| | import datasets |
| | import os |
| | import json |
| |
|
| |
|
| | _CITATION = """ |
| | |
| | """ |
| |
|
| | _DESCRIPTION = """ |
| | |
| | """ |
| |
|
| | class Builder(datasets.GeneratorBasedBuilder): |
| | VERSION = datasets.Version("1.0.0") |
| |
|
| | BUILDER_CONFIGS = [ |
| | datasets.BuilderConfig(name=name, version=datasets.Version("1.0.0"), description=_DESCRIPTION) |
| | for name in ["regular", "trec"] |
| | ] |
| |
|
| | def _info(self): |
| | features = datasets.Features( |
| | { |
| | "text": datasets.Value("string"), |
| | "references": datasets.Sequence({ |
| | "id": datasets.Value("string"), |
| | "text": datasets.Value("string"), |
| | "is_gold": datasets.Value("bool"), |
| | "relevance": datasets.Value("int64"), |
| | "rank": datasets.Value("int64"), |
| |
|
| | }), |
| | "golds": datasets.Sequence({ |
| | "id": datasets.Value("string"), |
| | "text": datasets.Value("string"), |
| | "is_gold": datasets.Value("bool"), |
| | "relevance": datasets.Value("int64"), |
| | "rank": datasets.Value("int64"), |
| | }), |
| | } |
| | ) |
| | return datasets.DatasetInfo( |
| | description=_DESCRIPTION, |
| | features=features, |
| | homepage="", |
| | license="", |
| | citation=_CITATION, |
| | ) |
| |
|
| | def _split_generators(self, dl_manager): |
| | train_json = dl_manager.download(os.path.join(self.config.name, "train.jsonl")) |
| | valid_json = dl_manager.download(os.path.join(self.config.name, "valid.jsonl")) |
| |
|
| | return [ |
| | datasets.SplitGenerator( |
| | name=datasets.Split.TRAIN, |
| | gen_kwargs={"path": train_json}, |
| | ), |
| | datasets.SplitGenerator( |
| | name=datasets.Split.VALIDATION, |
| | gen_kwargs={"path": valid_json}, |
| | ), |
| | ] |
| |
|
| | |
| | def _generate_examples(self, path): |
| | with open(path, encoding="utf-8") as f: |
| | for key, row in enumerate(f): |
| | yield key, json.loads(row) |
| |
|