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import os
import datasets
from typing import List
import json
logger = datasets.logging.get_logger(__name__)
_CITATION = """
"""
_DESCRIPTION = """
This is the dataset repository for SDU Dataset from SDU workshop at AAAI22.
The dataset can help build sequence labelling models for the task Abbreviation Detection.
"""
class SDUtestConfig(datasets.BuilderConfig):
"""BuilderConfig for Conll2003"""
def __init__(self, **kwargs):
"""BuilderConfig forConll2003.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(SDUtestConfig, self).__init__(**kwargs)
class SDUtestConfig(datasets.GeneratorBasedBuilder):
"""SDU Filtered dataset."""
BUILDER_CONFIGS = [
SDUtestConfig(name="SDUtest", version=datasets.Version("0.0.2"), description="SDU test dataset"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"B-O",
"B-AC",
"I-AC",
"B-LF",
"I-LF"
]
)
),
}
),
supervised_keys=None,
homepage="",
citation=_CITATION,
)
_URL = "https://huggingface.co/datasets/surrey-nlp/SDU-test/raw/main/"
_URLS = {
"train+dev": _URL + "sdu_data_trunc.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+dev"]}),
#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):
"""This function returns the examples in the raw (text) form."""
logger.info("generating examples from = %s", filepath)
with open(filepath) as f:
plod = json.load(f)
for object in plod:
id_ = int(object['id'])
yield id_, {
"id": str(id_),
"tokens": object['tokens'],
#"pos_tags": object['pos_tags'],
"ner_tags": object['ner_tags'],
} |