Datasets:
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
biomedical-information-retrieval
citation-prediction-retrieval
passage-retrieval
news-retrieval
argument-retrieval
zero-shot-information-retrieval
License:
Delete loading script
Browse files- nfcorpus.py +0 -58
nfcorpus.py
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import json
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import csv
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import os
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import datasets
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logger = datasets.logging.get_logger(__name__)
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_DESCRIPTION = "FIQA Dataset"
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_SPLITS = ["corpus", "queries"]
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URL = ""
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_URLs = {subset: URL + f"{subset}.jsonl.gz" for subset in _SPLITS}
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class BEIR(datasets.GeneratorBasedBuilder):
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"""BEIR BenchmarkDataset."""
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name=name,
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description=f"This is the {name} in the FiQA dataset.",
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) for name in _SPLITS
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features({
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"_id": datasets.Value("string"),
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"title": datasets.Value("string"),
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"text": datasets.Value("string"),
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}),
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supervised_keys=None,
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)
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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my_urls = _URLs[self.config.name]
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data_dir = dl_manager.download_and_extract(my_urls)
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return [
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datasets.SplitGenerator(
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name=self.config.name,
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# These kwargs will be passed to _generate_examples
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gen_kwargs={"filepath": data_dir},
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),
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]
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def _generate_examples(self, filepath):
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"""Yields examples."""
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with open(filepath, encoding="utf-8") as f:
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texts = f.readlines()
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for i, text in enumerate(texts):
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text = json.loads(text)
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if 'metadata' in text: del text['metadata']
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if "title" not in text: text["title"] = ""
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yield i, text
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