Datasets:
Tasks:
Sentence Similarity
Modalities:
Text
Formats:
json
Sub-tasks:
semantic-similarity-scoring
Size:
10K - 100K
ArXiv:
License:
| import json | |
| import datasets | |
| import os | |
| _CITATION = """\ | |
| """ | |
| _LICENSE = """\ | |
| """ | |
| _DESCRIPTION = """\ | |
| SemEval 2022 Task 8: Multilingual News Article Similarity | |
| """ | |
| _LANGUAGES_TRAIN = { | |
| "en": "en", | |
| "de": "de", | |
| "es": "es", | |
| "pl": "pl", | |
| "tr": "tr", | |
| "fr": "fr", | |
| "ar": "ar", | |
| "de-en": "de-en", | |
| } | |
| _LANGUAGES_TEST = { | |
| "en": "en", | |
| "de": "de", | |
| "es": "es", | |
| "pl": "pl", | |
| "tr": "tr", | |
| "ar": "ar", | |
| "ru": "ru", | |
| "zh": "zh", | |
| "fr": "fr", | |
| "de-en": "de-en", | |
| "es-en": "es-en", | |
| "it": "it", | |
| "pl-en": "pl-en", | |
| "zh-en": "zh-en", | |
| "es-it": "es-it", | |
| "de-fr": "de-fr", | |
| "de-pl": "de-pl", | |
| "fr-pl": "fr-pl", | |
| } | |
| _LANGUAGES = {**_LANGUAGES_TRAIN, **_LANGUAGES_TEST} | |
| _ALL_LANGUAGES = "all_languages" | |
| _HOMEPAGE_URL = "https://competitions.codalab.org/competitions/33835" | |
| _DOWNLOAD_URL = "{lang}/{split}.jsonl" | |
| _VERSION = "1.0.0" | |
| class STS22Task8Config(datasets.BuilderConfig): | |
| """BuilderConfig for STS22Task8Config.""" | |
| def __init__(self, languages=None, **kwargs): | |
| super(STS22Task8Config, self).__init__(version=datasets.Version(_VERSION, ""), **kwargs), | |
| self.languages = languages | |
| class STS22Task8(datasets.GeneratorBasedBuilder): | |
| """Multilingual News Article Similarity""" | |
| BUILDER_CONFIGS = [ | |
| STS22Task8Config( | |
| name=_ALL_LANGUAGES, | |
| languages=_LANGUAGES, | |
| description="Multilingual News Article Similarity", | |
| ) | |
| ] + [ | |
| STS22Task8Config( | |
| name=lang, | |
| languages=[lang], | |
| description=f"{_LANGUAGES[lang]} examples from a collection of multilingual news articles", | |
| ) | |
| for lang in _LANGUAGES | |
| ] | |
| BUILDER_CONFIG_CLASS = STS22Task8Config | |
| DEFAULT_CONFIG_NAME = _ALL_LANGUAGES | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "sentence1": datasets.Value("string"), | |
| "sentence2": datasets.Value("string"), | |
| "score": datasets.Value("float32"), | |
| }, | |
| ), | |
| supervised_keys=None, | |
| license=_LICENSE, | |
| homepage=_HOMEPAGE_URL, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # train_urls = [_DOWNLOAD_URL.format(split="train", lang=lang) for lang in self.config.languages if lang in _LANGUAGES_TRAIN] | |
| test_urls = [ | |
| _DOWNLOAD_URL.format(split="test", lang=lang) for lang in self.config.languages if lang in _LANGUAGES_TEST | |
| ] | |
| # train_paths = dl_manager.download_and_extract(train_urls) | |
| test_paths = dl_manager.download_and_extract(test_urls) | |
| return [ | |
| # datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"file_paths": train_paths}), | |
| datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"file_paths": test_paths}), | |
| ] | |
| def _generate_examples(self, file_paths): | |
| row_count = 0 | |
| for file_path in file_paths: | |
| with open(file_path, "r", encoding="utf-8") as f: | |
| for line in f: | |
| yield row_count, json.loads(line) | |
| row_count += 1 | |