Update README.md

README.md

@@ -17,16 +17,18 @@ metrics:
 
 licence: apache-2.0
 ---
-# T5 for Chinese Question Answering
-Randeng-T5-784M-QA-Chinese
+# Randeng-T5-784M-QA-Chinese
+T5 for Chinese Question Answering
+- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
+- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
 
 
-## Brief Introduction
+## 简介 Brief Introduction
 This T5-Large model, is the first pretrained generative question answering model for Chinese in huggingface. It was pretrained on the Wudao 180G corpus, and finetuned on Chinese SQuAD and CMRC2018 dataset. It can produce a fluent and accurate answer given a passage and question.
 
-这是huggingface上首个中文的生成式问答模型。它基于T5-Large结构，使用悟道180G语料进行预训练，在翻译的中文SQuAD和CMRC2018两个阅读理解数据集上进行微调。输入一篇文章和一个问题，可以生成准确流畅的回答。
+这是huggingface上首个中文的生成式问答模型。它基于T5-Large结构，使用悟道180G语料在[封神框架](https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen)进行预训练，在翻译的中文SQuAD和CMRC2018两个阅读理解数据集上进行微调。输入一篇文章和一个问题，可以生成准确流畅的回答。
 
-## Performance
+## 模型表现 Performance 
   
  CMRC 2018 dev (Original span prediction task, we cast it as a generative QA task)
 
@@ -43,7 +45,7 @@ This T5-Large model, is the first pretrained generative question answering model
    我们的模型有着极高的生成质量和准确率，76%的回答包含了正确答案(Contain Answer Rate)，和当前最好模型MacBERT-Large想媲美，它70%的起始位置预测和答案精确匹配(EM)。我们的模型EM值较低，因为生成的大部分为完整的句子，而标准答案通常是句子片段。
    P.S. SOTA模型只需预测起始和结束位置，这种抽取式阅读理解任务比生成式的简单很多。
 
-## Cases
+## 样例 Cases
 
 Here are random picked samples:
 <img src="https://huggingface.co/IDEA-CCNL/Randeng-T5-784M-QA-Chinese/resolve/main/cases_t5_cmrc.png" div align=middle />
@@ -52,7 +54,7 @@ Here are random picked samples:
 
 If the picture fails to display, you can find the picture in Files and versions. 
 
-## Usage
+## 使用 Usage
 ```python
 import numpy as np
 from transformers import T5Tokenizer,MT5ForConditionalGeneration
@@ -77,7 +79,7 @@ tokenizer.batch_decode(pred_ids, skip_special_tokens=True, clean_up_tokenization
 ```
 
 
-# Citation
+# 引用 Citation
 
 You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):