Datasets:
Tasks:
Token Classification
Modalities:
Text
Sub-tasks:
parsing
Languages:
English
Size:
10M - 100M
License:
File size: 3,012 Bytes
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"""\
Annotated Reference Strings dataset synthesized using CSL processor on citations obtained from CrossRef, JSTOR and
PubMed
"""
import gzip
import json
import os
import datasets
_CITATION = """\
@techreport{kee2021,
author = {Yuan Chuan Kee},
title = {Synthesis of a large dataset of annotated reference strings for developing citation parsers},
institution = {National University of Singapore},
year = {2021}
}
"""
_DESCRIPTION = """\
A repository of reference strings annotated using CSL processor using citations obtained from various sources.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://www.github.com/kylase"
_LICENSE = "cc-by-4.0"
_BASE_URL = "https://huggingface.co/datasets/yuanchuan/annotated_reference_strings/resolve/main/data"
_URL_FORMAT = "{base_url}/{source}-part-{part:05}.jsonl.gz"
_SOURCES_PARTS = {
"crossref": 16,
"pubmed": 32,
"jstor": 1
}
_URLs = {
"default": [
_URL_FORMAT.format(base_url=_BASE_URL, source=source, part=i)
for source, total_parts in _SOURCES_PARTS.items()
for i in range(1, total_parts + 1)
]
}
class AnnotatedReferenceStringsDataset(datasets.GeneratorBasedBuilder):
"""Annotated Reference Strings dataset"""
VERSION = datasets.Version("0.2.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="default", version=VERSION,
description="This dataset is the raw representation without tokenization."),
]
DEFAULT_CONFIG_NAME = "default"
def _info(self):
features = datasets.Features(
{
"source": datasets.Value("string"),
"lang": datasets.Value("string"),
"entry_type": datasets.Value("string"),
"doi_prefix": datasets.Value("string"),
"csl_style": datasets.Value("string"),
"content": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_urls = _URLs[self.config.name]
files = dl_manager.download(data_urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": files,
"split": "train",
},
)
]
def _generate_examples(self, filepaths, split):
id_ = 0
for filepath in filepaths:
with gzip.open(open(filepath, "rb"), "rt", encoding="utf-8") as f:
for line in f:
if line:
example = json.loads(line)
yield id_, example
id_ += 1
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