07: TableQA_Transformer_Pipeline.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/NLP_transformers/blob/main/TableQA_Transformer_Pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "Video help is available on https://www.youtube.com/watch?v=MclFnA00jkY&t=58s"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "# Installs"
      ]
    },
    {
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        },
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        "!pip install -q transformers==4.4.2"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 2.0MB 16.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 3.2MB 51.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 890kB 41.4MB/s \n",
            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0g5YKgdw4sXi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "8645d8fc-fdde-4466-c093-81145c8e545f"
      },
      "source": [
        "import torch\n",
        "torch.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.8.1+cu101'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lrnSxsTPhFC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b73a4b84-dac6-45e4-c3d4-79cf2b2e4dce"
      },
      "source": [
        "!pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Looking in links: https://pytorch-geometric.com/whl/torch-1.8.0+cu101.html\n",
            "Collecting torch-scatter\n",
            "\u001b[?25l  Downloading https://pytorch-geometric.com/whl/torch-1.8.0%2Bcu101/torch_scatter-2.0.6-cp37-cp37m-linux_x86_64.whl (2.5MB)\n",
            "\u001b[K     |████████████████████████████████| 2.6MB 15.8MB/s \n",
            "\u001b[?25hInstalling collected packages: torch-scatter\n",
            "Successfully installed torch-scatter-2.0.6\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gmdftz_CBWMR"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2r2JHK0N6yq"
      },
      "source": [
        "from transformers import pipeline\n",
        "import pandas as pd"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNYJ2B_H31wx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262,
          "referenced_widgets": [
            "50404b1aa3134a4aa6aaa416d3a92002",
            "97ec426391c3436b905bc81d9b5199e5",
            "0a4e79d439b54f4ea99f96801a6b5cce",
            "d7cc061c88fa405092697043b06f9f6b",
            "79d560602711499091274ac9ed551f3b",
            "12584b21f7074b74b4e9b0dab10d2b6d",
            "34fbe104703d4f868fc651a8989c80d8",
            "530bc1d10dcd4b6c9d044bab790d7380",
            "b355136977a54c4e8d586b65ac045465",
            "2e63f1308c86496a8c6574876b2547ac",
            "57369c243d9b4b7eb695a41916844638",
            "e992204f33b64d8b839cf43b4f5bf87d",
            "d3d73574b8be4ad89287460ce94ca46d",
            "69093386b27b499095212b9743ec0c62",
            "4c4f0e0c29554239b04a0d40cb42ed83",
            "f5efd035fd90415ca027665b4cac1289",
            "5fea7eb5277b4992ba9bfda42a048f0e",
            "7839885a8c6c4fb29535a8e6c9edc377",
            "cffeae7c67eb491ea4404a62528190d9",
            "570bc36e4c264808ad284613e78f0ddc",
            "83dd318ba91444ff86838d036b811d48",
            "24a9c77862c443d2b057a199a2c754f3",
            "8b0f6dfcd8854099980975828c3bc54b",
            "b018160961ca4e5687fb9849cc3bdce7",
            "2e4287c883fa4a47ab57003ca42e8597",
            "f34777a1e8144fa5be17a2da8ee8960c",
            "d01cb2c232b74496babbd22cdf12b335",
            "a7a3f35b53ff437d91a2a523b130ca3c",
            "70b6b5a2cd6945dd8134b86a7317af98",
            "17ec257249f848c286ba51a57648db4d",
            "b65ee8a317d54565bcfc4aefa982c304",
            "7c0b29ca252e4824ade07c28d68432e1",
            "2bfa766292d647459321d3d003623a54",
            "c6b2b56914024c46b683455e4d403e9e",
            "9eba780f5f2e4669bf330238b581886a",
            "4075dabc86964760888b68639e338cb0",
            "df71ae3706fe4ba290b43b76535745dd",
            "bc5fa19a22d14b788ed241ddc42fa795",
            "063cf8f815164688863b805a90b6e740",
            "cd4f2086129947a3b6d9622414f1d859"
          ]
        },
        "outputId": "fda1f29e-4132-4323-cdca-0d41e64df078"
      },
      "source": [
        "tqa = pipeline(task=\"table-question-answering\", \n",
        "               model=\"google/tapas-base-finetuned-wtq\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "50404b1aa3134a4aa6aaa416d3a92002",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1629.0, 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": "b355136977a54c4e8d586b65ac045465",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442791751.0, 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": "5fea7eb5277b4992ba9bfda42a048f0e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=262028.0, 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": "2e4287c883fa4a47ab57003ca42e8597",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=154.0, 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": "2bfa766292d647459321d3d003623a54",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=490.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07mxxHBiBYZD"
      },
      "source": [
        "# Reading Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6IxgPs-S36qW"
      },
      "source": [
        "table = pd.read_csv(\"/content/question_answer_tabular_data.csv\")\n",
        "table = table.astype(str)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Yt-WlPB6CQA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "d6e1738c-e86a-43e0-82c5-4d084750e305"
      },
      "source": [
        "table"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pos</th>\n",
              "      <th>Player</th>\n",
              "      <th>Team</th>\n",
              "      <th>Span</th>\n",
              "      <th>Innings</th>\n",
              "      <th>Runs</th>\n",
              "      <th>Highest Score</th>\n",
              "      <th>Average</th>\n",
              "      <th>Strike Rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Sachin Tendulkar</td>\n",
              "      <td>India</td>\n",
              "      <td>1989-2012</td>\n",
              "      <td>452</td>\n",
              "      <td>18426</td>\n",
              "      <td>200</td>\n",
              "      <td>44.83</td>\n",
              "      <td>86.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Kumar Sangakkara</td>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>2000-2015</td>\n",
              "      <td>380</td>\n",
              "      <td>14234</td>\n",
              "      <td>169</td>\n",
              "      <td>41.98</td>\n",
              "      <td>78.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Ricky Ponting</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1995-2012</td>\n",
              "      <td>365</td>\n",
              "      <td>13704</td>\n",
              "      <td>164</td>\n",
              "      <td>42.03</td>\n",
              "      <td>80.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Sanath Jayasuriya</td>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>1989-2011</td>\n",
              "      <td>433</td>\n",
              "      <td>13430</td>\n",
              "      <td>189</td>\n",
              "      <td>32.36</td>\n",
              "      <td>91.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Mahela Jayawardene</td>\n",
              "      <td>Sri Lanka</td>\n",
              "      <td>1998-2015</td>\n",
              "      <td>418</td>\n",
              "      <td>12650</td>\n",
              "      <td>144</td>\n",
              "      <td>33.37</td>\n",
              "      <td>78.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Virat Kohli</td>\n",
              "      <td>India</td>\n",
              "      <td>2008-2020</td>\n",
              "      <td>236</td>\n",
              "      <td>11867</td>\n",
              "      <td>183</td>\n",
              "      <td>59.85</td>\n",
              "      <td>93.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Inzamam-ul-Haq</td>\n",
              "      <td>Pakistan</td>\n",
              "      <td>1991-2007</td>\n",
              "      <td>350</td>\n",
              "      <td>11739</td>\n",
              "      <td>137</td>\n",
              "      <td>39.52</td>\n",
              "      <td>74.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Jacques Kallis</td>\n",
              "      <td>South Africa</td>\n",
              "      <td>1996-2014</td>\n",
              "      <td>314</td>\n",
              "      <td>11579</td>\n",
              "      <td>139</td>\n",
              "      <td>44.36</td>\n",
              "      <td>72.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Saurav Ganguly</td>\n",
              "      <td>India</td>\n",
              "      <td>1992-2007</td>\n",
              "      <td>300</td>\n",
              "      <td>11363</td>\n",
              "      <td>183</td>\n",
              "      <td>41.02</td>\n",
              "      <td>73.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Rahul Dravid</td>\n",
              "      <td>India</td>\n",
              "      <td>1996-2011</td>\n",
              "      <td>318</td>\n",
              "      <td>10889</td>\n",
              "      <td>153</td>\n",
              "      <td>39.16</td>\n",
              "      <td>71.24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Pos              Player          Team  ... Highest Score Average Strike Rate\n",
              "0   1    Sachin Tendulkar         India  ...           200   44.83       86.23\n",
              "1   2    Kumar Sangakkara     Sri Lanka  ...           169   41.98       78.86\n",
              "2   3       Ricky Ponting     Australia  ...           164   42.03       80.39\n",
              "3   4   Sanath Jayasuriya     Sri Lanka  ...           189   32.36        91.2\n",
              "4   5  Mahela Jayawardene     Sri Lanka  ...           144   33.37       78.96\n",
              "5   6         Virat Kohli         India  ...           183   59.85       93.39\n",
              "6   7      Inzamam-ul-Haq      Pakistan  ...           137   39.52       74.24\n",
              "7   8      Jacques Kallis  South Africa  ...           139   44.36       72.89\n",
              "8   9      Saurav Ganguly         India  ...           183   41.02        73.7\n",
              "9  10        Rahul Dravid         India  ...           153   39.16       71.24\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-HBn0MbyBa8B"
      },
      "source": [
        "# Magic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p0CW8-s3_eD1",
        "outputId": "c1dd86bd-2c8c-45a4-d72f-17aeca17f6d4"
      },
      "source": [
        "query = \"Who has scored the highest runs?\"\n",
        "print(tqa(table=table, query=query)[\"answer\"])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sachin Tendulkar\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ys1aDQdH4QSV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc7b7c72-fc4c-4982-ffb2-04f3fc3e4629"
      },
      "source": [
        "query = [\"Virat Kohli's highest score?\", \n",
        "         \"Which team does he play for?\"]\n",
        "answer = tqa(table=table, query=query)\n",
        "for ans in answer:\n",
        "    print(ans[\"answer\"])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AVERAGE > 183\n",
            "India\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nBB34HSCZdLr"
      },
      "source": [
        "-----------------------------------------"
      ]
    }
  ]
}