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"""MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition""" |
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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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@misc{malmasi2022multiconer, |
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title={MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition}, |
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author={Shervin Malmasi and Anjie Fang and Besnik Fetahu and Sudipta Kar and Oleg Rokhlenko}, |
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year={2022}, |
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eprint={2208.14536}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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""" |
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_DESCRIPTION = """\ |
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We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki \ |
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sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. \ |
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This dataset is designed to represent contemporary challenges in NER, including low-context scenarios (short \ |
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and uncased text), syntactically complex entities like movie titles, and long-tail entity distributions. The \ |
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26M token dataset is compiled from public resources using techniques such as heuristic-based sentence sampling, \ |
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template extraction and slotting, and machine translation. We applied two NER models on our dataset: a baseline \ |
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XLM-RoBERTa model, and a state-of-the-art GEMNET model that leverages gazetteers. The baseline achieves moderate \ |
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performance (macro-F1=54%), highlighting the difficulty of our data. GEMNET, which uses gazetteers, improvement \ |
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significantly (average improvement of macro-F1=+30%). MultiCoNER poses challenges even for large pre-trained \ |
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language models, and we believe that it can help further research in building robust NER systems. MultiCoNER \ |
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is publicly available at https://registry.opendata.aws/multiconer/ and we hope that this resource will help \ |
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advance research in various aspects of NER. |
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""" |
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subset_to_dir = { |
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"bn": "BN-Bangla", |
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"de": "DE-German", |
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"en": "EN-English", |
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"es": "ES-Spanish", |
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"fa": "FA-Farsi", |
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"hi": "HI-Hindi", |
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"ko": "KO-Korean", |
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"nl": "NL-Dutch", |
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"ru": "RU-Russian", |
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"tr": "TR-Turkish", |
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"zh": "ZH-Chinese", |
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"multi": "MULTI_Multilingual", |
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"mix": "MIX_Code_mixed", |
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} |
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class MultiCoNERConfig(datasets.BuilderConfig): |
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"""BuilderConfig for MultiCoNER""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for MultiCoNER. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(MultiCoNERConfig, self).__init__(**kwargs) |
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class MultiCoNER(datasets.GeneratorBasedBuilder): |
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"""MultiCoNER dataset.""" |
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BUILDER_CONFIGS = [ |
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MultiCoNERConfig( |
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name="bn", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Bangla dataset", |
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), |
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MultiCoNERConfig( |
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name="de", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER German dataset", |
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), |
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MultiCoNERConfig( |
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name="en", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER English dataset", |
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), |
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MultiCoNERConfig( |
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name="es", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Spanish dataset", |
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), |
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MultiCoNERConfig( |
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name="fa", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Farsi dataset", |
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), |
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MultiCoNERConfig( |
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name="hi", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Hindi dataset", |
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), |
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MultiCoNERConfig( |
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name="ko", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Korean dataset", |
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), |
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MultiCoNERConfig( |
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name="nl", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Dutch dataset", |
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), |
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MultiCoNERConfig( |
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name="ru", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Russian dataset", |
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), |
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MultiCoNERConfig( |
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name="tr", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Turkish dataset", |
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), |
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MultiCoNERConfig( |
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name="zh", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Chinese dataset", |
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), |
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MultiCoNERConfig( |
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name="multi", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Multilingual dataset", |
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), |
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MultiCoNERConfig( |
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name="mix", |
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version=datasets.Version("1.0.0"), |
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description="MultiCoNER Mixed dataset", |
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), |
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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("int32"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"ner_tags": 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", |
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"I-PER", |
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"B-LOC", |
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"I-LOC", |
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"B-CORP", |
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"I-CORP", |
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"B-GRP", |
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"I-GRP", |
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"B-PROD", |
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"I-PROD", |
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"B-CW", |
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"I-CW", |
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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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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": f"{subset_to_dir[self.config.name].upper()}/{self.config.name}_train.conll", |
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"dev": f"{subset_to_dir[self.config.name].upper()}/{self.config.name}_dev.conll", |
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"test": f"{subset_to_dir[self.config.name].upper()}/{self.config.name}_test.conll", |
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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( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={"filepath": downloaded_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": downloaded_files["dev"]}, |
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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": downloaded_files["test"]}, |
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), |
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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, "r", encoding="utf8") as f: |
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guid = -1 |
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tokens = [] |
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ner_tags = [] |
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for line in f: |
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if line.strip().startswith("# id"): |
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guid += 1 |
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tokens = [] |
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ner_tags = [] |
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elif " _ _ " in line: |
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splits = line.split(" _ _ ") |
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tokens.append(splits[0].strip()) |
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ner_tags.append(splits[1].strip()) |
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elif len(line.strip()) == 0: |
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if len(tokens) >= 1 and len(tokens) == len(ner_tags): |
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yield guid, { |
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"id": guid, |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
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tokens = [] |
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ner_tags = [] |
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else: |
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continue |
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if len(tokens) >= 1 and len(tokens) == len(ner_tags): |
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yield guid, { |
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"id": guid, |
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"tokens": tokens, |
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"ner_tags": ner_tags, |
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} |
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