| import json | |
| import datasets | |
| _DESCRIPTION = """ | |
| SciFact | |
| A dataset of expert-written scientific claims paired with evidence-containing | |
| abstracts and annotated with labels and rationales. | |
| """ | |
| _CITATION = """ | |
| @InProceedings{Wadden2020FactOF, | |
| author = {David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, | |
| Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi}, | |
| title = {Fact or Fiction: Verifying Scientific Claims}, | |
| booktitle = {EMNLP}, | |
| year = 2020, | |
| } | |
| """ | |
| _DOWNLOAD_URL = "https://testerstories.com/files/ai_learn/data.tar.gz" | |
| class ScifactConfig(datasets.BuilderConfig): | |
| def __init__(self, **kwargs): | |
| super(ScifactConfig, self).__init__( | |
| version=datasets.Version("1.0.0", ""), **kwargs | |
| ) | |
| class Scifact(datasets.GeneratorBasedBuilder): | |
| VERSION = datasets.Version("0.1.0") | |
| BUILDER_CONFIGS = [ | |
| ScifactConfig(name="corpus", description="The corpus of evidence documents"), | |
| ScifactConfig( | |
| name="claims", description="The claims are split into train, test, dev" | |
| ), | |
| ] | |
| def _info(self): | |
| if self.config.name == "corpus": | |
| features = { | |
| "doc_id": datasets.Value("int32"), | |
| "title": datasets.Value("string"), | |
| "abstract": datasets.features.Sequence(datasets.Value("string")), | |
| "structured": datasets.Value("bool"), | |
| } | |
| else: | |
| features = { | |
| "id": datasets.Value("int32"), | |
| "claim": datasets.Value("string"), | |
| "evidence_doc_id": datasets.Value("string"), | |
| "evidence_label": datasets.Value("string"), | |
| "evidence_sentences": datasets.features.Sequence( | |
| datasets.Value("int32") | |
| ), | |
| "cited_doc_ids": datasets.features.Sequence(datasets.Value("int32")), | |
| } | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features(features), | |
| supervised_keys=None, | |
| homepage="https://scifact.apps.allenai.org/", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| archive = dl_manager.download(_DOWNLOAD_URL) | |
| if self.config.name == "corpus": | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": "data/corpus.jsonl", | |
| "split": "train", | |
| "files": dl_manager.iter_archive(archive), | |
| }, | |
| ), | |
| ] | |
| else: | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": "data/claims_train.jsonl", | |
| "split": "train", | |
| "files": dl_manager.iter_archive(archive), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": "data/claims_test.jsonl", | |
| "split": "test", | |
| "files": dl_manager.iter_archive(archive), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": "data/claims_dev.jsonl", | |
| "split": "dev", | |
| "files": dl_manager.iter_archive(archive), | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath, split, files): | |
| for path, f in files: | |
| if path == filepath: | |
| for id_, row in enumerate(f): | |
| data = json.loads(row.decode("utf-8")) | |
| if self.config.name == "corpus": | |
| yield id_, { | |
| "doc_id": int(data["doc_id"]), | |
| "title": data["title"], | |
| "abstract": data["abstract"], | |
| "structured": data["structured"], | |
| } | |
| else: | |
| if split == "test": | |
| yield id_, { | |
| "id": data["id"], | |
| "claim": data["claim"], | |
| "evidence_doc_id": "", | |
| "evidence_label": "", | |
| "evidence_sentences": [], | |
| "cited_doc_ids": [], | |
| } | |
| else: | |
| evidences = data["evidence"] | |
| if evidences: | |
| for id1, doc_id in enumerate(evidences): | |
| for id2, evidence in enumerate(evidences[doc_id]): | |
| yield str(id_) + "_" + str(id1) + "_" + str( | |
| id2 | |
| ), { | |
| "id": data["id"], | |
| "claim": data["claim"], | |
| "evidence_doc_id": doc_id, | |
| "evidence_label": evidence["label"], | |
| "evidence_sentences": evidence["sentences"], | |
| "cited_doc_ids": data.get( | |
| "cited_doc_ids", [] | |
| ), | |
| } | |
| else: | |
| yield id_, { | |
| "id": data["id"], | |
| "claim": data["claim"], | |
| "evidence_doc_id": "", | |
| "evidence_label": "", | |
| "evidence_sentences": [], | |
| "cited_doc_ids": data.get("cited_doc_ids", []), | |
| } | |
| break | |