Update README.md

README.md

@@ -127,12 +127,12 @@ A cross-entropy loss is used for tuning the model.
 ## Model variations
 There are three versions of models released. The details are: 
 
-| Model | Backbone | #params | language | accuracy | Speed | #Training data
-|------------|-----------|----------|-------|-------|----|----|
+| Model | Backbone | #params | lang | 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    | 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    | En | -   |  -    | 20.48M |  
+|   [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    | Multiple | -   |  -    | 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 check [this repository](https://github.com/DAMO-NLP-SG/SSTuning) for the performance of each model.