Datasets:
ArXiv:
Tags:
License:
| """ | |
| Dataset from https://github.com/allenai/sequential_sentence_classification | |
| Dataset maintainer: @soldni | |
| """ | |
| import json | |
| from typing import Iterable, Sequence, Tuple | |
| import datasets | |
| from datasets.builder import BuilderConfig, GeneratorBasedBuilder | |
| from datasets.info import DatasetInfo | |
| from datasets.splits import Split, SplitGenerator | |
| from datasets.utils.logging import get_logger | |
| LOGGER = get_logger(__name__) | |
| _NAME = "CSAbstruct" | |
| _CITATION = """\ | |
| @inproceedings{Cohan2019EMNLP, | |
| title={Pretrained Language Models for Sequential Sentence Classification}, | |
| author={Arman Cohan, Iz Beltagy, Daniel King, Bhavana Dalvi, Dan Weld}, | |
| year={2019}, | |
| booktitle={EMNLP}, | |
| } | |
| """ | |
| _LICENSE = "Apache License 2.0" | |
| _DESCRIPTION = """\ | |
| As a step toward better document-level understanding, we explore \ | |
| classification of a sequence of sentences into their corresponding \ | |
| categories, a task that requires understanding sentences in context \ | |
| of the document. Recent successful models for this task have used \ | |
| hierarchical models to contextualize sentence representations, and \ | |
| Conditional Random Fields (CRFs) to incorporate dependencies between \ | |
| subsequent labels. In this work, we show that pretrained language \ | |
| models, BERT (Devlin et al., 2018) in particular, can be used for \ | |
| this task to capture contextual dependencies without the need for \ | |
| hierarchical encoding nor a CRF. Specifically, we construct a joint \ | |
| sentence representation that allows BERT Transformer layers to \ | |
| directly utilize contextual information from all words in all \ | |
| sentences. Our approach achieves state-of-the-art results on four \ | |
| datasets, including a new dataset of structured scientific abstracts. | |
| """ | |
| _HOMEPAGE = "https://github.com/allenai/sequential_sentence_classification" | |
| _VERSION = "1.0.0" | |
| _URL = ( | |
| "https://raw.githubusercontent.com/allenai/" | |
| "sequential_sentence_classification/master/" | |
| ) | |
| _SPLITS = { | |
| Split.TRAIN: _URL + "data/CSAbstruct/train.jsonl", | |
| Split.VALIDATION: _URL + "data/CSAbstruct/dev.jsonl", | |
| Split.TEST: _URL + "data/CSAbstruct/test.jsonl", | |
| } | |
| class CSAbstruct(GeneratorBasedBuilder): | |
| """CSAbstruct""" | |
| BUILDER_CONFIGS = [ | |
| BuilderConfig( | |
| name=_NAME, | |
| version=datasets.Version(_VERSION), | |
| description=_DESCRIPTION, | |
| ) | |
| ] | |
| def _info(self) -> DatasetInfo: | |
| class_labels = ["background", "method", "objective", "other", "result"] | |
| features = datasets.Features( | |
| { | |
| "abstract_id": datasets.Value("string"), | |
| "sentences": [datasets.Value("string")], | |
| "labels": [datasets.ClassLabel(names=class_labels)], | |
| "confs": [datasets.Value("float")], | |
| } | |
| ) | |
| return DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators( | |
| self, dl_manager: datasets.DownloadManager | |
| ) -> Sequence[SplitGenerator]: | |
| archive = dl_manager.download(_SPLITS) | |
| return [ | |
| SplitGenerator( | |
| name=split_name, # type: ignore | |
| gen_kwargs={ | |
| "split_name": split_name, | |
| "filepath": archive[split_name], # type: ignore | |
| }, | |
| ) | |
| for split_name in _SPLITS | |
| ] | |
| def _generate_examples( | |
| self, split_name: str, filepath: str | |
| ) -> Iterable[Tuple[str, dict]]: | |
| """This function returns the examples in the raw (text) form.""" | |
| LOGGER.info(f"generating examples from documents in {filepath}...") | |
| with open(filepath, mode="r", encoding="utf-8") as f: | |
| data = [json.loads(ln) for ln in f] | |
| for i, row in enumerate(data): | |
| row["abstract_id"] = f"{split_name}_{i:04d}" | |
| yield row["abstract_id"], row | |