Update parquet files

README.md

@@ -1,3 +0,0 @@
----
-license: mit
----

default/sentiment_digikala_snappfood-test.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e3afae7dbe40bd49baf4966ca08e596af34a8a5340e557e9049f3243fb72aac
+size 266140

default/sentiment_digikala_snappfood-train.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b12d86af0b6dca16a409fdb0b18cacdf696ac7d081c1419e66cec0e0f6fae30
+size 3437378

sentiment_digikala_snappfood.py

@@ -1,49 +0,0 @@
-import csv
-import datasets
-from datasets.tasks import TextClassification
-
-
-_DESCRIPTION = """\
-Sentiment analysis dataset extracted and labeled from Digikala and Snapp Food comments
-"""
-
-_DOWNLOAD_URLS = {
-
-    "train": "https://huggingface.co/datasets/hezar-ai/sentiment_digikala_snappfood/raw/main/sentiment_digikala_snappfood_train.csv",
-    "test": "https://huggingface.co/datasets/hezar-ai/sentiment_digikala_snappfood/raw/main/sentiment_digikala_snappfood_test.csv"
-}
-
-
-class SentimentDigikalaSnappfood(datasets.GeneratorBasedBuilder):
-    """Sentiment analysis on Digikala/SnappFood comments"""
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {"text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["negative", "positive", "neutral"])}
-            ),
-            supervised_keys=None,
-            homepage="https://huggingface.co/datasets/hezar-ai/sentiment_digikala_snappfood",
-            task_templates=[TextClassification(text_column="text", label_column="label")],
-        )
-
-    def _split_generators(self, dl_manager):
-        train_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["train"])
-        test_path = dl_manager.download_and_extract(_DOWNLOAD_URLS["test"])
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
-            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
-        ]
-
-    def _generate_examples(self, filepath):
-        """Generate examples."""
-        label_mapping = {"negative": 0, "positive": 1, "neutral": 2}
-        with open(filepath, encoding="utf-8") as csv_file:
-            csv_reader = csv.reader(
-                csv_file, quotechar='"', skipinitialspace=True
-            )
-            for id_, row in enumerate(csv_reader):
-                text, label = row
-                label = label_mapping[label]
-                yield id_, {"text": text, "label": label}