Update README.md (#6)

- Update README.md (1661329696483c2d43fae5f241c862deaa8b9dc6)


Co-authored-by: namespace-Pt <namespace-Pt@users.noreply.huggingface.co>

README.md

@@ -1,10 +1,18 @@
----
-license: mit
----
-
-
 <h1 align="center">FlagEmbedding</h1>
-
+<p align="center">
+    <a href="https://github.com/FlagOpen/FlagEmbedding">
+            <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/baai_general_embedding">
+        <img alt="Build" src="https://img.shields.io/badge/FlagEmbedding-1.1-red">
+    </a>
+</p>
 
 <h4 align="center">
     <p>
@@ -19,18 +27,18 @@ license: mit
     <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)
 
-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 databases for LLMs.
+<span style="#FF69B4;"> **Hiring:** We're seeking experienced NLP researchers and intern students focusing on dense retrieval and retrieval-augmented LLMs. If you're interested, please feel free to reach out to us via email at zhengliu1026@gmail.com.</span>
+
+FlagEmbedding can map any text to a low-dimensional dense vector, which can be used for tasks like retrieval, classification,  clustering, and semantic search.
+And it can also be used in vector databases for LLMs.
 
 ************* 🌟**Updates**🌟 *************
-- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf)  :fire:  
+- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire:  
 - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released 
-- 09/15/2023: The [masive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released 
+- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released 
 - 09/12/2023: New models: 
     - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. 
     - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
@@ -72,29 +80,27 @@ And it also can be used in vector databases for LLMs.
 |  [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) |   Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | 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：`  |
 
 
-[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
+[1\]: If you need to search the relevant passages in a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
 
-[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. 
-For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
+[2\]: Different from the embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. 
+For example, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 documents to get the final top-3 results.
 
 All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. 
-If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
+If you cannot open the Huggingface Hub, you can also download the models at https://model.baai.ac.cn/models .
 
 
 ## Frequently asked questions
 
-<details>
-  <summary>1. How to fine-tune bge embedding model?</summary>
+**1. How to fine-tune bge embedding model?**
 
-  <!-- ### How to fine-tune bge embedding model? -->
 Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. 
 Some suggestions:
 - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
+- In general, larger hyper-parameter `per_device_train_batch_size` brings better performance. You can expand it by enabling `--fp16`, `--deepspeed df_config.json` (df_config.json can refer to [ds_config.json](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/ds_config.json), `--gradient_checkpointing`, etc.
 - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
 - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
 
-  
-</details>
+
 
 <details>
   <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
@@ -134,7 +140,7 @@ In all cases, the documents/passages do not need to add the instruction.
 
 ### Usage for Embedding Model
 
-Here are some examples for using `bge` models with 
+Here are some examples of using `bge` models with 
 [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
 
 #### Using FlagEmbedding
@@ -366,11 +372,11 @@ See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for
 
 ### BAAI Embedding 
 
-We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning. 
+We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pair data using contrastive learning. 
 **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
 We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
 Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
-More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
+For more training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
 
 
 
@@ -381,8 +387,14 @@ which is more accurate than embedding model (i.e., bi-encoder) but more time-con
 Therefore, it can be used to re-rank the top-k documents returned by embedding model.
 We train the cross-encoder on a multilingual pair data, 
 The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). 
-More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
+For more details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
+
 
+### Our Contributors:
+
+<a href="https://github.com/FlagOpen/FlagEmbedding/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=FlagOpen/FlagEmbedding" />
+</a>
 
 ## Contact
 If you have any question or suggestion related to this project, feel free to open an issue or pull request.
@@ -402,6 +414,15 @@ If you find this repository useful, please consider giving a star :star: and cit
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
+
+@misc{llm_embedder,
+      title={Retrieve Anything To Augment Large Language Models}, 
+      author={Peitian Zhang and Shitao Xiao and Zheng Liu and Zhicheng Dou and Jian-Yun Nie},
+      year={2023},
+      eprint={2310.07554},
+      archivePrefix={arXiv},
+      primaryClass={cs.IR}
+}
 ```
 
 ## License