Update README.md

README.md

@@ -6,20 +6,7 @@ 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-1.0.1-red">
-    </a>
-</p>
+
 
 <h4 align="center">
     <p>
@@ -27,16 +14,16 @@ pipeline_tag: sentence-similarity
         <a href=#usage>Usage</a>  |
         <a href="#evaluation">Evaluation</a> |
         <a href="#train">Train</a> |
-        <a href="#contact">Contact</a> |
         <a href="#license">License</a> 
     <p>
 </h4>
 
+For more details please refer to our GitHub repo: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
 
 [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
 
 FlagEmbedding can map any text to a low-dimensional dense vector which can be used for tasks like retrieval, classification,  clustering, or semantic search.
-And it also can be used in vector database for LLMs.
+And it also can be used in vector databases for LLMs.
 
 ************* 🌟**Updates**🌟 *************
 - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
@@ -103,7 +90,7 @@ embeddings = model.encode(sentences, normalize_embeddings=True)
 print(embeddings)
 ```
 For retrieval task, 
-each query should start with a instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). 
+each query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). 
 ```python
 from sentence_transformers import SentenceTransformer
 queries = ["手机开不了机怎么办？"]
@@ -132,7 +119,7 @@ model = AutoModel.from_pretrained('BAAI/bge-large-zh')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
-# for retrieval task, add a instruction to query
+# for retrieval task, add an instruction to query
 # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
 
 # Compute token embeddings
@@ -176,7 +163,7 @@ More details and evaluation tools see our [scripts](https://github.com/FlagOpen/
 
 
 - **C-MTEB**:  
-We create a benchmark C-MTEB for chinese text embedding which consists of  31 datasets from 6 tasks. 
+We create a benchmark C-MTEB for Chinese text embedding which consists of  31 datasets from 6 tasks. 
 Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
  
 | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
@@ -204,7 +191,7 @@ and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagE
 We pre-train the model following the method [retromae](https://github.com/staoxiao/RetroMAE), 
 which shows promising improvement in retrieval task ([paper](https://aclanthology.org/2022.emnlp-main.35.pdf)). 
 The pre-training was conducted on 24 A100(40G) GPUs with a batch size of 720. 
-In retromae, the mask ratio of encoder and decoder are 0.3, 0.5 respectively.
+In retromae, the mask ratio of encoder and decoder are 0.3, and 0.5 respectively.
 We used the AdamW optimizer and the learning rate is 2e-5.
 
 **Pre-training data**:
@@ -221,7 +208,7 @@ We used the AdamW optimizer and the learning rate is 2e-5.
 We fine-tune the model using a contrastive objective. 
 The format of input data is a triple`(query, positive, negative)`. 
 Besides the negative in the triple, we also adopt in-batch negatives strategy. 
-We employ the cross-device negatives sharing method to sharing negatives among different GPUs, 
+We employ the cross-device negatives sharing method to share negatives among different GPUs, 
 which can dramatically **increase the number of negatives**.
 
 We trained our model on 48 A100(40G) GPUs with a large batch size of 32,768 (so there are **65,535** negatives for each query in a batch). 
@@ -246,22 +233,6 @@ 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
-- [ ] Training Datasets 
-- [ ] Multilingual model
-- [ ] ...
-
-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.