Update README.md

README.md

@@ -55,7 +55,7 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 ## Training Data
  
   The model craters note in the [Github Repository](https://github.com/SJTU-LIT/SynCSE/blob/main/README.md)
-> We train 
+> We use 26.2k generated synthetic train SynCSE-partial-RoBERTa-base.
  
  
 ## Training Procedure
@@ -75,15 +75,6 @@ More information needed
 # Evaluation
  
  
-## Testing Data, Factors & Metrics
- 
-### Testing Data
- 
- The model craters note in the [associated paper](https://arxiv.org/pdf/2104.08821.pdf)
-> Our evaluation code for sentence embeddings is based on a modified version of [SentEval](https://github.com/facebookresearch/SentEval). It evaluates sentence embeddings on semantic textual similarity (STS) tasks and downstream transfer tasks. 
- 
-For STS tasks, our evaluation takes the "all" setting, and report Spearman's correlation. See [associated paper](https://arxiv.org/pdf/2104.08821.pdf) (Appendix B) for evaluation details.
-
 
  
 ### Factors
@@ -108,16 +99,6 @@ More information needed
 
 
  
-# Environmental Impact
- 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- 
-- **Hardware Type:** Nvidia 3090 GPUs with CUDA 11
-- **Hours used:** More information needed
-- **Cloud Provider:** More information needed
-- **Compute Region:** More information needed
-- **Carbon Emitted:** More information needed
- 
 # Technical Specifications [optional]
  
 ## Model Architecture and Objective
@@ -143,11 +124,11 @@ More information needed.
 **BibTeX:**
 
 ```bibtex
-@inproceedings{gao2021simcse,
-   title={{SimCSE}: Simple Contrastive Learning of Sentence Embeddings},
-   author={Gao, Tianyu and Yao, Xingcheng and Chen, Danqi},
-   booktitle={Empirical Methods in Natural Language Processing (EMNLP)},
-   year={2021}
+@article{zhang2023contrastive,
+  title={Contrastive Learning of Sentence Embeddings from Scratch},
+  author={Zhang, Junlei and Lan, Zhenzhong and He, Junxian},
+  journal={arXiv preprint arXiv:2305.15077},
+  year={2023}
 }
 ```
  
@@ -159,13 +140,11 @@ More information needed
 More information needed 
 
  
-# Model Card Authors [optional]
- 
-Princeton NLP group in collaboration with Ezi Ozoani and the Hugging Face team.
+
 
 # Model Card Contact
  
-If you have any questions related to the code or the paper, feel free to email Tianyu (`tianyug@cs.princeton.edu`) and Xingcheng (`yxc18@mails.tsinghua.edu.cn`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!
+If you have any questions related to the code or the paper, feel free to email Junlei (`zhangjunlei@westlake.edu.cn`). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!
 
 
  
@@ -179,9 +158,9 @@ Use the code below to get started with the model.
 ```python
 from transformers import AutoTokenizer, AutoModel
 
-tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/sup-simcse-bert-large-uncased")
+tokenizer = AutoTokenizer.from_pretrained("sjtu-lit/SynCSE-partial-RoBERTa-base")
 
-model = AutoModel.from_pretrained("princeton-nlp/sup-simcse-bert-large-uncased")
+model = AutoModel.from_pretrained("sjtu-lit/SynCSE-partial-RoBERTa-base")
  
  ```
 </details>