Upload myxnli.py with huggingface_hub

myxnli.py

@@ -0,0 +1,143 @@
+from pathlib import Path
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+
+_CITATION = """
+@misc{myXNLI2023,
+    title = "myXNLI",
+    author = "akhtet",
+    year = "202",
+    url = "https://github.com/akhtet/myXNLI",
+}
+"""
+
+_DATASETNAME = "myxnli"
+
+_DESCRIPTION = """
+The myXNLI corpus is a collection of Myanmar language data designed for the Natural Language Inference (NLI) task, which
+originated from the XNLI and MultiNLI English datasets. The 7,500 sentence pairs from the XNLI English development and
+test sets are human-translated into Myanmar. The 392,702 data from the NLI English training data is translated using
+machine translation. In addition, it also extends its scope by adding Myanmar translations to the XNLI 15-language
+parallel corpus, to create a 16-language parallel corpus.
+"""
+
+_HOMEPAGE = "https://github.com/akhtet/myXNLI"
+
+_LANGUAGES = ["mya"]
+
+_LICENSE = Licenses.CC_BY_NC_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://huggingface.co/datasets/akhtet/myXNLI/resolve/main/data/train-00000-of-00001-2614419e00195781.parquet",
+        "dev": "https://huggingface.co/datasets/akhtet/myXNLI/resolve/main/data/validation-00000-of-00001-9c168eb31d1d810b.parquet",
+        "test": "https://huggingface.co/datasets/akhtet/myXNLI/resolve/main/data/test-00000-of-00001-0fd9f93baf8c9cdb.parquet",
+    },
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]
+
+_SOURCE_VERSION = "1.1.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class MyXNLIDataset(datasets.GeneratorBasedBuilder):
+    """The myXNLI corpus is a collection of Myanmar language data designed for the Natural Language Inference task."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=_DATASETNAME,
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_pairs",
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="seacrowd_pairs",
+            subset_id=_DATASETNAME,
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "genre": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=["contradiction", "entailment", "neutral"]),
+                    "sentence1_en": datasets.Value("string"),
+                    "sentence2_en": datasets.Value("string"),
+                    "sentence1_my": datasets.Value("string"),
+                    "sentence2_my": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_pairs":
+            features = schemas.pairs_features(["contradiction", "entailment", "neutral"])
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> list[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+        urls = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_dir, "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": data_dir, "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={"filepath": data_dir, "split": "dev"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> tuple[int, dict]:
+        if self.config.schema == "source":
+            df = pd.read_parquet(filepath[split])
+            for i, row in df.iterrows():
+                yield i, {
+                    "genre": row["genre"],
+                    "label": row["label"],
+                    "sentence1_en": row["sentence1_en"],
+                    "sentence2_en": row["sentence2_en"],
+                    "sentence1_my": row["sentence1_my"],
+                    "sentence2_my": row["sentence2_my"],
+                }
+
+        elif self.config.schema == "seacrowd_pairs":
+            df = pd.read_parquet(filepath[split])
+            for i, row in df.iterrows():
+                yield i, {
+                    "id": str(i),
+                    "text_1": row["sentence1_my"],
+                    "text_2": row["sentence2_my"],
+                    "label": row["label"],
+                }