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