Update README.md

README.md

@@ -125,7 +125,7 @@ The learning objective for FSP is to predict the index of the correct label.
 A cross-entropy loss is used for tuning the model.
 
 ## Model variations
-There are three versions of models released. The details are: 
+There are four versions of models released. The details are: 
 
 | Model | Backbone | #params | lang | acc | Speed | #Train
 |------------|-----------|----------|-------|-------|----|-------------|
@@ -134,7 +134,7 @@ There are three versions of models released. The details are:
 |   [zero-shot-classify-SSTuning-ALBERT](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-ALBERT)   |  [albert-xxlarge-v2](https://huggingface.co/albert-xxlarge-v2)      |  235M   | En |  High  | Low| 5.12M |
 |   [zero-shot-classify-SSTuning-XLM-R](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-XLM-R)    |  [xlm-roberta-base](https://huggingface.co/xlm-roberta-base)      |  278M    | Multi | -   |  -    | 20.48M |  
 
-Please note that zero-shot-classify-SSTuning-XLM-R is trained with 20.48M English samples only. However, it can also be used in other languages as long as XLM-R supports.
+Please note that zero-shot-classify-SSTuning-XLM-R is trained with 20.48M English samples only. However, it can also be used in other languages as long as xlm-roberta supports.
 Please check [this repository](https://github.com/DAMO-NLP-SG/SSTuning) for the performance of each model.
 
 ## Intended uses & limitations