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README.md

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+license: mit
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 # PromCSE: Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning
 [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1lanXViJzbmGM1bwm8AflNUKmrvDidg_3?usp=sharing)
 
-Our code is modified based on [SimCSE](https://github.com/princeton-nlp/SimCSE) and [P-tuning v2](https://github.com/THUDM/P-tuning-v2/). Here we would like to sincerely thank them for their excellent works. 
-
-**************************** **Updates** ****************************
-* 2023/4/5: We released our sentence embedding [python package](#getting-started).
-* 2022/3/3: We released a simple [colab notebook](https://colab.research.google.com/drive/1lanXViJzbmGM1bwm8AflNUKmrvDidg_3?usp=sharing) for a quick start!
-* 2022/1/8: We released our model checkpoints on [huggingface](https://huggingface.co/YuxinJiang).
-* 2022/10/9: We released the second verson of [our paper](https://arxiv.org/pdf/2203.06875v2.pdf). Check it out!
-* 2022/10/6: Our paper has been accepted to [**EMNLP 2022**](https://2022.emnlp.org/). 
-* 2022/3/14: We released the first verson of [our paper](https://arxiv.org/pdf/2203.06875v1.pdf). Check it out!
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-
-
-
-## Quick Links
-  - [Overview](#overview)
-  - [Model List](#model-list)
-  - [Usage](#usage)
-  - [Train PromCSE](#train-promcse)
-    - [Setups](#setups)
-    - [Evaluation](#evaluation)
-    - [Training](#training)
-  - [Citation](#citation)
-
-
-
-## Overview
-<img src="https://github.com/YJiangcm/PromCSE/blob/master/figure/overview.jpg" width="700" height="320">
-
+arXiv link: https://arxiv.org/abs/2203.06875v2  
+Published in [**EMNLP 2022**](https://2022.emnlp.org/)
 
+Our code is modified based on [SimCSE](https://github.com/princeton-nlp/SimCSE) and [P-tuning v2](https://github.com/THUDM/P-tuning-v2/). Here we would like to sincerely thank them for their excellent works. 
 
 ## Model List