "output_type": "stream",
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            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at t5-small were not used when initializing T5Model: ['decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight']\n",
            "- This IS expected if you are initializing T5Model from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing T5Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ],
          "name": "stderr"
        },
        {
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        },
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          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at t5-small were not used when initializing T5ForConditionalGeneration: ['decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight']\n",
            "- This IS expected if you are initializing T5ForConditionalGeneration from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing T5ForConditionalGeneration from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Token indices sequence length is longer than the specified maximum sequence length for this model (32805 > 512). Running this sequence through the model will result in indexing errors\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Chi8sHDgFkBD"
      },
      "source": [
        "print(res)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obQnexsaXp5g"
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      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}