BGE-Reranker.

We have updated the [new reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker), supporting larger lengths, more languages, and achieving better performance.

<h1 align="center">FlagEmbedding</h1>

<h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4>

More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).

English | 中文

FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:

• Long-Context LLM: Activation Beacon
• Fine-tuning of LM : LM-Cocktail
• Embedding Model: Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding
• Reranker Model: llm rerankers, BGE Reranker
• Benchmark: C-MTEB

News

• 3/18/2024: Release new rerankers, built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation.
• 3/18/2024: Release Visualized-BGE, equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text data.
• 1/30/2024: Release BGE-M3, a new member to BGE model series! M3 stands for Multi-linguality (100+ languages), Multi-granularities (input length up to 8192), Multi-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).

It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. Technical Report and Code. :fire:

• 1/9/2024: Release Activation-Beacon, an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. Technical Report :fire:
• 12/24/2023: Release LLaRA, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. Technical Report
• 11/23/2023: Release LM-Cocktail, a method to maintain general capabilities during fine-tuning by merging multiple language models. Technical Report :fire:
• 10/12/2023: Release LLM-Embedder, a unified embedding model to support diverse retrieval augmentation needs for LLMs. Technical Report
• 09/15/2023: The technical report of BGE has been released
• 09/15/2023: The massive training data 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.

<details> <summary>More</summary> <!-- ### More -->

• 09/07/2023: Update fine-tune code: Add script to mine hard negatives and support adding instruction during fine-tuning.
• 08/09/2023: BGE Models are integrated into Langchain, you can use it like this; C-MTEB leaderboard is available.
• 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 (C-MTEB), consisting of 31 test dataset.

</details>

Model List

bge is short for BAAI general embedding.

| Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | BAAI/bge-m3 | Multilingual | Inference Fine-tune | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | | BAAI/llm-embedder | English | Inference Fine-tune | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See README | | BAAI/bge-reranker-large | Chinese and English | Inference Fine-tune | a cross-encoder model which is more accurate but less efficient [2] | | | BAAI/bge-reranker-base | Chinese and English | Inference Fine-tune | a cross-encoder model which is more accurate but less efficient [2] | | | BAAI/bge-large-en-v1.5 | English | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | Represent this sentence for searching relevant passages: | | BAAI/bge-base-en-v1.5 | English | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | Represent this sentence for searching relevant passages: | | BAAI/bge-small-en-v1.5 | English | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | Represent this sentence for searching relevant passages: | | BAAI/bge-large-zh-v1.5 | Chinese | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | 为这个句子生成表示以用于检索相关文章： | | BAAI/bge-base-zh-v1.5 | Chinese | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | 为这个句子生成表示以用于检索相关文章： | | BAAI/bge-small-zh-v1.5 | Chinese | Inference Fine-tune | version 1.5 with more reasonable similarity distribution | 为这个句子生成表示以用于检索相关文章： | | BAAI/bge-large-en | English | Inference Fine-tune | :trophy: rank 1st in MTEB leaderboard | Represent this sentence for searching relevant passages: | | BAAI/bge-base-en | English | Inference Fine-tune | a base-scale model but with similar ability to bge-large-en | Represent this sentence for searching relevant passages: | | BAAI/bge-small-en | English | Inference Fine-tune |a small-scale model but with competitive performance | Represent this sentence for searching relevant passages: | | BAAI/bge-large-zh | Chinese | Inference Fine-tune | :trophy: rank 1st in C-MTEB benchmark | 为这个句子生成表示以用于检索相关文章： | | BAAI/bge-base-zh | Chinese | Inference Fine-tune | a base-scale model but with similar ability to 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 | Chinese | Inference Fine-tune | 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.

[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.

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 .

Frequently asked questions

<details> <summary>1. How to fine-tune bge embedding model?</summary>

<!-- ### How to fine-tune bge embedding model? --> Following this example to prepare data and fine-tune your model. Some suggestions:

• Mine hard negatives following this example, which can improve the retrieval performance.
• 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. Refer to this example for the fine-tuning for reranker

</details>

<details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>

<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.

Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar.

For downstream tasks, such as passage retrieval or semantic similarity, what matters is the relative order of the scores, not the absolute value. If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.

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