Datasets:
Tasks:
Token Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
named-entity-recognition
Languages:
English
Size:
1K - 10K
ArXiv:
License:
File size: 4,890 Bytes
966ef1c 8810dba 966ef1c 8810dba 966ef1c 8810dba 966ef1c 8810dba 966ef1c 8810dba 966ef1c 8810dba 966ef1c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 |
import os
import datasets
from typing import List
import json
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[3].rstrip())
# last example
if tokens:
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
"ner_tags": ner_tags,
} |