| import json |
| import bz2 |
|
|
| import datasets |
| from datasets import DownloadManager, DatasetInfo |
|
|
|
|
| def _order_langs(lang1, lang2): |
| return (lang1, lang2) if lang1 < lang2 else (lang2, lang1) |
|
|
|
|
| class WSDMTConfig(datasets.BuilderConfig): |
| def __init__(self, *args, corpus, lang1, lang2, variety='base', challenge=False, **kwargs): |
| lang1, lang2 = _order_langs(lang1, lang2) |
|
|
| super().__init__( |
| *args, |
| name=f"{corpus}{'#challenge' if challenge else ''}@{lang1}-{lang2}@{variety}", |
| **kwargs, |
| ) |
| self.lang1 = lang1 |
| self.lang2 = lang2 |
| self.corpus = corpus |
| self.variety = variety |
| self.challenge = challenge |
|
|
| def path_for(self, split, lang): |
| split_path = ('challenge/' if self.challenge else '') + split |
| return f"data/{self.corpus}/{self.variety}/{split_path}/{lang}.jsonl.bz2" |
|
|
|
|
| POS_TAGS = """ADJ |
| ADP |
| ADV |
| AUX |
| CCONJ |
| DET |
| INTJ |
| NOUN |
| NUM |
| PART |
| PRON |
| PROPN |
| PUNCT |
| SCONJ |
| SYM |
| VERB |
| X""".splitlines() |
|
|
|
|
| class WSDMTDataset(datasets.GeneratorBasedBuilder): |
| BUILDER_CONFIG_CLASS = WSDMTConfig |
| config: WSDMTConfig |
|
|
| def _generate_examples(self, path_lang1, path_lang2): |
| with bz2.open(path_lang1) as f1, bz2.open(path_lang2) as f2: |
| for n, (line1, line2) in enumerate(zip(f1, f2)): |
| sid1, data1 = self._read_json_line(line1) |
| sid2, data2 = self._read_json_line(line2) |
|
|
| assert sid1 == sid2, ( |
| f"Different sentence id found for {self.config.lang1} and {self.config.lang2}: " |
| f"{sid1} != {sid2} at line {n}" |
| ) |
|
|
| data_dict = { |
| 'sid': sid1, |
| self.config.lang1: data1, |
| self.config.lang2: data2, |
| } |
|
|
| yield n, data_dict |
|
|
| @classmethod |
| def _read_json_line(cls, line): |
| obj = json.loads(line) |
| sid = obj.pop('sid') |
| sentence = obj.pop('sentence') |
| data = obj.pop('data') |
| tokens, lemmas, pos_tags, senses, is_senses, is_polysemous, *_ = zip(*data) |
| assert len(tokens) == len(lemmas) == len(pos_tags) == len(senses) == len(is_senses) == len(is_polysemous), ( |
| f"Inconsistent annotation lengths in sentence {sid}" |
| ) |
|
|
| return sid, dict( |
| sentence=sentence, |
| tokens=tokens, lemmas=lemmas, pos_tags=pos_tags, |
| sense=senses, identified_as_sense=is_senses, is_polysemous=is_polysemous, |
| ) |
|
|
| def _info(self) -> DatasetInfo: |
| language_features = dict( |
| sentence=datasets.Value("string"), |
| tokens=datasets.Sequence(datasets.Value("string")), |
| sense=datasets.Sequence(datasets.Value("string")), |
| identified_as_sense=datasets.Sequence(datasets.Value("bool")), |
| is_polysemous=datasets.Sequence(datasets.Value("bool")), |
| lemmas=datasets.Sequence(datasets.Value("string")), |
| pos_tags=datasets.Sequence(datasets.ClassLabel(names=POS_TAGS)), |
| |
| ) |
|
|
| return datasets.DatasetInfo( |
| description="empty description", |
| features=datasets.Features( |
| { |
| "sid": datasets.Value("string"), |
| self.config.lang1: language_features, |
| self.config.lang2: language_features |
| }, |
| ), |
| supervised_keys=None, |
| homepage="no-homepage", |
| citation="no-citation", |
| ) |
|
|
| def _split_generators(self, dl_manager: DownloadManager): |
|
|
| if self.config.challenge: |
| split_names = ['wsd_bias', 'adversarial'] |
| else: |
| splits_file = dl_manager.download(f'data/{self.config.corpus}/splits.txt') |
|
|
| with open(splits_file) as f: |
| split_names = [line.rstrip() for line in f] |
|
|
| urls = { |
| split: { |
| self.config.lang1: self.config.path_for(split, self.config.lang1), |
| self.config.lang2: self.config.path_for(split, self.config.lang2), |
| } |
| for split in split_names |
| if not (split == 'wsd_bias' and 'adv.' in self.config.lang1) |
| } |
| downloaded = dl_manager.download(urls) |
|
|
| return [ |
| datasets.SplitGenerator(name=split, |
| gen_kwargs=dict( |
| path_lang1=paths[self.config.lang1], |
| path_lang2=paths[self.config.lang2], |
| )) |
| for split, paths in downloaded.items() |
| ] |
|
|
|
|
| if __name__ == '__main__': |
| from datasets import load_dataset |
| load_dataset('Valahaar/wsdmt', corpus='wmt', variety='all', lang1='en', lang2='de', script_version='main') |
|
|
