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| import datasets |
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|
| logger = datasets.logging.get_logger(__name__) |
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| _CITATION = """\ |
| @article{ding2021few, |
| title={Few-NERD: A Few-Shot Named Entity Recognition Dataset}, |
| author={Ding, Ning and Xu, Guangwei and Chen, Yulin and Wang, Xiaobin and Han, Xu and Xie, Pengjun and Zheng, Hai-Tao and Liu, Zhiyuan}, |
| journal={arXiv preprint arXiv:2105.07464}, |
| year={2021} |
| }""" |
|
|
| _DESCRIPTION = """\ |
| Recently, considerable literature has grown up around the theme of few-shot named entity recognition (NER), but little published benchmark |
| data specifically focused on the practical and challenging task. Current approaches collect existing supervised NER datasets and reorganize |
| them into the few-shot setting for empirical study. These strategies conventionally aim to recognize coarse-grained entity types with few |
| examples, while in practice, most unseen entity types are fine-grained. In this paper, we present FEW-NERD, a large-scale human-annotated |
| few-shot NER dataset with a hierarchy of 8 coarse-grained and 66 fine-grained entity types. FEW-NERD consists of 188,238 sentences from |
| Wikipedia, 4,601,160 words are included and each is annotated as context or a part of a two-level entity type. To the best of our knowledge, |
| this is the first few-shot NER dataset and the largest human-crafted NER dataset. We construct benchmark tasks with different emphases to |
| comprehensively assess the generalization capability of models. Extensive empirical results and analysis show that FEW-NERD is challenging |
| and the problem requires further research. We make Few-NERD public at https://nigding97.github.io/fewnerd/ |
| """ |
|
|
|
|
| class NERDConfig(datasets.BuilderConfig): |
| """BuilderConfig for NERD""" |
|
|
| def __init__(self, **kwargs): |
| """BuilderConfig for NERD. |
| Args: |
| **kwargs: keyword arguments forwarded to super. |
| """ |
| super(NERDConfig, self).__init__(**kwargs) |
|
|
|
|
| class NERD(datasets.GeneratorBasedBuilder): |
| """Conll2012 dataset.""" |
|
|
| BUILDER_CONFIGS = [ |
| NERDConfig(name="nerd", version=datasets.Version("1.0.0"), description="NERD dataset"), |
| ] |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "id": datasets.Value("string"), |
| "tokens": datasets.Sequence(datasets.Value("string")), |
| "tags": datasets.Sequence( |
| datasets.features.ClassLabel( |
| names=['O', 'I-ART', 'I-BUILDING', 'I-EVENT', 'I-LOC', 'I-ORG', 'I-MISC', 'I-PER', 'I-PRODUCT'] |
| ) |
| ), |
| "tags_fine": datasets.Sequence( |
| datasets.features.ClassLabel( |
| names=[ |
| 'O', |
| 'I-ART_broadcastprogram', |
| 'I-ART_film', |
| 'I-ART_music', |
| 'I-ART_other', |
| 'I-ART_painting', |
| 'I-ART_writtenart', |
| 'I-BUILDING_airport', |
| 'I-BUILDING_hospital', |
| 'I-BUILDING_hotel', |
| 'I-BUILDING_library', |
| 'I-BUILDING_other', |
| 'I-BUILDING_restaurant', |
| 'I-BUILDING_sportsfacility', |
| 'I-BUILDING_theater', |
| 'I-EVENT_attack/battle/war/militaryconflict', |
| 'I-EVENT_disaster', |
| 'I-EVENT_election', |
| 'I-EVENT_other', |
| 'I-EVENT_protest', |
| 'I-EVENT_sportsevent', |
| 'I-LOC_GPE', |
| 'I-LOC_bodiesofwater', |
| 'I-LOC_island', |
| 'I-LOC_mountain', |
| 'I-LOC_other', |
| 'I-LOC_park', |
| 'I-LOC_road/railway/highway/transit', |
| 'I-ORG_company', |
| 'I-ORG_education', |
| 'I-ORG_government/governmentagency', |
| 'I-ORG_media/newspaper', |
| 'I-ORG_other', |
| 'I-ORG_politicalparty', |
| 'I-ORG_religion', |
| 'I-ORG_showorganization', |
| 'I-ORG_sportsleague', |
| 'I-ORG_sportsteam', |
| 'I-MISC_astronomything', |
| 'I-MISC_award', |
| 'I-MISC_biologything', |
| 'I-MISC_chemicalthing', |
| 'I-MISC_currency', |
| 'I-MISC_disease', |
| 'I-MISC_educationaldegree', |
| 'I-MISC_god', |
| 'I-MISC_language', |
| 'I-MISC_law', |
| 'I-MISC_livingthing', |
| 'I-MISC_medical', |
| 'I-PER_actor', |
| 'I-PER_artist/author', |
| 'I-PER_athlete', |
| 'I-PER_director', |
| 'I-PER_other', |
| 'I-PER_politician', |
| 'I-PER_scholar', |
| 'I-PER_soldier', |
| 'I-PRODUCT_airplane', |
| 'I-PRODUCT_car', |
| 'I-PRODUCT_food', |
| 'I-PRODUCT_game', |
| 'I-PRODUCT_other', |
| 'I-PRODUCT_ship', |
| 'I-PRODUCT_software', |
| 'I-PRODUCT_train', |
| 'I-PRODUCT_weapon' |
| ] |
| ) |
| ), |
| } |
| ), |
| supervised_keys=None, |
| homepage="https://catalog.ldc.upenn.edu/LDC2013T19", |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| urls_to_download = { |
| 'train': 'train.txt', |
| 'validation': 'validation.txt', |
| 'test': 'test.txt', |
| } |
| downloaded_files = dl_manager.download_and_extract(urls_to_download) |
|
|
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), |
| datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["validation"]}), |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), |
| ] |
|
|
| def _generate_examples(self, filepath): |
| logger.info("⏳ Generating examples from = %s", filepath) |
| |
| with open(filepath, encoding="utf-8") as f: |
| lines = f.readlines() |
|
|
| guid = 0 |
| tokens = [] |
| tags = [] |
| tags_fine = [] |
|
|
| for line in lines: |
| if line.startswith("-DOCSTART_") or line.strip() == "" or line == "\n": |
| if tokens: |
| yield guid, { |
| 'id': str(guid), |
| 'tokens': tokens, |
| 'tags': tags, |
| 'tags_fine': tags_fine, |
| } |
| guid += 1 |
| tokens = [] |
| tags = [] |
| tags_fine = [] |
| else: |
| |
| splits = line.split('\t') |
| tokens.append(splits[0]) |
| tags.append(splits[1]) |
| tags_fine.append(splits[2].rstrip()) |
| |
| yield guid, { |
| 'id': str(guid), |
| 'tokens': tokens, |
| 'tags': tags, |
| 'tags_fine': tags_fine, |
| } |
|
|
