| """ |
| 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, |
| gen_kwargs={ |
| "split_name": split_name, |
| "filepath": archive[split_name], |
| }, |
| ) |
| 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 |
|
|
