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5639eec
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Create stsb_multi_mt_extend.py

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  1. stsb_multi_mt_extend.py +82 -0
stsb_multi_mt_extend.py ADDED
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+ # coding=utf-8
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+
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+ """STS Benchmark Multilingual dataset"""
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+
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+ import json
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+ import os
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+ import gzip
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+
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+ import datasets
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+
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+ logger = datasets.logging.get_logger(__name__)
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+
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+ _DESCRIPTION = """\
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+ STS Benchmark Multilingual dataset by deepl and google (only ar)
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+ """
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+
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+ _LANGUAGES = ["de", "en", "es", "fr", "it", "nl", "pl", "pt", "ru", "zh"]
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+
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+ _NEW_LANGUAGES = ['ar', 'id']
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+
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+
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+ class STSBMultilingual(datasets.GeneratorBasedBuilder):
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+ """STS Benchmark Multilingual"""
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+
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+ BUILDER_CONFIGS = [
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+ datasets.BuilderConfig(
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+ name=name,
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+ version=datasets.Version("1.0.0"),
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+ description=f"The STS Benchmark for {name} language{'' if name == 'en' else ' by deepl'}.",
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+ )
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+ for name in _LANGUAGES
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+ ] + [
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+ datasets.BuilderConfig(
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+ name='id',
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+ version=datasets.Version("1.0.0"),
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+ description=f"The STS Benchmark for id language by deepl.",
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+ ),
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+ datasets.BuilderConfig(
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+ name='ar',
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+ version=datasets.Version("1.0.0"),
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+ description=f"The STS Benchmark for id language by google translate.",
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+ )
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+ ]
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+
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+ DEFAULT_CONFIG_NAME = 'en'
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+
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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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+ "sentence1": datasets.Value("string"),
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+ "sentence2": datasets.Value("string"),
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+ "score": datasets.Value("float32"),
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+ },
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+ ),
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+ supervised_keys=None,
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+ homepage="https://github.com/PhilipMay/stsb-multi-mt",
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+ )
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+
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+ def _split_generators(self, dl_manager):
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+ if self.config.name == 'ar':
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+ path_or_ds = dl_manager.download('test_ar_google.jsonl')
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+ elif self.config.name == 'id':
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+ path_or_ds = dl_manager.download('test_id_deepl.jsonl')
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+ else:
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+ path_or_ds = datasets.load_dataset("stsb_multi_mt", self.config.name, split="test")
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+ return [
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+ datasets.SplitGenerator(
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+ name=datasets.Split.TEST, gen_kwargs={"path_or_ds": path_or_ds},
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+ ),
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+ ]
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+
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+ def _generate_examples(self, path_or_ds):
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+ """Yields examples."""
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+ if isinstance(path_or_ds, datasets.Dataset):
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+ for i, ins in enumerate(path_or_ds):
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+ yield i, {'sentence1': ins['sentence1'], 'sentence2': ins['sentence2'], 'score': ins['similarity_score']}
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+ else:
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+ with open(text_path) as f:
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+ for i, line in enumerate(f):
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+ yield i, json.loads(line)