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answerability example content
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examples/answerability_detection/answerability_detection_msmarco.ipynb

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"The data is present in *JSONL* format where each object contains a sample having the two sentences as ``sentence1`` and ``sentence2``. We consider ``gold_label`` field as the label which can have value: entailment, contradiction or neutral.\n",
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"We already provide a sample transformation function ``snli_entailment_to_tsv`` to convert this data to required tsv format. Contradiction and neutral labels are mapped to 0 representing non-entailment scenario. Only entailment label is mapped to 1.\n",
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"We already provide a sample transformation function ``msmarco_answerability_detection_to_tsv`` to convert this data to required tsv format. Data is transformed into sentence pair classification format, with query-positive context pair labeled as 1 (answerable) and query-negative context pair labeled as 0 (non-answerable)\n",
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"Running data transformations will save the required train, dev and test tsv data files under ``data`` directory in root of library. For more details on the data transformation process, refer to <a href=\"https://multi-task-nlp.readthedocs.io/en/latest/data_transformations.html\">data transformations</a> in documentation.\n",
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