pyterrier_monoT5_direct_rerank.ipynb

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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m182.4/182.4 KB\u001b[0m \u001b[31m24.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m84.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pyterrier-t5 (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F3u9r6Wd0iTf",
        "outputId": "4ef2c810-d6eb-4c2b-977d-25566b222919"
      },
      "source": [
        "import pandas as pd\n",
        "import pyterrier as pt\n",
        "pt.init()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "terrier-assemblies 5.7 jar-with-dependencies not found, downloading to /root/.pyterrier...\n",
            "Done\n",
            "terrier-python-helper 0.0.7 jar not found, downloading to /root/.pyterrier...\n",
            "Done\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272,
          "referenced_widgets": [
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            "344c0ec4583f44ce805900f094b44862"
          ]
        },
        "id": "5fMRKXSjjd1w",
        "outputId": "20a3172f-f8e8-4330-81bb-309bb1836d91"
      },
      "source": [
        "from pyterrier_t5 import MonoT5ReRanker\n",
        "monoT5 = MonoT5ReRanker()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/792k [00:00<?, ?B/s]"
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        {
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              "Downloading:   0%|          | 0.00/1.21k [00:00<?, ?B/s]"
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              "version_minor": 0,
              "model_id": "88f37c195e674c038bc75aa665aa929f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/models/t5/tokenization_t5.py:163: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
            "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
            "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
            "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
            "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.84k [00:00<?, ?B/s]"
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/892M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aef0ce5bf3c9401784c2d43f39811337"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "example_input = pd.DataFrame([\n",
        "    ['q1', 'chemical reactions', 'd1', 'excitation mechanisms of the oxygen emission in the upper atmosphere\\nphotochemical reactions affected by mass motions and direct excitation\\ndue to mass motions are examined'\n",
        "],\n",
        "    ['q1', 'chemical reactions', 'd2', 'ion neutral reactions  a list is given of reactions which may control\\nthe chemical composition of positive ions in the ionosphere  under\\ncertain assumptions about reaction rate coefficients nitrogen is\\nsubstantially dissociated in the f region to the theoretical conclusions\\nof other investigators']\n",
        "    ], columns=['qid', 'query', 'docno', 'text'])\n",
        "example_input\n"
      ],
      "metadata": {
        "id": "olaw2OA_fouh",
        "outputId": "46366898-1659-4fc1-920c-f06eea61cf46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "  qid               query docno  \\\n",
              "0  q1  chemical reactions    d1   \n",
              "1  q1  chemical reactions    d2   \n",
              "\n",
              "                                                text  \n",
              "0  excitation mechanisms of the oxygen emission i...  \n",
              "1  ion neutral reactions  a list is given of reac...  "
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-088a2859-ba0b-416f-ad7d-0af1e3bf11d2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "monoT5.transform(example_input)\n",
        "\n",
        "# ranking is defined by the _descending_ score values, \n",
        "# or, equivalently the _ascending_ rank\n",
        "# NOT by the ordering of the dataframe\n",
        "\n",
        "# so, we see that d2 is estimated to be more relevant to the query"
      ],
      "metadata": {
        "id": "2hyEi_MQgSlG",
        "outputId": "f181e849-0b68-450f-ee04-fafae231d886",
        "colab": {
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          "height": 144,
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            "0641f427e1fd47e29381bdc5ee308f22",
            "7d0d149c3d1f4901a948a6501fdad265",
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            "085df64a383b4db7b0907d9dd3841a8c"
          ]
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "monoT5:   0%|          | 0/1 [00:00<?, ?batches/s]"
            ],
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              "  qid               query docno  \\\n",
              "0  q1  chemical reactions    d1   \n",
              "1  q1  chemical reactions    d2   \n",
              "\n",
              "                                                text      score  rank  \n",
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              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ece98dd9-0591-4704-add1-7f554afcc8d0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ece98dd9-0591-4704-add1-7f554afcc8d0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    }
  ]
}