Create sts22-crosslingual-sts.py

sts22-crosslingual-sts.py

@@ -0,0 +1,117 @@
+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 AmazonReviewsMulti(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