pyterrier_t5_vaswani.ipynb

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            "terrier-assemblies 5.4  jar-with-dependencies not found, downloading to /root/.pyterrier...\n",
            "Done\n",
            "terrier-python-helper 0.0.5  jar not found, downloading to /root/.pyterrier...\n",
            "Done\n",
            "PyTerrier 0.5.0 has loaded Terrier 5.4 (built by craigm on 2021-01-16 14:17)\n"
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          "output_type": "display_data",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=891733303.0, style=ProgressStyle(descri…"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381,
          "referenced_widgets": [
            "f2319dfb777948039e176b828bbc5636",
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            "d2da7059e6f24ddcb115aebef8a1788b",
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            "728221a42f784b7f97b813ac696b9cb3",
            "ac0af410a67b4c35af93e1b478b4e0e5",
            "b8330541a63545feacec0618a3e89f32",
            "a9959da70330425fa7d2e2c21bbdf1db"
          ]
        },
        "id": "ALVOUpA12-Pc",
        "outputId": "7fbd65ad-5b09-4114-b191-c353f1d21587"
      },
      "source": [
        "dataset = pt.get_dataset(\"irds:vaswani\")\n",
        "index_loc = \"./index_path\"\n",
        "indexer = pt.IterDictIndexer(index_loc)\n",
        "indexref = indexer.index(dataset.get_corpus_iter())"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] [starting] building docstore\n",
            "[INFO] If you have a local copy of http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz, you can symlink it here to avoid downloading it again: /root/.ir_datasets/downloads/23e5607081191b153738e81fbd834680\n",
            "[INFO] [starting] http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz\n",
            "docs_iter: 0it [00:00, ?it/s]\n",
            "http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: 0.0%| 0.00/2.13M [00:00<?, ?B/s]\u001b[A\n",
            "http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: 2.3%| 49.2k/2.13M [00:00<00:07, 284kB/s]\u001b[A\n",
            "http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: 5.4%| 115k/2.13M [00:00<00:04, 412kB/s] \u001b[A\n",
            "http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: 13.5%| 287k/2.13M [00:00<00:02, 760kB/s]\u001b[A\n",
            "http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: 31.6%| 672k/2.13M [00:00<00:01, 1.41MB/s]\u001b[A\n",
            "\n",
            "\u001b[A[INFO] [finished] http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: [00:00] [2.13MB] [3.12MB/s]\n",
            "docs_iter: 0it [00:01, ?it/s]\n",
            "http://ir.dcs.gla.ac.uk/resources/test_collections/npl/npl.tar.gz: [00:00] [2.13MB] [2.93MB/s]\u001b[A\n",
            "docs_iter: 11429it [00:01, 8710.83it/s]\n",
            "[INFO] [finished] docs_iter: [00:01] [11429it] [8700.80it/s]\n",
            "[INFO] [finished] building docstore [1.32s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f2319dfb777948039e176b828bbc5636",
              "version_minor": 0,
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='vaswani documents', max=11429.0, style=ProgressStyle(desc…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGSrg3MyBgt5"
      },
      "source": [
        "bm25 = pt.BatchRetrieve(indexref, wmodel=\"BM25\") % 100\n",
        "mono_pipeline = bm25 >> pt.text.get_text(dataset, \"text\") >> monoT5\n",
        "duo_pipeline = mono_pipeline % 10 >> duoT5"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313,
          "referenced_widgets": [
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        },
        "id": "xGXAbTltKyG-",
        "outputId": "8c650ca0-13d9-44c2-f01f-b1acfda00cfe"
      },
      "source": [
        "pt.Experiment(\n",
        "  [\n",
        "   bm25,\n",
        "   mono_pipeline,\n",
        "   duo_pipeline,\n",
        "  ],\n",
        "  dataset.get_topics(),\n",
        "  dataset.get_qrels(),\n",
        "  names=[\"BM25\", \"BM25 >> monoT5\", \"BM25 >> monoT5 >> duoT5\"],\n",
        "  eval_metrics=[\"map\", \"recip_rank\", \"P.10\", \"ndcg_cut.10\"]\n",
        ")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] NumExpr defaulting to 2 threads.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='monoT5', max=2325.0, style=ProgressStyle(description_widt…"
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          },
          "metadata": {
            "tags": []
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "99ff3a4063b04e5f8538124f1d26c421",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='monoT5', max=2325.0, style=ProgressStyle(description_widt…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "346a9b44f6cc4cc29851849f64acf355",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='duoT5', max=93.0, style=ProgressStyle(description_width='…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "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>name</th>\n",
              "      <th>map</th>\n",
              "      <th>recip_rank</th>\n",
              "      <th>P_10</th>\n",
              "      <th>ndcg_cut_10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>BM25</td>\n",
              "      <td>0.272523</td>\n",
              "      <td>0.725587</td>\n",
              "      <td>0.352688</td>\n",
              "      <td>0.446609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BM25 &gt;&gt; monoT5</td>\n",
              "      <td>0.295071</td>\n",
              "      <td>0.750839</td>\n",
              "      <td>0.413978</td>\n",
              "      <td>0.490703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BM25 &gt;&gt; monoT5 &gt;&gt; duoT5</td>\n",
              "      <td>0.157311</td>\n",
              "      <td>0.655615</td>\n",
              "      <td>0.413978</td>\n",
              "      <td>0.464646</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      name       map  recip_rank      P_10  ndcg_cut_10\n",
              "0                     BM25  0.272523    0.725587  0.352688     0.446609\n",
              "1           BM25 >> monoT5  0.295071    0.750839  0.413978     0.490703\n",
              "2  BM25 >> monoT5 >> duoT5  0.157311    0.655615  0.413978     0.464646"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zd22qHDLAun-"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}