Update README.md

README.md

@@ -109,7 +109,7 @@ It was introduced in the paper [Zero-Shot Text Classification via Self-Supervise
 Chaoqun Liu, Wenxuan Zhang, Guizhen Chen, Xiaobao Wu, Anh Tuan Luu, Chip Hong Chang, Lidong Bing
 and first released in [this repository](https://github.com/DAMO-NLP-SG/SSTuning).
 
-The model backbone is RoBERTa-base.
+The model backbone is xlm-roberta-base.
 
 ## Model description
 
@@ -127,14 +127,15 @@ 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 | accuracy | Speed | #Training data
+| Model | Backbone | #params | language | 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 |
-|   [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    |  -    |  -    | 20.48M |  
+|   [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 |  
 
 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.
 
 ## Intended uses & limitations
 The model can be used for zero-shot text classification such as sentiment analysis and topic classification. No further finetuning is needed.