pyterrier_t5_trec-covid.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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              "version_minor": 0,
              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=891733303.0, style=ProgressStyle(descri…"
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          "metadata": {
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253,
          "referenced_widgets": [
            "83aebbd385fd49afa7ef55185e75f633",
            "5725a48c96254aceaf32a15116d2ca48",
            "adc84a510dad4005b11fe36670acac34",
            "188a85b929d44dc09b146ab14d6adb84",
            "88fc04d467ed48dfa4cbfff477deb868",
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            "735bea7252d14de6a4c2085698eaad87",
            "9fd81137bb8b46b89b26c8c7365fe25e"
          ]
        },
        "id": "ALVOUpA12-Pc",
        "outputId": "5e84e638-2c02-4c02-f9cc-1db983023859"
      },
      "source": [
        "dataset = pt.get_dataset(\"irds:cord19/trec-covid\")\n",
        "index_loc = \"./index_path\"\n",
        "indexer = pt.IterDictIndexer(index_loc)\n",
        "indexref = indexer.index(dataset.get_corpus_iter(), fields=('title', 'abstract'))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[INFO] If you have a local copy of https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/2020-07-16/metadata.csv, you can symlink it here to avoid downloading it again: /root/.ir_datasets/downloads/80d664e496b8b7e50a39c6f6bb92e0ef\n",
            "[INFO] [starting] https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/2020-07-16/metadata.csv\n",
            "[INFO] [finished] https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/2020-07-16/metadata.csv: [00:14] [269MB] [18.5MB/s]\n",
            "[INFO] [starting] building docstore\n",
            "docs_iter: 192509it [00:10, 17669.07it/s]\n",
            "[INFO] [finished] docs_iter: [00:10] [192509it] [17667.11it/s]\n",
            "[INFO] [finished] building docstore [10.91s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "83aebbd385fd49afa7ef55185e75f633",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, description='cord19/trec-covid documents', max=192509.0, style=Progres…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "11:26:23.439 [ForkJoinPool-1-worker-3] WARN  o.t.structures.indexing.Indexer - Adding an empty document to the index (8is9x9sc) - further warnings are suppressed\n",
            "\n",
            "11:27:40.396 [ForkJoinPool-1-worker-3] WARN  o.t.structures.indexing.Indexer - Indexed 60 empty documents\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NcUQj7CDcMW"
      },
      "source": [
        "bm25 = pt.BatchRetrieve(indexref, wmodel=\"BM25\") % 100\n",
        "mono_pipeline = bm25 >> pt.text.get_text(dataset, \"abstract\") >> monoT5\n",
        "duo_pipeline = mono_pipeline % 10 >> duoT5"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480,
          "referenced_widgets": [
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        "id": "xGXAbTltKyG-",
        "outputId": "005b7ccd-871e-4db6-e4ef-b9414996afa4"
      },
      "source": [
        "pt.Experiment(\n",
        "  [\n",
        "   bm25,\n",
        "   mono_pipeline,\n",
        "   duo_pipeline,\n",
        "  ],\n",
        "  dataset.get_topics('title'),\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] If you have a local copy of https://ir.nist.gov/covidSubmit/data/topics-rnd5.xml, you can symlink it here to avoid downloading it again: /root/.ir_datasets/downloads/0307a37b6b9f1a5f233340a769d538ea\n",
            "[INFO] [starting] https://ir.nist.gov/covidSubmit/data/topics-rnd5.xml\n",
            "[INFO] [finished] https://ir.nist.gov/covidSubmit/data/topics-rnd5.xml: [00:00] [18.7kB] [5.90MB/s]\n",
            "[INFO] If you have a local copy of https://ir.nist.gov/covidSubmit/data/qrels-covid_d5_j0.5-5.txt, you can symlink it here to avoid downloading it again: /root/.ir_datasets/downloads/8138424a59daea0aba751c8a891e5f54\n",
            "[INFO] [starting] https://ir.nist.gov/covidSubmit/data/qrels-covid_d5_j0.5-5.txt\n",
            "[INFO] [finished] https://ir.nist.gov/covidSubmit/data/qrels-covid_d5_j0.5-5.txt: [00:03] [1.14MB] [365kB/s]\n",
            "[INFO] NumExpr defaulting to 2 threads.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "00472b2355cf40769d778df10212af21",
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              "HBox(children=(FloatProgress(value=0.0, description='monoT5', max=1250.0, style=ProgressStyle(description_widt…"
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          },
          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (548 > 512). Running this sequence through the model will result in indexing errors\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='monoT5', max=1250.0, style=ProgressStyle(description_widt…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "69da26da792440f5b2cc0b24e65ab829",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='duoT5', max=50.0, style=ProgressStyle(description_width='…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Token indices sequence length is longer than the specified maximum sequence length for this model (541 > 512). Running this sequence through the model will result in indexing errors\n"
          ],
          "name": "stderr"
        },
        {
          "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>0.076117</td>\n",
              "      <td>0.807094</td>\n",
              "      <td>0.678</td>\n",
              "      <td>0.601847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BM25 &gt;&gt; monoT5</td>\n",
              "      <td>0.081632</td>\n",
              "      <td>0.869000</td>\n",
              "      <td>0.740</td>\n",
              "      <td>0.676241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BM25 &gt;&gt; monoT5 &gt;&gt; duoT5</td>\n",
              "      <td>0.015044</td>\n",
              "      <td>0.813000</td>\n",
              "      <td>0.720</td>\n",
              "      <td>0.646023</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      name       map  recip_rank   P_10  ndcg_cut_10\n",
              "0                     BM25  0.076117    0.807094  0.678     0.601847\n",
              "1           BM25 >> monoT5  0.081632    0.869000  0.740     0.676241\n",
              "2  BM25 >> monoT5 >> duoT5  0.015044    0.813000  0.720     0.646023"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C-x5-joFChfa"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}