Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
coreference-resolution
Languages:
English
Size:
< 1K
License:
| """SciCo""" | |
| import os | |
| from datasets.arrow_dataset import DatasetTransformationNotAllowedError | |
| from datasets.utils import metadata | |
| import jsonlines | |
| import datasets | |
| _CITATION = """\ | |
| @inproceedings{ | |
| cattan2021scico, | |
| title={SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts}, | |
| author={Arie Cattan and Sophie Johnson and Daniel S. Weld and Ido Dagan and Iz Beltagy and Doug Downey and Tom Hope}, | |
| booktitle={3rd Conference on Automated Knowledge Base Construction}, | |
| year={2021}, | |
| url={https://openreview.net/forum?id=OFLbgUP04nC} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| SciCo is a dataset for hierarchical cross-document coreference resolution | |
| over scientific papers in the CS domain. | |
| """ | |
| _DATA_URL = "./data.tar" | |
| class Scico(datasets.GeneratorBasedBuilder): | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| homepage="https://scico.apps.allenai.org/", | |
| features=datasets.Features( | |
| { | |
| "flatten_tokens": datasets.features.Sequence(datasets.features.Value("string")), | |
| "flatten_mentions": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("int32"), length=3)), | |
| "tokens": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("string"))), | |
| "doc_ids": datasets.features.Sequence(datasets.features.Value("int32")), | |
| "metadata": datasets.features.Sequence( | |
| { | |
| "title": datasets.features.Value("string"), | |
| "paper_sha": datasets.features.Value("string"), | |
| "fields_of_study": datasets.features.Value("string"), | |
| "Year": datasets.features.Value("string"), | |
| "BookTitle": datasets.features.Value("string"), | |
| "url": datasets.features.Value("string") | |
| } | |
| ), | |
| "sentences": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("int32")))), | |
| "mentions": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("int32"), length=4)), | |
| "relations": datasets.features.Sequence(datasets.features.Sequence(datasets.features.Value("int32"), length=2)), | |
| "id": datasets.Value("int32"), | |
| "source": datasets.Value("string"), | |
| "hard_10": datasets.features.Value("bool"), | |
| "hard_20": datasets.features.Value("bool"), | |
| "curated": datasets.features.Value("bool") | |
| } | |
| ), | |
| supervised_keys=None, | |
| citation = _CITATION) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(_DATA_URL) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "test.jsonl")} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "dev.jsonl")} | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "train.jsonl")} | |
| ), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| with jsonlines.open(filepath, 'r') as f: | |
| for i, topic in enumerate(f): | |
| topic['hard_10'] = topic['hard_10'] if 'hard_10' in topic else False | |
| topic['hard_20'] = topic['hard_20'] if 'hard_20' in topic else False | |
| topic["curated"] = topic["curated"] if "curated" in topic else False | |
| yield i, topic | |