Update README.md

README.md

@@ -2,7 +2,7 @@
 language: Chinese
 datasets: CLUECorpusSmall
 widget: 
-- text: "中国的首都是extra0京"
+- text: "作为电子为主的电商平台，京东商城绝对是extra0者。如今的刘强extra1已经是身价过extra2的老板。"
 
 
 
@@ -12,11 +12,14 @@ widget:
 
 ## Model description
 
-The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. Based on this, we released this Chinese t5-small model. You can download the model via HuggingFace from the link [t5-small-chinese-cluecorpussmall](https://huggingface.co/uer/t5-small-chinese-cluecorpussmall).
+The Text-to-Text Transfer Transformer (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. Following their paper, we released a series of Chinese T5 models.
 
-## How to use
+|          |           Link           |
+| -------- | :-----------------------: |
+| **Small**  | [**2/128 (Tiny)**][2_128] |
+| **Base**  | [**4/256 (Mini)**][4_256] |
 
-We provide two vocabs ( vocab.txt and google_zh_with_sentinel_vocab.txt ) for this model and use the google_zh_with_sentinel_vocab.txt to train this model. In order to use Hosted inference API, we replaced characters like [extra_id_0] in the google_zh_with_sentinel_vocab.txt with characters extra0 to prevent characters from being split .
+## How to use
 
 You can use the model directly with a pipeline for text2text generation:
 
@@ -29,15 +32,13 @@ You can use the model directly with a pipeline for text2text generation:
     [{'generated_text': 'extra0 北 extra1 extra2 extra3 extra4 extra5'}]
 ```
 
-
-
 ## Training data
 
 [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020/) is used as training data. 
 
 ## Training procedure
 
-The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud TI-ONE](https://cloud.tencent.com/product/tione/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. 
+The model is pre-trained by [UER-py](https://github.com/dbiir/UER-py/) on [Tencent Cloud](https://cloud.tencent.com/). We pre-train 1,000,000 steps with a sequence length of 128 and then pre-train 250,000 additional steps with a sequence length of 512. 
 
 Stage1: