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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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_DESCRIPTION = """CEIL (Catalan Entity Identification and Linking). |
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This is a dataset for complex Named Eentity Reacognition (NER) created by the AINA project in the BSC for |
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Machine Learning and Language Model evaluation purposes. |
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CEIL corpus is used under [CC-by] (https://creativecommons.org/licenses/by/4.0/) licence. |
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This dataset was developed by BSC as part of the AINA project, and to enrich the Catalan Language Understanding Benchmark (CLUB). |
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""" |
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_HOMEPAGE = """https://aina.bsc.es""" |
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_URL = "https://huggingface.co/datasets/crodri/ceil/resolve/main/" |
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_TRAINING_FILE = "train.conll" |
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_DEV_FILE = "dev.conll" |
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class CEILConfig(datasets.BuilderConfig): |
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""" Builder config for the CEIL dataset """ |
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def __init__(self, **kwargs): |
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"""BuilderConfig for CEIL. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(CEILConfig, self).__init__(**kwargs) |
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class CEIL(datasets.GeneratorBasedBuilder): |
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""" CEIL dataset.""" |
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BUILDER_CONFIGS = [ |
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CEILConfig( |
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name="CEIL", |
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version=datasets.Version("2.0.0"), |
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description="CEIL 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("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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'I-product-vehicle', |
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'I-organization-sportsteam', |
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'B-location-road/railway/highway/transit', |
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'I-CW-other', |
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'B-event-other', |
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'I-CW-painting', |
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'I-person-group', |
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'B-CW-music', |
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'I-location-other', |
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'B-organization-religious', |
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'I-product-E-device', |
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'B-product-software', |
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'B-event-attack/terrorism/militaryconflict', |
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'B-organization-politicalparty', |
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'B-person-scholar/scientist', |
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'I-person-artist/author', |
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'B-CW-other', |
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'I-person-influencer', |
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'B-event-protest', |
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'I-building-other', |
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'I-organization-other', |
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'B-organization-sportsteam', |
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'B-organization-media', |
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'I-event-disaster', |
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'I-organization-privatecompany', |
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'I-event-other', |
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'B-location-other', |
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'B-product-clothing', |
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'B-organization-education', |
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'B-building-sportsfacility', |
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'I-building-shops', |
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'I-location-park', |
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'B-organization-government', |
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'I-person-politician', |
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'B-building-airport', |
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'B-CW-writtenart', |
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'B-location-park', |
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'B-location-island', |
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'I-building-hotel', |
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'B-Other', |
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'B-organization-other', |
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'B-person-group', |
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'B-event-disaster', |
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'I-organization-onlinebusiness', |
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'B-product-consumer_good', |
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'I-CW-broadcastprogram', |
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'I-person-other', |
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'B-building-hotel', |
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'B-product-vehicle', |
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'I-organization-politicalparty', |
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'B-event-political', |
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'B-location-mountain', |
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'I-organization-religious', |
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'B-GPE', |
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'I-location-mountain', |
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'I-CW-film', |
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'I-CW-music', |
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'B-location-bodiesofwater', |
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'I-location-road/railway/highway/transit', |
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'I-event-sportsevent', |
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'B-organization-onlinebusiness', |
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'I-organization-government', |
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'I-person-actor/director', |
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'B-person-athlete', |
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'I-organization-education', |
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'I-event-attack/terrorism/militaryconflict', |
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'I-product-consumer_good', |
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'I-building-hospital', |
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'B-building-shops', |
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'I-event-political', |
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'I-building-religious', |
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'B-CW-painting', |
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'I-building-sportsfacility', |
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'I-event-protest', |
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'B-building-restaurant', |
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'B-person-politician', |
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'B-product-other', |
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'I-CW-writtenart', |
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'I-product-other', |
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'I-product-food', |
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'B-event-sportsevent', |
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'B-CW-film', |
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'I-product-clothing', |
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'B-CW-broadcastprogram', |
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'I-product-software', |
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'I-person-athlete', |
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'B-product-E-device', |
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'B-person-actor/director', |
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'B-building-religious', |
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'I-GPE', |
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'B-person-artist/author', |
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'B-organization-privatecompany', |
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'I-building-restaurant', |
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'B-building-hospital', |
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'I-Other', |
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'I-person-scholar/scientist', |
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'B-person-influencer', |
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'B-person-other', |
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'I-location-bodiesofwater', |
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'I-building-airport', |
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'I-organization-media', |
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'B-product-food', |
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'B-building-other', |
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'B-building-governmentfacility', |
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'I-building-governmentfacility', |
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'I-location-island' |
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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=_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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urls_to_download = { |
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"train": f"{_URL}{_TRAINING_FILE}", |
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"dev": f"{_URL}{_DEV_FILE}", |
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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["dev"]}), |
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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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n = 0 |
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for line in f: |
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try: |
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n += 1 |
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if line.startswith("-DOCSTART-") or line == "" or line == "\n" or line == "\xa0\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('\t') |
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tokens.append(splits[0]) |
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ner_tags.append(splits[-1].rstrip()) |
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except Exception as error: |
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print(error) |
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print("line: ",n) |
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print("Error line: ",line) |
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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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