Upload clef24_dataset_en.py

clef24_dataset_en.py

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+"""Multilang Dataset loading script."""
+
+from datasets import DatasetDict, DatasetInfo, BuilderConfig, Version, GeneratorBasedBuilder
+from datasets import SplitGenerator, Split, Features, Value
+
+import os
+
+_DESCRIPTION = """
+This dataset includes multilingual data for language classification tasks across several languages.
+"""
+
+_CITATION = """\
+@InProceedings{huggingface:multilang_dataset,
+title = {Multilingual Text Dataset},
+authors = {Your Name},
+year = {2024}
+}
+"""
+
+_LICENSE = "Your dataset's license here."
+
+class MultilangDataset(GeneratorBasedBuilder):
+    """A multilingual text dataset."""
+
+    BUILDER_CONFIGS = [
+        BuilderConfig(name="multilang_dataset", version=Version("1.0.0"), description="Multilingual dataset for text classification."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "multilang_dataset"  # Default configuration name.
+
+    def _info(self):
+        return DatasetInfo(
+            description=_DESCRIPTION,
+            features=Features({
+                "Sentence_id": Value("string"),
+                "Text": Value("string"),
+                "class_label": Value("string"),
+            }),
+            supervised_keys=("Text", "class_label"),
+            homepage="https://www.example.com",
+            citation=_CITATION,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # Assumes your dataset is located in "path/to/dataset"
+        data_dir = os.path.abspath("data/")
+        splits = ["train", "dev", "dev-test"]
+        
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, f"{split}.tsv"),
+                    "split": split
+                },
+            )
+            for split in splits
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                if id_ == 0:  # Optionally skip header
+                    continue
+                cols = row.strip().split('\t')
+                yield f"{split}_{id_}", {
+                    "sentence_id": cols[0],
+                    "sentence": cols[1],
+                    "label": cols[2],
+                }