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