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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{huggingface:dataset, |
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title = {Luganda, Kanuri, and Hausa NER Dataset}, |
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author = {multiple authors}, |
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year = {2022} |
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} |
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""" |
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_DESCRIPTION = """ |
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LugandaPII is a dataset that includes named entities such as PERSON, ORG, LOCATION, NORP, USERID, and DATE. |
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The dataset is available in Lum, Kanuri, and Hausa languages, distributed across train, validation, and test splits. |
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""" |
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_URL = "https://github.com/EricPeter/pii/raw/main/data" |
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_TRAINING_FILE = "train.txt" |
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_VAL_FILE = "val.txt" |
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_TEST_FILE = "test.txt" |
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class LugPIIConfig(datasets.BuilderConfig): |
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"""Configuration for LugandaPII dataset.""" |
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def __init__(self, **kwargs): |
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super().__init__(**kwargs) |
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class Masakhaner(datasets.GeneratorBasedBuilder): |
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"""Generator for Masakhaner dataset.""" |
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BUILDER_CONFIGS = [ |
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LugPIIConfig(name="lug", version=datasets.Version("1.0.0"), description="PII NER dataset for Luganda."), |
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LugPIIConfig(name="hau", version=datasets.Version("1.0.0"), description="PII NER dataset for Hausa."), |
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LugPIIConfig(name="knr", version=datasets.Version("1.0.0"), description="PII NER dataset for Kanuri."), |
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LugPIIConfig(name="lum", version=datasets.Version("1.0.0"), description="PII NER dataset for Lum."), |
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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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"id": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"ner_tags": datasets.Sequence(datasets.features.ClassLabel(names=['B-DATE', |
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'B-GOVT_ID', |
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'B-LOC', |
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'B-LOCATION', |
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'B-NORP', |
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'B-ORG', |
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'B-PERSON', |
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'B-USERID', |
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'B-USER_ID', |
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'I-DATE', |
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'I-GOVT_ID', |
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'I-LOC', |
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'I-LOCATION', |
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'I-NORP', |
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'I-ORG', |
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'I-PERSON', |
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'I-USERID', |
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'I-USER_ID', |
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'L-DATE', |
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'L-GOVT_ID', |
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'L-LOC', |
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'L-LOCATION', |
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'L-NORP', |
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'L-ORG', |
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'L-PERSON', |
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'L-USERID', |
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'L-USER_ID', |
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'O', |
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'U-DATE', |
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'U-GOVT_ID', |
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'U-LOCATION', |
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'U-NORP', |
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'U-ORG', |
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'U-PERSON', |
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'U-USERID'])), |
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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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lang_code = self.config.name |
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urls_to_download = { |
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"train": f"{_URL}/{lang_code}/{_TRAINING_FILE}", |
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"val": f"{_URL}/{lang_code}/{_VAL_FILE}", |
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"test": f"{_URL}/{lang_code}/{_TEST_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["val"]}), |
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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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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.strip() == "": |
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if tokens: |
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yield guid, {"id": str(guid), "tokens": tokens, "ner_tags": ner_tags} |
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guid += 1 |
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tokens = [] |
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ner_tags = [] |
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continue |
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splits = line.strip().split() |
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tokens.append(splits[0]) |
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ner_tags.append(splits[1]) |
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if tokens: |
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yield guid, {"id": str(guid), "tokens": tokens, "ner_tags": ner_tags} |
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