Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
ArXiv:
License:
Delete PLOD-CW.py
Browse files- PLOD-CW.py +0 -84
PLOD-CW.py
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import os
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import datasets
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from typing import List
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logger = datasets.logging.get_logger(__name__)
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_CITATION = """
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"""
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_DESCRIPTION = """
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This is the dataset repository for a subset of the PLOD Dataset published at LREC 2022 (from cleaned portion accepted at LREC COLING 2024).
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The dataset can help build sequence labelling models for the task Abbreviation and Long form Detection.
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"""
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class PLODfilteredConfig(datasets.BuilderConfig):
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"""BuilderConfig for PLOD-CW"""
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def __init__(self, **kwargs):
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"""BuilderConfig for PLOD-CW.
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Args:
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**kwargs: keyword arguments forwarded to super.
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"""
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super(PLODfilteredConfig, self).__init__(**kwargs)
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class PLODfilteredConfig(datasets.GeneratorBasedBuilder):
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"""PLOD CW dataset."""
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BUILDER_CONFIGS = [
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PLODfilteredConfig(name="PLOD-CW", version=datasets.Version("0.0.5"), description="PLOD CW dataset for NLP 2024"),
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]
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def _info(self):
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=datasets.Features(
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{
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# "id": datasets.Value("string"),
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"tokens": datasets.Sequence(datasets.Value("string")),
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"pos_tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"ADJ",
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"ADP",
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"ADV",
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"AUX",
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"CONJ",
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"CCONJ",
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"DET",
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"INTJ",
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"NOUN",
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"NUM",
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"PART",
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"PRON",
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"PROPN",
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"PUNCT",
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"SCONJ",
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"SYM",
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"VERB",
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"X",
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"SPACE"
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]
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)
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),
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"ner_tags": datasets.Sequence(
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datasets.features.ClassLabel(
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names=[
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"B-O",
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"B-AC",
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"I-AC",
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"B-LF",
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"I-LF"
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]
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)
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),
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}
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),
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supervised_keys=None,
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homepage="https://github.com/surrey-nlp/PLOD-AbbreviationDetection",
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citation=_CITATION,
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)
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