Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
ArXiv:
License:
File size: 4,878 Bytes
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import os
import datasets
from typing import List
logger = datasets.logging.get_logger(__name__)
_CITATION = """
"""
_DESCRIPTION = """
This is the dataset repository for a subset of the PLOD Dataset published at LREC 2022 (from cleaned portion accepted at LREC COLING 2024).
The dataset can help build sequence labelling models for the task Abbreviation and Long form Detection.
"""
class PLODfilteredConfig(datasets.BuilderConfig):
"""BuilderConfig for PLOD-CW"""
def __init__(self, **kwargs):
"""BuilderConfig for PLOD-CW.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(PLODfilteredConfig, self).__init__(**kwargs)
class PLODfilteredConfig(datasets.GeneratorBasedBuilder):
"""PLOD CW dataset."""
BUILDER_CONFIGS = [
PLODfilteredConfig(name="PLOD-CW", version=datasets.Version("0.0.5"), description="PLOD CW dataset for NLP 2024"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"pos_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"ADJ",
"ADP",
"ADV",
"AUX",
"CONJ",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
"SPACE"
]
)
),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"B-O",
"B-AC",
"I-AC",
"B-LF",
"I-LF"
]
)
),
}
),
supervised_keys=None,
homepage="https://github.com/surrey-nlp/PLOD-AbbreviationDetection",
citation=_CITATION,
)
_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-CW/resolve/main/data/"
_URLS = {
"train": _URL + "train.conll",
"dev": _URL + "dev.conll",
"test": _URL + "test.conll"
}
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
urls_to_download = self._URLS
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]})
]
def _generate_examples(self, filepath):
logger.info("⏳ Generating examples from = %s", filepath)
with open(filepath, encoding="utf-8") as f:
guid = 0
tokens = []
pos_tags = []
ner_tags = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
"ner_tags": ner_tags,
}
guid += 1
tokens = []
pos_tags = []
ner_tags = []
else:
splits = line.split(" ")
tokens.append(splits[0])
pos_tags.append(splits[1])
ner_tags.append(splits[2].rstrip())
# last example
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
"ner_tags": ner_tags,
} |