Create sentiment_digikala_snappfood.py

sentiment_digikala_snappfood.py

@@ -0,0 +1,70 @@
+import csv
+import datasets
+from datasets.tasks import TextClassification
+
+
+_DESCRIPTION = """\
+Sentiment analysis dataset extracted and labeled from Digikala and Snapp Food comments
+"""
+
+_DOWNLOAD_URLS = [
+    "https://huggingface.co/datasets/hezar-ai/sentiment_digikala_snappfood/blob/main/sentiment_digikala_snappfood_train.csv",
+    "https://huggingface.co/datasets/hezar-ai/sentiment_digikala_snappfood/blob/main/sentiment_digikala_snappfood_test.csv"
+    ]
+
+
+class SentimentDigikalaSnappfoodConfig(datasets.BuilderConfig):
+    """BuilderConfig for SentimentMixedV1"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for SentimentMixedV1.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SentimentDigikalaSnappfoodConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+
+
+class SentimentDigikalaSnappfood(datasets.GeneratorBasedBuilder):
+    """Sentiment analysis on Digikala/SnappFood comments"""
+
+    BUILDER_CONFIGS = [
+        SentimentDigikalaSnappfoodConfig(
+            name="plain_text",
+            description="Plain text",
+        )
+    ]
+
+    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):
+        archive = dl_manager.download(_DOWNLOAD_URLS)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Generate examples."""
+        # For labeled examples, extract the label from the path.
+        label_mapping = {"negative": 0, "positive": 1, "neutral": 2}
+        with open(filepath, encoding="utf-8") as csv_file:
+            csv_reader = csv.reader(
+                csv_file, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True
+            )
+            for id_, row in enumerate(csv_reader):
+                text, label = row
+                label = label_mapping[label]
+                yield id_, {"text": text, "label": label}