Update README.md

README.md

@@ -35,8 +35,6 @@ We have released our supervised and unsupervised models on huggingface, which ac
 
 If you have any questions, feel free to raise an issue.
 
-[//]: <## Architecture>
-[//]: <We add multi-layer trainable dense vectors as soft prompts to the input sequence, which means the input embeddings as well as each layer's hidden embeddings of prompts are optimized (the orange blocks). Note that all parameters of the pre-trained model are frozen (the blue blocks), thus reducing the number of tunable parameters to around **0.1\%**. The [CLS] token embedding of the last layer is selected as the sentence representation. The contrastive framework is the same as SimCSE.>
 
 
 ## Setups
@@ -183,7 +181,7 @@ All our experiments are conducted on Nvidia 3090 GPUs.
 
 
 ## Usage
-We provide [tool.py](https://github.com/YJiangcm/PromCSE/blob/master/tool.py) which contains the following functions (A quick start [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lanXViJzbmGM1bwm8AflNUKmrvDidg_3?usp=sharing)):  
+We provide [tool.py](https://github.com/YJiangcm/PromCSE/blob/master/tool.py) which contains the following functions: 
 
 **(1) encode sentences into embedding vectors;  
 (2) compute cosine simiarities between sentences;