Update README.md

README.md

@@ -1,84 +1,84 @@
----
-language: 
-  - en
-
-inference: 
-  parameters:
-    temperature: 0.7
-    top_p: 0.6
-    max_new_tokens: 64
-    num_return_sequences: 3
-    do_sample: true
-
-license: apache-2.0
-tags:
-- QA
-- medical 
-- gpt2
-
-widget:
-- text: "Question:What should gout patients pay attention to in diet? Answer:"
-  example_title: "test Question1"
-- text: "Question:How should covid-19 be prevented? Answer:"
-  example_title: "test Question2"
----
-# YuyuanQA-GPT2-3.5B model (Medical)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
-**YuyuanQA-GPT2-3.5B** is fine-tuned with 10000 medical QA pairs based on **Yuyuan-3.5B** model.
-
-**Question answering（QA）** is an important subject related to natural language processing and information retrieval. There are many application scenarios in the actual industry. **Traditional methods are often complex**, and their core algorithms involve **machine learning**, **deep learning** and **knowledge graph** related knowledge.
-
-We hope to explore a **simpler** and more **effective** way to use the powerful memory and understanding ability of the large model to directly realize question and answering. Yuyuanqa-GPT2-3.5b model is an attempt and **performs well under subjective test**. At the same time, we also tested 100 QA pairs with ***BLUE***:
-
-| gram | 1-gram | 2-gram | 3-gram | 4-gram |
-| ----------- | ----------- |------|------|------|
-| **blue_score**   | 0.357727 | 0.2713 | 0.22304 | 0.19099 |
-
-## Usage
-
-### load model
-```python 
-from transformers import GPT2Tokenizer,GPT2LMHeadModel
-
-hf_model_path = 'model_path or model name'
-
-tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path)
-model = GPT2LMHeadModel.from_pretrained(hf_model_path)
-```
-### generation
-```python
-fquestion = "What should gout patients pay attention to in diet?"
-inputs = tokenizer(f'Question:{question} answer:',return_tensors='pt')
-
-generation_output = model.generate(**inputs,
-                                return_dict_in_generate=True,
-                                output_scores=True,
-                                max_length=150,
-                                # max_new_tokens=80,
-                                do_sample=True,
-                                top_p = 0.6,
-                                eos_token_id=50256,
-                                pad_token_id=0,
-                                num_return_sequences = 5)
-
-for idx,sentence in enumerate(generation_output.sequences):
-    print('next sentence %d:\n'%idx,
-          tokenizer.decode(sentence).split('<|endoftext|>')[0])
-    print('*'*40)
-
-```
-## example
-
-We made a demo of medical Q & A with YuyuanQA-GPT2-3.5B model. In the future, we will make this product into a wechat app to meet you. Please look forward to it.
-
-![avatar](https://huggingface.co/IDEA-CCNL/YuyuanQA-GPT2-3.5B/resolve/main/QA-DEMO.png)
-
-## Citation
-If you find the resource is useful, please cite the following website in your paper.
-```
-@misc{Fengshenbang-LM,
-  title={Fengshenbang-LM},
-  author={IDEA-CCNL},
-  year={2022},
-  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
-}
-```
+---
+language: 
+  - en
+
+inference: 
+  parameters:
+    temperature: 0.7
+    top_p: 0.6
+    max_new_tokens: 64
+    num_return_sequences: 3
+    do_sample: true
+
+license: apache-2.0
+tags:
+- QA
+- medical 
+- gpt2
+
+widget:
+- text: "Question:What should gout patients pay attention to in diet? Answer:"
+  example_title: "test Question1"
+- text: "Question:How should covid-19 be prevented? Answer:"
+  example_title: "test Question2"
+---
+# YuyuanQA-GPT2-3.5B model (Medical)，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
+**YuyuanQA-GPT2-3.5B** is fine-tuned with 10000 medical QA pairs based on **Yuyuan-3.5B** model.
+
+**Question answering（QA）** is an important subject related to natural language processing and information retrieval. There are many application scenarios in the actual industry. **Traditional methods are often complex**, and their core algorithms involve **machine learning**, **deep learning** and **knowledge graph** related knowledge.
+
+We hope to explore a **simpler** and more **effective** way to use the powerful memory and understanding ability of the large model to directly realize question and answering. Yuyuanqa-GPT2-3.5b model is an attempt and **performs well under subjective test**. At the same time, we also tested 100 QA pairs with ***BLEU***:
+
+| gram | 1-gram | 2-gram | 3-gram | 4-gram |
+| ----------- | ----------- |------|------|------|
+| **blue_score**   | 0.357727 | 0.2713 | 0.22304 | 0.19099 |
+
+## Usage
+
+### load model
+```python 
+from transformers import GPT2Tokenizer,GPT2LMHeadModel
+
+hf_model_path = 'model_path or model name'
+
+tokenizer = GPT2Tokenizer.from_pretrained(hf_model_path)
+model = GPT2LMHeadModel.from_pretrained(hf_model_path)
+```
+### generation
+```python
+fquestion = "What should gout patients pay attention to in diet?"
+inputs = tokenizer(f'Question:{question} answer:',return_tensors='pt')
+
+generation_output = model.generate(**inputs,
+                                return_dict_in_generate=True,
+                                output_scores=True,
+                                max_length=150,
+                                # max_new_tokens=80,
+                                do_sample=True,
+                                top_p = 0.6,
+                                eos_token_id=50256,
+                                pad_token_id=0,
+                                num_return_sequences = 5)
+
+for idx,sentence in enumerate(generation_output.sequences):
+    print('next sentence %d:\n'%idx,
+          tokenizer.decode(sentence).split('<|endoftext|>')[0])
+    print('*'*40)
+
+```
+## example
+
+We made a demo of medical Q & A with YuyuanQA-GPT2-3.5B model. In the future, we will make this product into a wechat app to meet you. Please look forward to it.
+
+![avatar](https://huggingface.co/IDEA-CCNL/YuyuanQA-GPT2-3.5B/resolve/main/QA-DEMO.png)
+
+## Citation
+If you find the resource is useful, please cite the following website in your paper.
+```
+@misc{Fengshenbang-LM,
+  title={Fengshenbang-LM},
+  author={IDEA-CCNL},
+  year={2022},
+  howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
+}
+```