Upload COPA-ca.py

data/COPA-ca.py

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+# Loading script for the COPA-ca dataset.
+import json
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+
+_CITATION = ""
+
+_DESCRIPTION = """\
+               The COPA-ca dataset (Choice of plausible alternatives in Catalan) is a professional translation of the English COPA dataset into Catalan, commissioned by BSC LangTech Unit. The dataset consists of 1000 premises, each given a question and two choices with a label encoding which of the choices is more plausible given the annotator.
+
+The dataset is split into 400 training samples, 100 validation samples, and 500 test samples. It includes the following features: 'premise', 'choice1', 'choice2', 'label', 'question', 'changed' (boolean).
+
+This work is licensed under a Attribution-ShareAlike 4.0 International License.
+               """
+
+_HOMEPAGE = "https://zenodo.org/record/8124398"
+
+
+_URL = "https://huggingface.co/datasets/projecte-aina/copa-ca/resolve/main/"
+_TRAIN_FILE = "copa-ca.train.jsonl"
+_DEV_FILE = "copa-ca.val.jsonl"
+_TEST_FILE = "copa-ca.test.jsonl"
+
+
+class copaCaConfig(datasets.BuilderConfig):
+    """ Builder config for the COPA-ca dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for COPA-ca.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(copaCaConfig, self).__init__(**kwargs)
+        
+class copaCa(datasets.GeneratorBasedBuilder):
+    """ COPA-ca Dataset """
+
+    BUILDER_CONFIGS = [
+        copaCaConfig(
+            name="copa-ca",
+            version=datasets.Version("1.0.1"),
+            description="COPA-ca dataset",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "premise": datasets.Value("string"),
+                    "choice1": datasets.Value("string"),
+                    "choice2": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    'label': datasets.features.ClassLabel(names=['1', '2']),
+                    "idx": datasets.Value("int64"),
+                    "changed": datasets.Value("bool"),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAIN_FILE}",
+            "dev": f"{_URL}{_DEV_FILE}",
+            "test": f"{_URL}{_TEST_FILE}",
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding='utf-8') as f:
+            for i, line in enumerate(f):
+                data = json.loads(line)
+                yield i, {
+                    'premise': data['premise'],
+                    'choice1': data['choice1'],
+                    'choice2': data['choice2'],
+                    'question': data['question'],
+                    'label': str(data['label']),
+                    'idx': data['idx'],
+                    'changed': data['changed']
+                }