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xnli.py

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+# Some code referenced from https://huggingface.co/datasets/xnli/blob/main/xnli.py
+
+"""
+The Cross-lingual Natural Language Inference (XNLI) corpus is a crowd-sourced collection of 5,000 test and 2,500 dev pairs for the MultiNLI corpus. The pairs are annotated with textual entailment and translated into 14 languages: French, Spanish, German, Greek, Bulgarian, Russian, Turkish, Arabic, Vietnamese, Thai, Chinese, Hindi, Swahili and Urdu. This results in 112.5k annotated pairs. Each premise can be associated with the corresponding hypothesis in the 15 languages, summing up to more than 1.5M combinations.
+"""
+
+import csv
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@InProceedings{conneau2018xnli,
+  author = "Conneau, Alexis
+        and Rinott, Ruty
+        and Lample, Guillaume
+        and Williams, Adina
+        and Bowman, Samuel R.
+        and Schwenk, Holger
+        and Stoyanov, Veselin",
+  title = "XNLI: Evaluating Cross-lingual Sentence Representations",
+  booktitle = "Proceedings of the 2018 Conference on Empirical Methods
+               in Natural Language Processing",
+  year = "2018",
+  publisher = "Association for Computational Linguistics",
+  location = "Brussels, Belgium",
+}
+"""
+
+_DATASETNAME = "xnli"
+
+_DESCRIPTION = """\
+XNLI is a subset of a few thousand examples from MNLI which has been translated into a 14 different languages (some low-ish resource). As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).
+"""
+
+_HOMEPAGE = "https://github.com/facebookresearch/XNLI"
+
+# We follow ISO639-3 language code (https://iso639-3.sil.org/code_tables/639/data)
+_LANGUAGES = ["tha", "vie"]
+_LANGUAGE_MAPPER = {"tha": "th", "vie": "vi"}
+
+_LICENSE = Licenses.CC_BY_NC_4_0.value
+
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: {
+        "train": "https://dl.fbaipublicfiles.com/XNLI/XNLI-MT-1.0.zip",
+        "test": "https://dl.fbaipublicfiles.com/XNLI/XNLI-1.0.zip",
+    }
+}
+
+_SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]
+
+_SOURCE_VERSION = "1.1.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+class XNLIDataset(datasets.GeneratorBasedBuilder):
+    """
+    XNLI is an evaluation corpus for language transfer and cross-lingual sentence classification in 15 languages.
+    In SeaCrowd, we currently only have Thailand and Vietnam Language.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    subsets = ["xnli.tha", "xnli.vie"]
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{sub}_source",
+            version=datasets.Version(_SOURCE_VERSION),
+            description=f"{sub} source schema",
+            schema="source",
+            subset_id=f"{sub}",
+        )
+        for sub in subsets
+    ] + [
+        SEACrowdConfig(
+            name=f"{sub}_seacrowd_pairs",
+            version=datasets.Version(_SEACROWD_VERSION),
+            description=f"{sub} SEACrowd schema",
+            schema="seacrowd_pairs",
+            subset_id=f"{sub}",
+        )
+        for sub in subsets
+    ]
+
+    DEFAULT_CONFIG_NAME = "xnli.vie_source"
+    labels = ["contradiction", "entailment", "neutral"]
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "label": datasets.ClassLabel(names=self.labels),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_pairs":
+            features = schemas.pairs_features(self.labels)
+
+        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)
+
+        xnli_train = os.path.join(data_dir["train"], "XNLI-MT-1.0", "multinli")
+        train_data_path = os.path.join(xnli_train, "multinli.train.{}.tsv")
+
+        xnli_test = os.path.join(data_dir["test"], "XNLI-1.0")
+        val_data_path = os.path.join(xnli_test, "xnli.dev.tsv")
+        test_data_path = os.path.join(xnli_test, "xnli.test.tsv")
+
+        lang = self.config.name.split("_")[0].split(".")[-1]
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": train_data_path,
+                    "split": "train",
+                    "language": _LANGUAGE_MAPPER[lang],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": val_data_path,
+                    "split": "dev",
+                    "language": _LANGUAGE_MAPPER[lang],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": test_data_path,
+                    "split": "test",
+                    "language": _LANGUAGE_MAPPER[lang],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str, language: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        if self.config.schema == "source":
+            if split == "train":
+                file = open(filepath.format(language), encoding="utf-8")
+                reader = csv.DictReader(file, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for row_idx, row in enumerate(reader):
+                    key = str(row_idx)
+                    yield key, {
+                        "premise": row["premise"],
+                        "hypothesis": row["hypo"],
+                        "label": row["label"].replace("contradictory", "contradiction"),
+                    }
+            else:
+                with open(filepath, encoding="utf-8") as f:
+                    reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+                    for row in reader:
+                        if row["language"] == language:
+                            yield row["pairID"], {
+                                "premise": row["sentence1"],
+                                "hypothesis": row["sentence2"],
+                                "label": row["gold_label"],
+                            }
+        elif self.config.schema == "seacrowd_pairs":
+            if split == "train":
+                file = open(filepath.format(language), encoding="utf-8")
+                reader = csv.DictReader(file, delimiter="\t", quoting=csv.QUOTE_NONE)
+                for row_idx, row in enumerate(reader):
+                    yield str(row_idx), {
+                        "id": str(row_idx),
+                        "text_1": row["premise"],
+                        "text_2": row["hypo"],
+                        "label": row["label"].replace("contradictory", "contradiction"),
+                    }
+            else:
+                with open(filepath, encoding="utf-8") as f:
+                    reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+                    skip = set()
+                    for row in reader:
+                        if row["language"] == language:
+                            if row["pairID"] in skip:
+                                continue
+                            skip.add(row["pairID"])
+                            yield row["pairID"], {
+                                "id": row["pairID"],
+                                "text_1": row["sentence1"],
+                                "text_2": row["sentence2"],
+                                "label": row["gold_label"],
+                            }
+        else:
+            raise ValueError(f"Invalid config: {self.config.name}")