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import tarfile |
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import conllu |
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import datasets |
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_CITATION = """\ |
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@InProceedings{VelOvrBer18, |
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author = {Erik Velldal and Lilja Ovrelid and |
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Eivind Alexander Bergem and Cathrine Stadsnes and |
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Samia Touileb and Fredrik Jorgensen}, |
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title = {{NoReC}: The {N}orwegian {R}eview {C}orpus}, |
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booktitle = {Proceedings of the 11th edition of the |
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Language Resources and Evaluation Conference}, |
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year = {2018}, |
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address = {Miyazaki, Japan}, |
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pages = {4186--4191} |
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} |
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""" |
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_DESCRIPTION = """\ |
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NoReC was created as part of the SANT project (Sentiment Analysis for Norwegian Text), a collaboration between the Language Technology Group (LTG) at the Department of Informatics at the University of Oslo, the Norwegian Broadcasting Corporation (NRK), Schibsted Media Group and Aller Media. This first release of the corpus comprises 35,194 reviews extracted from eight different news sources: Dagbladet, VG, Aftenposten, Bergens Tidende, Fædrelandsvennen, Stavanger Aftenblad, DinSide.no and P3.no. In terms of publishing date the reviews mainly cover the time span 2003–2017, although it also includes a handful of reviews dating back as far as 1998. |
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""" |
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_URL = "https://www.mn.uio.no/ifi/english/research/projects/sant/data/norec/norec-1.0.1.tar.gz" |
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class Norec(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.1") |
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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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"idx": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"lemmas": datasets.Sequence(datasets.Value("string")), |
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"pos_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names=[ |
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"ADJ", |
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"ADP", |
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"ADV", |
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"AUX", |
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"CCONJ", |
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"DET", |
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"INTJ", |
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"NOUN", |
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"NUM", |
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"PART", |
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"PRON", |
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"PROPN", |
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"PUNCT", |
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"SCONJ", |
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"SYM", |
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"VERB", |
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"X", |
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] |
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) |
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), |
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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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"head": datasets.Sequence(datasets.Value("string")), |
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"deprel": datasets.Sequence(datasets.Value("string")), |
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"deps": datasets.Sequence(datasets.Value("string")), |
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"misc": datasets.Sequence(datasets.Value("string")), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/ljos/navnkjenner", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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archive = dl_manager.download(_URL) |
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subarchive_path = "norec/conllu.tar.gz" |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"data_dir": "conllu/train", |
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"subarchive_path": subarchive_path, |
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"files": dl_manager.iter_archive(archive), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"data_dir": "conllu/dev", |
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"subarchive_path": subarchive_path, |
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"files": dl_manager.iter_archive(archive), |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"data_dir": "conllu/test", |
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"subarchive_path": subarchive_path, |
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"files": dl_manager.iter_archive(archive), |
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}, |
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), |
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] |
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def _generate_examples(self, data_dir, subarchive_path, files): |
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counter = 0 |
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for path, f in files: |
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if path == subarchive_path: |
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stream = tarfile.open(fileobj=f, mode="r|*") |
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for tarinfo in stream: |
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file_path = tarinfo.name |
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if file_path.startswith(data_dir) and file_path.endswith(".conllu"): |
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data = stream.extractfile(tarinfo).read().decode("utf-8") |
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for sent in conllu.parse(data): |
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res = { |
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"idx": sent.metadata["sent_id"], |
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"text": sent.metadata["text"], |
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"tokens": [str(token["form"]) for token in sent], |
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"lemmas": [str(token["lemma"]) for token in sent], |
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"pos_tags": [str(token["upostag"]) for token in sent], |
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"xpos_tags": [str(token["xpostag"]) for token in sent], |
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"feats": [str(token["feats"]) for token in sent], |
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"head": [str(token["head"]) for token in sent], |
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"deprel": [str(token["deprel"]) for token in sent], |
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"deps": [str(token["deps"]) for token in sent], |
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"misc": [str(token["misc"]) for token in sent], |
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} |
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yield counter, res |
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counter += 1 |
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stream.members = [] |
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del stream |
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break |
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