update readme

README.md

@@ -2604,20 +2604,7 @@ pipeline_tag: sentence-similarity
 
 
 <h1 align="center">FlagEmbedding</h1>
-<p align="center">
-    <a href="https://www.python.org/">
-            <img alt="Build" src="https://img.shields.io/badge/Contribution-Welcome-blue">
-    </a>
-    <a href="https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE">
-        <img alt="License" src="https://img.shields.io/badge/LICENSE-MIT-green">
-    </a>
-    <a href="https://huggingface.co/C-MTEB">
-        <img alt="Build" src="https://img.shields.io/badge/C_MTEB-🤗-yellow">
-    </a>
-    <a href="https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding">
-        <img alt="Build" src="https://img.shields.io/badge/FlagEmbedding-1.0-red">
-    </a>
-</p>
+
 
 <h4 align="center">
     <p>
@@ -2630,6 +2617,7 @@ pipeline_tag: sentence-similarity
     <p>
 </h4>
 
+More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
 
 [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
 
@@ -2637,7 +2625,7 @@ FlagEmbedding can map any text to a low-dimensional dense vector which can be us
 And it also can be used in vector database for LLMs.
 
 ************* 🌟**Updates**🌟 *************
-- 08/09/2023: BGE Models are integrated into Langchain, you can use it like [this](#using-langchain); C-MTEB leaderboard is [avaliable](https://huggingface.co/spaces/mteb/leaderboard).  
+- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [avaliable](https://huggingface.co/spaces/mteb/leaderboard).  
 - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
 - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: 
 - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.   
@@ -2867,23 +2855,10 @@ You can easily finetune your model with it.
 
 **The data collection is to be released in the future.**
 
-
-## Schedule
-- [x] Chinese Massive Text Embedding Benchmark
-- [x] release baai-general-embedding models
-- [x] release codes for training
-- [ ] Multilingual model
-- [ ] Training Datasets 
-- [ ] ...
-
 We will continually update the embedding models and training codes, 
 hoping to promote the development of the embedding model community.
 
 
-## Contact
-If you have any question or suggestion related to this project, feel free to open an issue or pull a request.
-You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). 
-
 
 ## License
 FlagEmbedding is licensed under [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.