Upload healthcare_facilities_reviews.py

healthcare_facilities_reviews.py

@@ -0,0 +1,56 @@
+import datasets
+import pandas as pd
+
+class HfreviewsConfig(datasets.BuilderConfig):
+    def __init__(self, features, **kwargs):
+        super(HfreviewsConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.features = features
+
+class HealthcareFacilitiesReviews(datasets.GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        HfreviewsConfig(
+            name="simple",
+            description="Simple config",
+            features=["content", "title", "sentiment", "category", "review_id", "source_url"],
+        )
+    ]
+    
+    def _info(self):
+        return datasets.DatasetInfo(
+            description='Healthcare facilities reviews dataset.',
+            features=datasets.Features(
+                {
+                    "content": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "sentiment": datasets.Value("string"),
+                    "category": datasets.Value("string"),
+                    "review_id": datasets.Value("string"),
+                    "source_url": datasets.Value("string"),
+                    "Idx": datasets.Value("int32"),
+                }
+            ),
+            supervised_keys=None,
+            homepage='',
+        )
+    
+    def _split_generators(self, dl_manager: datasets.DownloadManager):
+        urls_to_download = {
+            "train": "healthcare_facilities_reviews.jsonl",
+            "dev": "healthcare_facilities_reviews.jsonl",
+        }
+        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"]}),
+        ]
+            
+    def _generate_examples(self, filepath):
+        df = pd.read_json(filepath, lines=True)
+        rows = df.to_dict(orient="records")
+        
+        for n, row in enumerate(rows):
+            example = row
+            example["Idx"] = n
+
+            yield example["Idx"], example