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README.md

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 ---
 license: apache-2.0
+task_categories:
+- text-generation
+language:
+- zh
+tags:
+- medical
+size_categories:
+- 100K<n<1M
 ---
+# Dataset Card for huatuo_consultation_qa
+
+
+
+## Dataset Description
+
+- **Homepage: https://www.huatuogpt.cn/** 
+- **Repository: https://github.com/FreedomIntelligence/HuatuoGPT** 
+- **Paper: https://arxiv.org/abs/2305.01526** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+
+
+### Dataset Summary
+
+We collected data from a website for medical consultation , consisting of many online consultation records by medical experts. Each record is a QA pair: a patient raises a question and a medical doctor answers the question. The basic information of doctors (including name, hospital organization, and department) was recorded.
+
+We directly crawl patient’s questions and doctor’s answers as QA pairs, getting 31,677,604 pairs. Subsequently, we removed the QA pairs containing special characters and removed the repeated pairs. Finally, we got 25,341,578 QA pairs.
+
+**Please note that for some reasons we cannot directly provide text data, so the answer part of our data set is url. If you want to use text data, you can refer to the other two parts of our open source data set, or use url for data collection.** 
+
+## Dataset Creation
+
+
+
+### Source Data
+
+
+....
+

my_dataset.py

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+
+
+
+from datasets import DatasetInfo, Features, Split, SplitGenerator, GeneratorBasedBuilder, Value, Sequence
+import json
+
+class MyDataset(GeneratorBasedBuilder):
+    def _info(self):
+        return DatasetInfo(
+            features=Features({
+                "questions": Sequence(Value("string")),
+                "answers": Sequence(Value("string"))
+            }),
+            supervised_keys=("questions", "answers"),
+            homepage="https://github.com/FreedomIntelligence/HuatuoGPT",
+            citation="...",
+        )
+
+    def _split_generators(self, dl_manager):
+        train_path = "train_datasets.jsonl"
+        validation_path = "validation_datasets.jsonl"
+        test_path = "test_datasets.jsonl"
+
+        return [
+            SplitGenerator(name=Split.TRAIN, gen_kwargs={"filepath": train_path}),
+            SplitGenerator(name=Split.VALIDATION, gen_kwargs={"filepath": validation_path}),
+            SplitGenerator(name=Split.TEST, gen_kwargs={"filepath": test_path}),
+        ]
+
+    def _generate_examples(self, filepath):
+        with open(filepath, encoding="utf-8") as f:
+            for id_, row in enumerate(f):
+                # Process your data here and create a dictionary with the features.
+                # For example, if your data is in JSON format:
+                data = json.loads(row)
+                yield id_, {
+                    "questions": data["questions"],
+                    "answers": data["answers"],
+                }
+
+if __name__ == '__main__':
+    from datasets import load_dataset
+
+    dataset = load_dataset("my_dataset.py")
+
+    print()