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OLD/Parafraseja.py

@@ -0,0 +1,100 @@
+# Loading script for the ReviewsFinder dataset.
+
+
+import json
+import csv
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """ """
+
+
+_DESCRIPTION = """ Parafraseja is a dataset of 16,584 pairs of sentences with a label that indicates if they are paraphrases or not. The original sentences were collected from TE-ca and STS-ca. For each sentence, an annotator wrote a sentence that was a paraphrase and another that was not. The guidelines of this annotation are available.  """
+
+
+_HOMEPAGE = """ https://huggingface.co/datasets/projecte-aina/Parafraseja/ """
+
+
+
+_URL = "https://huggingface.co/datasets/projecte-aina/Parafraseja/resolve/main/"
+_TRAINING_FILE = "train.jsonl"
+_DEV_FILE = "dev.jsonl"
+_TEST_FILE = "test.jsonl"
+
+
+class ParafrasejaConfig(datasets.BuilderConfig):
+    """ Builder config for the Parafraseja dataset """
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for parafrasis.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(ParafrasejaConfig, self).__init__(**kwargs)
+
+
+class Parafraseja(datasets.GeneratorBasedBuilder):
+    """ Parafrasis Dataset """
+
+
+    BUILDER_CONFIGS = [
+        ParafrasejaConfig(
+            name="Parafraseja",
+            version=datasets.Version("1.0.0"),
+            description="Parafraseja dataset",
+        ),
+    ]
+
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "sentence1": datasets.Value("string"),
+                    "sentence2": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel
+                    (names=
+                        [
+                            "No Parafrasis",
+                            "Parafrasis",
+                        ]
+                    ),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+                
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = {
+            "train": f"{_URL}{_TRAINING_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):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        with open(filepath, encoding="utf-8") as f:
+            data = [json.loads(line) for line in f]
+            for id_, article in enumerate(data):
+                    yield id_, {
+                        "sentence1": article['original'],
+                        "sentence2": article['new'],
+                        "label": article['label'],
+                    }