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import datasets |
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_CITATION = "" |
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_DESCRIPTION = """\ |
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HisGermaNER is another NER dataset from historical German newspapers. |
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In the first release of our dataset, 11 newspapers from 1710 to 1840 from the Austrian National Library (ONB) are selected, resulting in 100 pages. |
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""" |
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class HisGermaNERConfig(datasets.BuilderConfig): |
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"""BuilderConfig for HisGermaNER""" |
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def __init__(self, data_url, **kwargs): |
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super(HisGermaNERConfig, self).__init__(**kwargs) |
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self.data_url = data_url |
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class HisGermaNER(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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HisGermaNERConfig( |
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name="HisGermaNER", |
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version=datasets.Version("0.0.1"), |
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description="HisGermaNER Dataset", |
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data_url="https://huggingface.co./datasets/stefan-it/HisGermaNER/resolve/main/splits/HisGermaNER_v0_", |
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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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"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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] |
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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://huggingface.co./datasets/stefan-it/HisGermaNER", |
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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 generator for dataset splits.""" |
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download_urls = { |
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split: self.config.data_url + split + ".tsv" for split in ["train", "dev", "test"] |
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} |
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downloaded_files = dl_manager.download_and_extract(download_urls) |
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splits = [ |
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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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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}) |
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] |
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return splits |
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def _generate_examples(self, filepath): |
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with open(filepath, "rt", encoding="utf-8") as f_p: |
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current_tokens = [] |
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current_tags = [] |
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sentence_counter = 0 |
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for line in f_p: |
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line = line.strip() |
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if not line: |
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if len(current_tokens) > 0: |
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sentence = ( |
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sentence_counter, { |
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"id": str(sentence_counter), |
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"tokens": current_tokens, |
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"ner_tags": current_tags, |
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} |
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) |
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sentence_counter += 1 |
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current_tokens = [] |
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current_tags = [] |
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yield sentence |
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continue |
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if line.startswith("TOKEN"): |
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continue |
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if line.startswith("# "): |
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continue |
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token, tag, misc = line.split("\t") |
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current_tokens.append(token) |
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current_tags.append(tag) |
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if len(current_tokens) > 0: |
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yield sentence_counter, { |
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"id": str(sentence_counter), |
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"tokens": current_tokens, |
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"ner_tags": current_tags, |
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} |
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