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.gitattributes

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-*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

@@ -1,32 +0,0 @@
----
-language:
-- ru
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-task_categories:
-- text-classification
-task_ids:
-- sentiment-classification
----
-### Dataset Summary
-The dataset contains user reviews about medical facilities.
-
-In total it contains 70,597 reviews. The detailed distribution on sentiment scale is:
-- 41,419 positive reviews;
-- 29,178 negative reviews.
-### Data Fields
-Each sample contains the following fields:
-- **review_id**;
-- **category** category of medical facility (one of 48);
-- **title**: review title;
-- **content**: review text;
-- **sentiment**: sentiment (<em>positive</em> or <em>negative</em>);
-- **source_url**.
-### Python
-```python3
-import pandas as pd
-df = pd.read_json('healthcare_facilities_reviews.jsonl', lines=True)
-df.sample(5)
-```

healthcare_facilities_reviews.py

@@ -1,54 +0,0 @@
-import datasets
-import json
-
-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):
-        """Yields examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for uid, row in enumerate(f):
-                data = json.loads(row)
-                data["Idx"] = uid
-                yield uid, data

healthcare_facilities_reviews.jsonl → simple/healthcare_facilities_reviews-train.parquet

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-size 95300708
+oid sha256:fb6784e528e5c22e3bc8888ed8d03e91a68fc0fb03bb828d206ff686b7a8aa26
+size 44314725

simple/healthcare_facilities_reviews-validation.parquet

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