Update README.md

README.md

@@ -6,7 +6,20 @@ 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>
@@ -19,17 +32,16 @@ pipeline_tag: sentence-similarity
     <p>
 </h4>
 
-More details please refer to our github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
 
-[English](README.md) | [中文](README_zh.md)
+[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.
 
 ************* 🌟**Updates**🌟 *************
 - 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!**  
-- 08/01/2023: We release the Chinese Massive Text Embedding Benchmark (**C-MTEB**), consisting of 31 test dataset.   
+- 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.   
 
 
 ## Model List
@@ -38,12 +50,12 @@ And it also can be used in vector database for LLMs.
 
 |              Model              | Language | Description | query instruction for retrieval |
 |:-------------------------------|:--------:| :--------:| :--------:|
-|  [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) |   English |  **rank 1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
-|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English |  **rank 2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) |   English |  :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
+|  [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) |   English |  rank **2nd** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: `  |
 |  [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) |   English | a small-scale model but with competitive performance  | `Represent this sentence for searching relevant passages: `  |
-|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | **rank 1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/benchmark) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
-|  [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) |   Chinese | This model is trained without instruction, and **rank 2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/benchmark) benchmark |   |
-|  [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) |   Chinese |  a base-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) |   Chinese | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：`  |
+|  [BAAI/bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) |   Chinese | This model is trained without instruction, and rank **2nd** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark |   |
+|  [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) |   Chinese |  a base-scale model but has similar ability with `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：`  |
 |  [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) |   Chinese | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：`  |
 
 
@@ -52,10 +64,12 @@ And it also can be used in vector database for LLMs.
 
 * **Using FlagEmbedding**
 ```
-pip install flag_embedding
+pip install FlagEmbedding
 ```
+See [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
+
 ```python
-from flag_embedding import FlagModel
+from FlagEmbedding import FlagModel
 sentences = ["样例数据-1", "样例数据-2"]
 model = FlagModel('BAAI/bge-large-zh', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：")
 embeddings = model.encode(sentences)
@@ -134,7 +148,7 @@ print("Sentence embeddings:", sentence_embeddings)
 
 ## Evaluation  
 `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
-More details and evaluation scripts see [benchemark](benchmark/README.md). 
+More details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). 
 
 - **MTEB**:   
 
@@ -162,8 +176,8 @@ More details and evaluation scripts see [benchemark](benchmark/README.md).
 
 
 - **C-MTEB**:  
-We create a benchmark C-MTEB for Chinese text embedding which consists of  31 datasets from 6 tasks. 
-Please refer to [benchemark](benchmark/README.md) for a detailed introduction.
+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 |
 |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
@@ -180,18 +194,17 @@ Please refer to [benchemark](benchmark/README.md) for a detailed introduction.
 
 
 
-
 ## Train
 This section will introduce the way we used to train the general embedding. 
-The training scripts are in [flag_embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/flag_embedding/baai_general_embedding/), 
-and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain/) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).
+The training scripts are in [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md), 
+and we provide some examples to do [pre-train](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/pretrain/README.md) and [fine-tune](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/README.md).
 
 
 **1. RetroMAE Pre-train**  
 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 the encoder and decoder are 0.3, and 0.5 respectively.
+In retromae, the mask ratio of encoder and decoder are 0.3, 0.5 respectively.
 We used the AdamW optimizer and the learning rate is 2e-5.
 
 **Pre-training data**:
@@ -208,33 +221,47 @@ 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 share negatives among different GPUs, 
+We employ the cross-device negatives sharing method to sharing 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). 
 We used the AdamW optimizer and the learning rate is 1e-5.
 The temperature for contrastive loss is 0.01.
 
-For the version with `*-instrcution`, we add instruction to the query for the retrieval task in the training. 
-For English, the instruction is `Represent this sentence for searching relevant passages: `;
-For Chinese, the instruction is `为这个句子生成表示以用于检索相关文章：`.
+For the version with `*-instrcution`, we add instruction to the query for retrieval task in the training. 
+For english, the instruction is `Represent this sentence for searching relevant passages: `;
+For chinese, the instruction is `为这个句子生成表示以用于检索相关文章：`.
 In the evaluation, the instruction should be added for sentence to passages retrieval task, not be added for other tasks.
 
 
-The finetune script is accessible in this repository: [flag_embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/flag_embedding/baai_general_embedding/README.md). 
+The finetune script is accessible in this repository: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). 
 You can easily finetune your model with it.
 
 **Training data**:
 
 - For English, we collect 230M text pairs from [wikipedia](https://huggingface.co/datasets/wikipedia), [cc-net](https://github.com/facebookresearch/cc_net), and so on.
 
-- For Chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on.
+- For chinese, we collect 120M text pairs from [wudao](https://github.com/BAAI-WuDao/Data), [simclue](https://github.com/CLUEbenchmark/SimCLUE) and so on.
 
 **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](). The released models can be used for commercial purposes free of charge.
+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.