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"""Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition""" |
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import os |
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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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@inproceedings{tjong-kim-sang-de-meulder-2003-introduction, |
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title = "Introduction to the {C}o{NLL}-2003 Shared Task: Language-Independent Named Entity Recognition", |
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author = "Tjong Kim Sang, Erik F. and |
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De Meulder, Fien", |
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booktitle = "Proceedings of the Seventh Conference on Natural Language Learning at {HLT}-{NAACL} 2003", |
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year = "2003", |
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url = "https://www.aclweb.org/anthology/W03-0419", |
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pages = "142--147", |
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} |
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""" |
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_DESCRIPTION = """\ |
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The shared task of CoNLL-2003 concerns language-independent named entity recognition. We will concentrate on |
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four types of named entities: persons, locations, organizations and names of miscellaneous entities that do |
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not belong to the previous three groups. |
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The CoNLL-2003 shared task data files contain four columns separated by a single space. Each word has been put on |
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a separate line and there is an empty line after each sentence. The first item on each line is a word, the second |
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a part-of-speech (POS) tag, the third a syntactic chunk tag and the fourth the named entity tag. The chunk tags |
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and the named entity tags have the format I-TYPE which means that the word is inside a phrase of type TYPE. Only |
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if two phrases of the same type immediately follow each other, the first word of the second phrase will have tag |
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B-TYPE to show that it starts a new phrase. A word with tag O is not part of a phrase. Note the dataset uses IOB2 |
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tagging scheme, whereas the original dataset uses IOB1. |
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For more details see https://www.clips.uantwerpen.be/conll2003/ner/ and https://www.aclweb.org/anthology/W03-0419 |
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""" |
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_URL = "https://huggingface.co/datasets/Geawher/Entityrecongnitionjobs/resolve/main/data.zip" |
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_TRAINING_FILE = "train.txt.txt" |
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_DEV_FILE = "valid.txt.txt" |
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_TEST_FILE = "test.txt.txt" |
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class Conll2003Config(datasets.BuilderConfig): |
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"""BuilderConfig for Conll2003""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig forConll2003. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(Conll2003Config, self).__init__(**kwargs) |
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class Conll2003(datasets.GeneratorBasedBuilder): |
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"""Conll2003 dataset.""" |
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BUILDER_CONFIGS = [ |
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Conll2003Config(name="Entityrecongnitionjobs", version=datasets.Version("1.0.0"), description="Conll2003 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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"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-ORG", |
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"I-ORG", |
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"B-LOC", |
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"I-LOC", |
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"B-MISC", |
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"I-MISC", |
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"B-MET", |
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"I-MET", |
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"B-CONT", |
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"I-CONT", |
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"B-EDU", |
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"I-EDU", |
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"B-CER", |
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"I-CER", |
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"B-EXP", |
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"I-EXP", |
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"B-SAL", |
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"I-SAL", |
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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="", |
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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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downloaded_file = dl_manager.download_and_extract(_URL) |
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data_files = { |
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"train": os.path.join(downloaded_file, _TRAINING_FILE), |
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"dev": os.path.join(downloaded_file, _DEV_FILE), |
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"test": os.path.join(downloaded_file, _TEST_FILE), |
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} |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_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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guid = 0 |
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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.startswith("-DOCSTART-") or line == "" 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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"ner_tags": ner_tags, |
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} |
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guid += 1 |
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tokens = [] |
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ner_tags = [] |
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else: |
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splits = line.split(" ") |
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tokens.append(splits[0]) |
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ner_tags.append(splits[1].rstrip()) |
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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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"ner_tags": ner_tags, |
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} |
