Update README.md

README.md

@@ -10,4 +10,81 @@ and first released in [this repository](https://github.com/DAMO-NLP-SG/SSTuning)
 
 The model backbone is albert-xxlarge-v2.
 
-Please refer to [zero-shot-classify-SSTuning-base](https://huggingface.co/DAMO-NLP-SG/zero-shot-classify-SSTuning-base) for more information.
+## Model description
+The model is tuned with unlabeled data using a learning objective called first sentence prediction (FSP). 
+The FSP task is designed by considering both the nature of the unlabeled corpus and the input/output format of classification tasks. 
+The training and validation sets are constructed from the unlabeled corpus using FSP. 
+
+During tuning, BERT-like pre-trained masked language 
+models such as RoBERTa and ALBERT are employed as the backbone, and an output layer for classification is added. 
+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 | #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 |
+
+Please note that zero-shot-classify-SSTuning-base is trained with more data (20.48M) than the paper, as this will increase the accuracy.
+
+
+## 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.
+
+The number of labels should be 2 ~ 20. 
+
+### How to use
+You can try the model with the Colab [Notebook](https://colab.research.google.com/drive/17bqc8cXFF-wDmZ0o8j7sbrQB9Cq7Gowr?usp=sharing).
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch, string, random
+
+tokenizer = AutoTokenizer.from_pretrained("DAMO-NLP-SG/zero-shot-classify-SSTuning-base")
+model = AutoModelForSequenceClassification.from_pretrained("DAMO-NLP-SG/zero-shot-classify-SSTuning-base")
+
+text = "I love this place! The food is always so fresh and delicious."
+list_label = ["negative", "positive"]
+
+list_ABC = [x for x in string.ascii_uppercase]
+def add_prefix(text, list_label, shuffle = False):
+    list_label = [x+'.' if x[-1] != '.' else x for x in list_label]
+    list_label_new = list_label + [tokenizer.pad_token]* (20 - len(list_label))
+    if shuffle: 
+        random.shuffle(list_label_new)
+    s_option = ' '.join(['('+list_ABC[i]+') '+list_label_new[i] for i in range(len(list_label_new))])
+    return f'{s_option} {tokenizer.sep_token} {text}', list_label_new
+
+text_new, list_label_new = add_prefix(text,list_label,shuffle=False)
+
+encoding = tokenizer([text_new],truncation=True, padding='max_length',max_length=512, return_tensors='pt')
+with torch.no_grad():
+    logits = model(**encoding).logits
+    probs = torch.nn.functional.softmax(logits, dim = -1).tolist()
+    predictions = torch.argmax(logits, dim=-1)
+
+print(probs)
+print(predictions)
+```
+
+
+### BibTeX entry and citation info
+```bibtxt
+@inproceedings{acl23/SSTuning,
+  author    = {Chaoqun Liu and
+               Wenxuan Zhang and
+               Guizhen Chen and
+               Xiaobao Wu and
+               Anh Tuan Luu and
+               Chip Hong Chang and 
+               Lidong Bing},
+  title     = {Zero-Shot Text Classification via Self-Supervised Tuning},
+  booktitle = {Findings of the 2023 ACL},
+  year      = {2023},
+  url       = {},
+}
+```