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import datasets
from datasets.download.download_manager import DownloadManager
import pyarrow.parquet as pq
_DESCRIPTION = """\
The Weibo NER dataset is a Chinese Named Entity Recognition dataset
drawn from the social media website Sina Weibo.
"""
_CITATION = """\
@inproceedings{peng-dredze-2015-named,
title = "Named Entity Recognition for {C}hinese
Social Media with Jointly Trained Embeddings",
author = "Peng, Nanyun and Dredze, Mark",
booktitle = "Proceedings of the 2015 Conference on
Empirical Methods in Natural Language Processing",
month = sep,
year = "2015",
address = "Lisbon, Portugal",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D15-1064",
doi = "10.18653/v1/D15-1064",
pages = "548--554",
}
"""
_URL = "https://huggingface.co/datasets/minskiter/weibo/resolve/main/"
_URLS = {
"train": _URL + "data/train.parquet",
"validation": _URL + "data/validation.parquet",
"test": _URL + "data/test.parquet",
}
class WeiboNamedEntities(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"text": datasets.Value("string"),
"labels": datasets.Sequence(
datasets.features.ClassLabel(
names=[
'O',
'B-PER.NAM',
'I-PER.NAM',
'E-PER.NAM',
'S-PER.NAM',
'B-ORG.NAM',
'I-ORG.NAM',
'E-ORG.NAM',
'S-ORG.NAM',
'B-LOC.NAM',
'I-LOC.NAM',
'E-LOC.NAM',
'S-LOC.NAM',
'B-GPE.NAM',
'I-GPE.NAM',
'E-GPE.NAM',
'S-GPE.NAM',
'B-PER.NOM',
'I-PER.NOM',
'E-PER.NOM',
'S-PER.NOM',
'B-ORG.NOM',
'I-ORG.NOM',
'E-ORG.NOM',
'S-ORG.NOM',
'B-LOC.NOM',
'I-LOC.NOM',
'E-LOC.NOM',
'S-LOC.NOM',
'B-GPE.NOM',
'I-GPE.NOM',
'E-GPE.NOM',
'S-GPE.NOM',
]
)
),
}
),
supervised_keys=None,
homepage="https://aclanthology.org/D15-1064/",
citation=_CITATION,
)
def _split_generators(self, dl_manager: DownloadManager):
urls_to_download = _URLS
download_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": download_files["train"]},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": download_files["validation"]},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": download_files["test"]},
),
]
def _generate_examples(self, filepath):
file = pq.ParquetFile(filepath)
_id = -1
for i in file.iter_batches(batch_size=64):
rows = i.to_pylist()
for row in rows:
_id+=1
yield _id, row
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