Update README.md

README.md

@@ -14,42 +14,44 @@ The performance of SNCSE on STS task with different encoders is:
 
 
 To reproduct above results, please [download](https://pan.baidu.com/s/1fkvNRxu-ytbVbtxQhNF4Gw?pwd=9y7y) the files and unzip it to replace the original file folder. Then [download](https://pan.baidu.com/s/10KpCU2v_Wk36OxEBSdykiQ?pwd=0wot) the models, modify the file path variables and run:
-
+```
 python bert_prediction.py
 
 python roberta_prediction.py
-
+```
 
 
 To train SNCSE, please [download](https://huggingface.co/datasets/princeton-nlp/datasets-for-simcse/blob/main/wiki1m_for_simcse.txt) the training file, and put it at /SNCSE/data. You can either run:
-
+```
 python generate_soft_negative_samples.py
-
-to generate soft negative samples, or use our files in /Files/soft_negative_samples.txt. Then you may modify and run train_SNCSE.sh.
+```
+to generate soft negative samples, or use our files in `/Files/soft_negative_samples.txt`. Then you may modify and run `train_SNCSE.sh`.
 
 
 
 To  evalute the checkpoints saved during traing on the development set of STSB task, please run:
-
+```
 python bert_evaluation.py
 
 python roberta_evaluation.py
 
-
+```
 
 Feel free to contact the authors at [email protected] for any questions.
 
 
 
 Please cite SNCSE as 
+```
 
 {
 
-Hao Wang, Yangguang Li, Zhen Huang, Yong Dou, Lingpeng Kong, Jing Shao.
+    Hao Wang, Yangguang Li, Zhen Huang, Yong Dou, Lingpeng Kong, Jing Shao.
 
-SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples.
+    SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples.
 
-CoRR, abs/2201.05979, 2022.
+    CoRR, abs/2201.05979, 2022.
 
 }
+```