Update README.md

README.md

@@ -126,10 +126,34 @@ print(sentence_embeddings)
 ## Full Model Architecture
 ```
 CoSENT(
-  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel 
+  (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: ErnieModel 
   (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True})
 )
 ```
+
+
+## Intended uses
+
+Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures 
+the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
+
+By default, input text longer than 256 word pieces is truncated.
+
+
+## Training procedure
+
+### Pre-training 
+
+We use the pretrained [`nghuyong/ernie-3.0-base-zh`](https://huggingface.co/nghuyong/ernie-3.0-base-zh) model. 
+Please refer to the model card for more detailed information about the pre-training procedure.
+
+### Fine-tuning 
+
+We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each 
+possible sentence pairs from the batch.
+We then apply the rank loss by comparing with true pairs and false pairs.
+
+
 ## Citing & Authors
 This model was trained by [text2vec](https://github.com/shibing624/text2vec).