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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_knowledge_graph_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 built this QA dataset based on the medical knowledge map, with a total of 798,444 pieces of data, in which the questions are constructed by means of templates, and the answers are the contents of the entries in the knowledge map.
+
+
+
+
+
+
+
+## Dataset Creation
+
+
+
+
+### Source Data
+
+https://cpubmed.openi.org.cn/graph/wiki  
+https://github.com/zhihao-chen/QASystemOnMedicalGraph  
+https://github.com/baiyang2464/chatbot-base-on-Knowledge-Graph  
+
+
+
+
+
+## Citation
+
+```
+@misc{li2023huatuo26m,
+      title={Huatuo-26M, a Large-scale Chinese Medical QA Dataset}, 
+      author={Jianquan Li and Xidong Wang and Xiangbo Wu and Zhiyi Zhang and Xiaolong Xu and Jie Fu and Prayag Tiwari and Xiang Wan and Benyou Wang},
+      year={2023},
+      eprint={2305.01526},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

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/Huatuo-26M",
+            citation='''
+            @misc{li2023huatuo26m,
+                  title={Huatuo-26M, a Large-scale Chinese Medical QA Dataset}, 
+                  author={Jianquan Li and Xidong Wang and Xiangbo Wu and Zhiyi Zhang and Xiaolong Xu and Jie Fu and Prayag Tiwari and Xiang Wan and Benyou Wang},
+                  year={2023},
+                  eprint={2305.01526},
+                  archivePrefix={arXiv},
+                  primaryClass={cs.CL}
+            }
+            
+            ''',
+        )
+
+    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()

test_datasets.jsonl

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train_datasets.jsonl

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validation_datasets.jsonl

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