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Download UFSAC.py from liyucheng/UFSAC: direct link, hf CLI and curl.
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https://huggingface.co/datasets/liyucheng/UFSAC/resolve/main/UFSAC.py
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hf download hf://datasets/liyucheng/UFSAC/UFSAC.py
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curl -L -o UFSAC.py https://huggingface.co/datasets/liyucheng/UFSAC/resolve/main/UFSAC.py
2.67 kB
| import datasets | |
| import xml.etree.cElementTree as ET | |
| from glob import glob | |
| import os | |
| _UFSAC_FILE = 'ufsac-public-2.1.tar.xz' | |
| class UFSAC(datasets.GeneratorBasedBuilder): | |
| BUILDER_CONFIG_CLASS = datasets.BuilderConfig | |
| def _info(self): | |
| feature = { | |
| 'tokens': datasets.Sequence(datasets.Value('string')), | |
| 'lemmas': datasets.Sequence(datasets.Value('string')), | |
| 'pos_tags': datasets.Sequence(datasets.Value('string')), | |
| 'target_idx': datasets.Value('int32'), | |
| 'sense_keys': datasets.Sequence(datasets.Value('string')), | |
| } | |
| return datasets.DatasetInfo( | |
| features=datasets.Features(feature), | |
| description = 'UFSAC: the unified Sense Annotated Corpora and Tool' | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(_UFSAC_FILE) | |
| return datasets.SplitGenerator(name = datasets.Split.TRAIN, gen_kwargs={'data_dir': data_dir}), | |
| def _generate_examples(self, data_dir): | |
| used_sents = set() | |
| count = 0 | |
| for file in glob(os.path.join(data_dir, 'ufsac-public-2.1/*.xml')): | |
| context = ET.iterparse(file, events=('start', 'end')) | |
| event, root = next(context) | |
| for event, element in context: | |
| if element.tag == 'paragraph': | |
| para = element | |
| if element.tag != 'sentence': | |
| continue | |
| if event == 'end' and element.tag == 'sentence': | |
| para.remove(element) | |
| sent = element | |
| words = sent.findall('word') | |
| tokens = [token.attrib['surface_form'] if 'surface_form' in token.attrib else '_' for token in words] | |
| sent_key = ''.join([token.lower() for token in tokens]) | |
| if sent_key in used_sents: | |
| continue | |
| used_sents.add(sent_key) | |
| lemmas = [token.attrib['lemma'] if 'lemma' in token.attrib else '_' for token in words] | |
| pos_tags = [token.attrib['pos'] if 'pos' in token.attrib else '_' for token in words] | |
| for index, word in enumerate(words): | |
| if 'wn30_key' in word.attrib: | |
| senses = word.attrib['wn30_key'].split(';') | |
| yield count, { | |
| 'tokens': tokens, | |
| 'lemmas': lemmas, | |
| 'pos_tags': pos_tags, | |
| 'target_idx': index, | |
| 'sense_keys': senses | |
| } | |
| count+=1 | |