Datasets:
Tasks:
Token Classification
Modalities:
Text
Languages:
English
Size:
100K - 1M
ArXiv:
Tags:
abbreviation-detection
License:
File size: 6,295 Bytes
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import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """
"""
_DESCRIPTION = """
This is the dataset repository for PLOD Dataset accepted to be published at LREC 2022.
The dataset can help build sequence labelling models for the task Abbreviation Detection.
"""
class PLODfilteredConfig(datasets.BuilderConfig):
"""BuilderConfig for Conll2003"""
def __init__(self, **kwargs):
"""BuilderConfig forConll2003.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(PLODfilteredConfig, self).__init__(**kwargs)
class PLODfilteredConfig(datasets.GeneratorBasedBuilder):
"""PLOD Filtered dataset."""
BUILDER_CONFIGS = [
PLODfilteredConfig(name="PLODfiltered", version=datasets.Version("0.0.2"), description="PLOD filtered dataset"),
]
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,
)
# _TRAINING_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-train70-filtered-pos_bio.json"
# _DEV_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-val15-filtered-pos_bio.json"
# _TEST_FILE_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/PLOS-test15-filtered-pos_bio.json"
# _TRAINING_FILE = "PLOS-train70-filtered-pos_bio.json"
# _DEV_FILE = "PLOS-val15-filtered-pos_bio.json"
# _TEST_FILE = "PLOS-test15-filtered-pos_bio.json"
_URL = "https://huggingface.co/datasets/surrey-nlp/PLOD-filtered/resolve/main/data/"
_URLS = {
"train": _URL + "PLOS-train70-filtered-pos_bio.json",
"dev": _URL + "PLOS-val15-filtered-pos_bio.json",
"test": _URL + "PLOS-test15-filtered-pos_bio.json"
}
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 _split_generators(self, dl_manager):
# """Returns SplitGenerators."""
# downloaded_train = dl_manager.download_and_extract(_TRAINING_FILE_URL)
# downloaded_val = dl_manager.download_and_extract(_DEV_FILE_URL)
# downloaded_test = dl_manager.download_and_extract(_TEST_FILE_URL)
# data_files = {
# "train": _TRAINING_FILE,
# "dev": _DEV_FILE,
# "test": _TEST_FILE,
# }
# return [
# datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
# datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
# datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_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:
# conll2003 tokens are space separated
splits = line.split(" ")
tokens.append(splits[0])
pos_tags.append(splits[1].strip())
ner_tags.append(splits[2].strip())
# last example
yield guid, {
"id": str(guid),
"tokens": tokens,
"pos_tags": pos_tags,
"ner_tags": ner_tags,
} |