Update README.md

README.md

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 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: 
+
+| Model | Backbone | #params | accuracy | Speed |
+|------------|-----------|----------|-------|-------|
+|   [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    |
+|   [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     |
+|   [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     |
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 ## Intended uses & limitations
 The model can be used for zero-shot text classification such sentiment analysis and topic classificaion. No further finetuning is needed.