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', **kwargs): lang1, lang2 = _order_langs(lang1, lang2) super().__init__( *args, name=f"{corpus}@{lang1}-{lang2}@{variety}", **kwargs, ) self.lang1 = lang1 self.lang2 = lang2 self.corpus = corpus self.variety = variety def path_for(self, split, lang): return f"data/{self.corpus}/{self.variety}/{split}/{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)), # pos_tags=datasets.Sequence(datasets.Value("string")), ) 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): 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 } 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() ]