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import os |
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import datasets |
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_CITATION = '' |
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_DESCRIPTION = """The dataset contains 6273 training samples, 762 validation samples and 749 test samples. |
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Each sample represents a sentence and includes the following features: sentence ID ('sent_id'), |
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list of tokens ('tokens'), list of normalised word forms ('norms'), list of lemmas ('lemmas'), |
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list of Multext-East tags ('xpos_tags), list of morphological features ('feats'), |
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and list of UPOS tags ('upos_tags'), which are encoded as class labels. |
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""" |
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_HOMEPAGE = '' |
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_LICENSE = '' |
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_URL = 'https://huggingface.co/datasets/classla/janes_tag/raw/main/data.zip' |
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_TRAINING_FILE = 'train_all.conllup' |
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_DEV_FILE = 'dev_all.conllup' |
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_TEST_FILE = 'test_all.conllup' |
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_DATA_DIR = 'data' |
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class JanesTag(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version('1.0.0') |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name='janes_tag', |
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version=VERSION, |
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description='' |
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) |
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] |
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def _info(self): |
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features = datasets.Features( |
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{ |
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'sent_id': datasets.Value('string'), |
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'tokens': datasets.Sequence(datasets.Value('string')), |
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'norms': datasets.Sequence(datasets.Value('string')), |
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'lemmas': datasets.Sequence(datasets.Value('string')), |
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'xpos_tags': datasets.Sequence(datasets.Value('string')), |
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'feats': datasets.Sequence(datasets.Value('string')), |
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'upos_tags': datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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'SCONJ VERB', 'NOUN', 'NOUN NOUN', 'CCONJ SCONJ', 'ADV X', 'ADJ', 'NOUN NUM', 'ADP VERB', |
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'CCONJ', 'SCONJ AUX', 'VERB', 'PRON PRON', 'CCONJ PART', 'ADV ADJ', 'PRON AUX', 'AUX AUX', |
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'VERB ADP', 'DET ADJ', 'ADJ NOUN', 'PART PART', 'ADV AUX', 'NOUN ADV', 'PART CCONJ', |
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'DET NOUN', 'CCONJ CCONJ', 'ADV', 'NUM', 'AUX NUM', 'ADV DET', 'ADV ADV', 'PRON VERB', |
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'ADP PRON', 'DET AUX', 'VERB ADV', 'PROPN PROPN', 'NOUN PROPN', 'ADJ ADP', 'PART AUX', |
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'PROPN NOUN', 'PROPN ADV', 'ADP NOUN', 'NUM ADV', 'NOUN ADJ', 'SCONJ', 'PART NOUN', |
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'ADV NUM', 'VERB PRON', 'PART ADJ', 'AUX', 'ADP NUM', 'PRON', 'ADP ADJ', 'INTJ', 'ADV VERB', |
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'NOUN SYM', 'PART', 'ADV PART', 'DET VERB', 'SCONJ PART', 'ADV SCONJ', 'NOUN CCONJ', |
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'NUM DET', 'ADP X', 'INTJ X', 'NOUN VERB', 'PUNCT', 'ADP', 'ADV CCONJ', 'NOUN DET', |
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'X NOUN', 'DET', 'PROPN X', 'SYM', 'PROPN NUM', 'PART VERB', 'SYM INTJ', 'ADP ADV', |
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'X PROPN', 'X X', 'PROPN', 'ADP DET', 'X', 'AUX ADV', 'NUM NOUN', 'INTJ NOUN', 'AUX PRON', |
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'PART ADV', 'PRON ADP', 'INTJ INTJ', 'VERB NOUN', 'NOUN AUX' |
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] |
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) |
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) |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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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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data_dir = os.path.join(dl_manager.download_and_extract(_URL), _DATA_DIR) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={ |
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'filepath': os.path.join(data_dir, _TRAINING_FILE), |
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'split': 'train'} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={ |
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'filepath': os.path.join(data_dir, _DEV_FILE), |
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'split': 'dev'} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={ |
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'filepath': os.path.join(data_dir, _TEST_FILE), |
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'split': 'test'} |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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with open(filepath, encoding='utf-8') as f: |
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sent_id = '' |
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tokens = [] |
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norms = [] |
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lemmas = [] |
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xpos_tags = [] |
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feats = [] |
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upos_tags = [] |
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data_id = 0 |
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for line in f: |
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if line and line != '\n' and not line.startswith('# global.columns') and not line.startswith('# text'): |
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if line.startswith('# sent_id'): |
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if tokens: |
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yield data_id, { |
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'sent_id': sent_id, |
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'tokens': tokens, |
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'norms': norms, |
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'lemmas': lemmas, |
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'xpos_tags': xpos_tags, |
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'feats': feats, |
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'upos_tags': upos_tags |
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} |
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tokens = [] |
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norms = [] |
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lemmas = [] |
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xpos_tags = [] |
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feats = [] |
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upos_tags = [] |
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data_id += 1 |
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sent_id = line.split(' = ')[1].strip() |
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else: |
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splits = line.split('\t') |
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tokens.append(splits[1].strip()) |
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norms.append(splits[2].strip()) |
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lemmas.append(splits[3].strip()) |
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upos_tags.append(splits[4].strip()) |
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xpos_tags.append(splits[5].strip()) |
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feats.append(splits[6].strip()) |
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yield data_id, { |
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'sent_id': sent_id, |
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'tokens': tokens, |
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'norms': norms, |
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'lemmas': lemmas, |
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'xpos_tags': xpos_tags, |
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'feats': feats, |
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'upos_tags': upos_tags |
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} |
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