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"""
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
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