Update README.md

README.md

@@ -28,15 +28,17 @@ 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 | accuracy | Speed | #Training data
-|------------|-----------|----------|-------|-------|----|
-|   [zero-shot-classify-SSTuning-base](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-base)    |  [roberta-base](https://huggingface.co/roberta-base)      |  125M    |  Low    |  High    | 20.48M |  
-|   [zero-shot-classify-SSTuning-large](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-large)    |    [roberta-large](https://huggingface.co/roberta-large)      | 355M     |   Medium   | Medium | 5.12M |
-|   [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   |    High  | Low| 5.12M |
+| Model | Backbone | #params | lang | acc | Speed | #Train
+|------------|-----------|----------|-------|-------|----|-------------|
+|   [zero-shot-classify-SSTuning-base](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-base)    |  [roberta-base](https://huggingface.co/roberta-base)      |  125M    | En | Low    |  High    | 20.48M |  
+|   [zero-shot-classify-SSTuning-large](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-large)    |    [roberta-large](https://huggingface.co/roberta-large)      | 355M     | En |   Medium   | Medium | 5.12M |
+|   [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-base is trained with more data (20.48M) than the paper, as this will increase the accuracy.
+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