import datasets from datasets.download.download_manager import DownloadManager import pyarrow.parquet as pq _DESCRIPTION = """\ The MSRA NER dataset is a Chinese Named Entity Recognition dataset """ _CITATION = """\ @inproceedings{levow-2006-third, title = "The Third International {C}hinese Language Processing Bakeoff: Word Segmentation and Named Entity Recognition", author = "Levow, Gina-Anne", booktitle = "Proceedings of the Fifth {SIGHAN} Workshop on {C}hinese Language Processing", month = jul, year = "2006", address = "Sydney, Australia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W06-0115", pages = "108--117", } """ _URL = "https://huggingface.co/datasets/minskiter/msra_dev/resolve/main/" _URLS = { "train": _URL + "data/train.parquet", 'validation': _URL + "data/validation.parquet", "test": _URL + "data/test.parquet", } class MSRANamedEntities(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "text": datasets.Sequence(datasets.Value("string")), "labels": datasets.Sequence( datasets.features.ClassLabel( names=[ 'O', 'B-NS', 'M-NS', 'E-NS', 'S-NS', 'B-NT', 'M-NT', 'E-NT', 'S-NT', 'B-NR', 'M-NR', 'E-NR', 'S-NR' ] ) ), } ), supervised_keys=None, homepage="https://aclanthology.org/W06-0115/", 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): with open(filepath,"rb") as f: with pq.ParquetFile(f) as file: _id = -1 for i in file.iter_batches(batch_size=64): rows = i.to_pylist() for row in rows: _id+=1 yield _id, row