pyterrier_doc2query_vaswani.ipynb

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   "source": [
    "%pip install -q python-terrier\n",
    "%pip install --upgrade -q git+https://github.com/terrierteam/pyterrier_doc2query.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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    "outputId": "4bbaff89-2b0a-41d7-b6e9-af0b7b9fc4ce"
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTerrier 0.8.1 has loaded Terrier 5.6 (built by craigmacdonald on 2021-09-17 13:27)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pyterrier as pt\n",
    "pt.init()"
   ]
  },
  {
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    "outputId": "ba564941-5c29-402a-ddab-6d00853ce530"
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   "outputs": [],
   "source": [
    "from pyterrier_doc2query import Doc2Query\n",
    "doc2query = Doc2Query()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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       "  docno                                               text\n",
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   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame([\n",
    "    {'docno': 'd1', 'text': \"The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated\"}\n",
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    "df"
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       "  docno                                               text  \\\n",
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    "doc2query(df)"
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     "text": [
      "vaswani documents: 100%|██████████| 11429/11429 [18:57<00:00, 10.05it/s]\n"
     ]
    }
   ],
   "source": [
    "dataset = pt.get_dataset(\"irds:vaswani\")\n",
    "index_loc = \"./index_path\"\n",
    "indexer = Doc2Query(append=True, batch_size=20, num_samples=10) >> pt.IterDictIndexer(index_loc)\n",
    "indexref = indexer.index(dataset.get_corpus_iter())"
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "from pyterrier.measures import *\n",
    "pt.Experiment([\n",
    "    pt.BatchRetrieve.from_dataset('vaswani', wmodel=\"BM25\"),\n",
    "    pt.BatchRetrieve(indexref, wmodel=\"BM25\"),\n",
    "  ],\n",
    "  dataset.get_topics(),\n",
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    ")"
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