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