|
--- |
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license: mit |
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language: |
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- zh |
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- en |
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tags: |
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- mteb |
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model-index: |
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- name: bge-reranker-large |
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results: |
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- task: |
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type: Reranking |
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dataset: |
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type: C-MTEB/CMedQAv1-reranking |
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name: MTEB CMedQAv1 |
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config: default |
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split: test |
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revision: None |
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metrics: |
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- type: map |
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value: 82.13813829648727 |
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- type: mrr |
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value: 84.92349206349207 |
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- task: |
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type: Reranking |
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dataset: |
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type: C-MTEB/CMedQAv2-reranking |
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name: MTEB CMedQAv2 |
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config: default |
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split: test |
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revision: None |
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metrics: |
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- type: map |
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value: 84.19313276771856 |
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- type: mrr |
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value: 86.96876984126984 |
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- task: |
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type: Reranking |
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dataset: |
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type: C-MTEB/Mmarco-reranking |
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name: MTEB MMarcoReranking |
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config: default |
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split: dev |
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revision: None |
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metrics: |
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- type: map |
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value: 37.16533876035345 |
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- type: mrr |
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value: 36.60039682539682 |
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- task: |
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type: Reranking |
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dataset: |
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type: C-MTEB/T2Reranking |
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name: MTEB T2Reranking |
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config: default |
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split: dev |
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revision: None |
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metrics: |
|
- type: map |
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value: 67.60068968300665 |
|
- type: mrr |
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value: 77.68363585560605 |
|
--- |
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<h1 align="center">FlagEmbedding</h1> |
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<h4 align="center"> |
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<p> |
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<a href=#model-list>Model List</a> | |
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<a href=#frequently-asked-questions>FAQ</a> | |
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<a href=#usage>Usage</a> | |
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<a href="#evaluation">Evaluation</a> | |
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<a href="#train">Train</a> | |
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<a href="#contact">Contact</a> | |
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<a href="#citation">Citation</a> | |
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<a href="#license">License</a> |
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<p> |
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</h4> |
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More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). |
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[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) |
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FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: |
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- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon) |
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- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail) |
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- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding) |
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- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) |
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- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) |
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## News |
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- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval). |
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It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks. |
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[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire: |
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- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire: |
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- 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](https://arxiv.org/abs/2312.15503) :fire: |
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- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire: |
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- 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. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf) |
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- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released |
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- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released |
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- 09/12/2023: New models: |
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- **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. |
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- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. |
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<details> |
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<summary>More</summary> |
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<!-- ### More --> |
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- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. |
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- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). |
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- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** |
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- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: |
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- 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. |
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</details> |
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## Model List |
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`bge` is short for `BAAI general embedding`. |
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| Model | Language | | Description | query instruction for retrieval [1] | |
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|:-------------------------------|:--------:| :--------:| :--------:|:--------:| |
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| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | | |
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| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) | |
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| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | |
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| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | |
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| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | 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：` | |
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| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | 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：` | |
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| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | 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：` | |
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| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | |
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| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :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：` | |
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| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | 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：` | |
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| [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：` | |
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[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. |
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[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. |
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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. |
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All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. |
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If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models . |
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## Frequently asked questions |
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<details> |
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<summary>1. How to fine-tune bge embedding model?</summary> |
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<!-- ### How to fine-tune bge embedding model? --> |
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Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. |
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Some suggestions: |
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- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. |
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- 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. |
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- 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. |
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</details> |
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<details> |
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<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> |
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<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> |
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**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** |
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Since we finetune the models by contrastive learning with a temperature of 0.01, |
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the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. |
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So a similarity score greater than 0.5 does not indicate that the two sentences are similar. |
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For downstream tasks, such as passage retrieval or semantic similarity, |
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**what matters is the relative order of the scores, not the absolute value.** |
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If you need to filter similar sentences based on a similarity threshold, |
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please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). |
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</details> |
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<details> |
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<summary>3. When does the query instruction need to be used</summary> |
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<!-- ### When does the query instruction need to be used --> |
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For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. |
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No instruction only has a slight degradation in retrieval performance compared with using instruction. |
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So you can generate embedding without instruction in all cases for convenience. |
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For a retrieval task that uses short queries to find long related documents, |
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it is recommended to add instructions for these short queries. |
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**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** |
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In all cases, the documents/passages do not need to add the instruction. |
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</details> |
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## Usage |
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### Usage for Embedding Model |
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Here are some examples for using `bge` models with |
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[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). |
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#### Using FlagEmbedding |
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``` |
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pip install -U FlagEmbedding |
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``` |
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If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. |
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```python |
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from FlagEmbedding import FlagModel |
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sentences_1 = ["样例数据-1", "样例数据-2"] |
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sentences_2 = ["样例数据-3", "样例数据-4"] |
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model = FlagModel('BAAI/bge-large-zh-v1.5', |
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query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", |
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use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
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embeddings_1 = model.encode(sentences_1) |
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embeddings_2 = model.encode(sentences_2) |
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similarity = embeddings_1 @ embeddings_2.T |
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print(similarity) |
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# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query |
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# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction |
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queries = ['query_1', 'query_2'] |
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passages = ["样例文档-1", "样例文档-2"] |
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q_embeddings = model.encode_queries(queries) |
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p_embeddings = model.encode(passages) |
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scores = q_embeddings @ p_embeddings.T |
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``` |
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For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). |
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By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. |
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You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. |
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#### Using Sentence-Transformers |
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You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): |
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``` |
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pip install -U sentence-transformers |
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``` |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences_1 = ["样例数据-1", "样例数据-2"] |
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sentences_2 = ["样例数据-3", "样例数据-4"] |
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model = SentenceTransformer('BAAI/bge-large-zh-v1.5') |
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embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) |
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embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) |
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similarity = embeddings_1 @ embeddings_2.T |
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print(similarity) |
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``` |
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For s2p(short query to long passage) retrieval task, |
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each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). |
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But the instruction is not needed for passages. |
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```python |
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from sentence_transformers import SentenceTransformer |
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queries = ['query_1', 'query_2'] |
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passages = ["样例文档-1", "样例文档-2"] |
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instruction = "为这个句子生成表示以用于检索相关文章：" |
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model = SentenceTransformer('BAAI/bge-large-zh-v1.5') |
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q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) |
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p_embeddings = model.encode(passages, normalize_embeddings=True) |
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scores = q_embeddings @ p_embeddings.T |
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``` |
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#### Using Langchain |
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You can use `bge` in langchain like this: |
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```python |
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from langchain.embeddings import HuggingFaceBgeEmbeddings |
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model_name = "BAAI/bge-large-en-v1.5" |
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model_kwargs = {'device': 'cuda'} |
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encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity |
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model = HuggingFaceBgeEmbeddings( |
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model_name=model_name, |
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model_kwargs=model_kwargs, |
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encode_kwargs=encode_kwargs, |
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query_instruction="为这个句子生成表示以用于检索相关文章：" |
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) |
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model.query_instruction = "为这个句子生成表示以用于检索相关文章：" |
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``` |
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#### Using HuggingFace Transformers |
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With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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import torch |
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# Sentences we want sentence embeddings for |
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sentences = ["样例数据-1", "样例数据-2"] |
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# Load model from HuggingFace Hub |
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tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') |
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model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') |
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model.eval() |
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# Tokenize sentences |
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encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
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# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) |
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# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') |
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# Compute token embeddings |
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with torch.no_grad(): |
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model_output = model(**encoded_input) |
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# Perform pooling. In this case, cls pooling. |
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sentence_embeddings = model_output[0][:, 0] |
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# normalize embeddings |
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sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) |
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print("Sentence embeddings:", sentence_embeddings) |
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``` |
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### Usage for Reranker |
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Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. |
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You can get a relevance score by inputting query and passage to the reranker. |
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The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. |
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#### Using FlagEmbedding |
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``` |
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pip install -U FlagEmbedding |
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``` |
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Get relevance scores (higher scores indicate more relevance): |
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```python |
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from FlagEmbedding import FlagReranker |
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reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation |
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|
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score = reranker.compute_score(['query', 'passage']) |
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print(score) |
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|
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scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) |
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print(scores) |
|
``` |
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|
|
|
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#### Using Huggingface transformers |
|
|
|
```python |
|
import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
|
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tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') |
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model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') |
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model.eval() |
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|
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pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] |
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with torch.no_grad(): |
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inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) |
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scores = model(**inputs, return_dict=True).logits.view(-1, ).float() |
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print(scores) |
|
``` |
|
|
|
#### Usage reranker with the ONNX files |
|
|
|
```python |
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from optimum.onnxruntime import ORTModelForSequenceClassification # type: ignore |
|
|
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import torch |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
|
|
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tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') |
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model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base') |
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model_ort = ORTModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-base', file_name="onnx/model.onnx") |
|
|
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# Sentences we want sentence embeddings for |
|
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] |
|
|
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# Tokenize sentences |
|
encoded_input = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt') |
|
|
|
scores_ort = model_ort(**encoded_input, return_dict=True).logits.view(-1, ).float() |
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# Compute token embeddings |
|
with torch.inference_mode(): |
|
scores = model_ort(**encoded_input, return_dict=True).logits.view(-1, ).float() |
|
|
|
# scores and scores_ort are identical |
|
``` |
|
#### Usage reranker with infinity |
|
|
|
Its also possible to deploy the onnx/torch files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. |
|
```python |
|
import asyncio |
|
from infinity_emb import AsyncEmbeddingEngine, EngineArgs |
|
|
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query='what is a panda?' |
|
docs = ['The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear', "Paris is in France."] |
|
|
|
engine = AsyncEmbeddingEngine.from_args( |
|
EngineArgs(model_name_or_path = "BAAI/bge-reranker-base", device="cpu", engine="torch" # or engine="optimum" for onnx |
|
)) |
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|
|
async def main(): |
|
async with engine: |
|
ranking, usage = await engine.rerank(query=query, docs=docs) |
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print(list(zip(ranking, docs))) |
|
asyncio.run(main()) |
|
``` |
|
|
|
## Evaluation |
|
|
|
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** |
|
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). |
|
|
|
- **MTEB**: |
|
|
|
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |
|
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | |
|
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | |
|
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | |
|
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | |
|
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | |
|
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | |
|
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | |
|
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | |
|
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | |
|
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | |
|
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | |
|
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | |
|
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | |
|
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | |
|
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | |
|
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | |
|
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | |
|
|
|
|
|
|
|
- **C-MTEB**: |
|
We create the 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 | |
|
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | |
|
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | |
|
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | |
|
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | |
|
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | |
|
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | |
|
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | |
|
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | |
|
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | |
|
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | |
|
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | |
|
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | |
|
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | |
|
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | |
|
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | |
|
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | |
|
|
|
|
|
- **Reranking**: |
|
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. |
|
|
|
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |
|
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| |
|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | |
|
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | |
|
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | |
|
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | |
|
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | |
|
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | |
|
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | |
|
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | |
|
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | |
|
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | |
|
|
|
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks |
|
|
|
## Train |
|
|
|
### 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. |
|
**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). |
|
|
|
|
|
|
|
### BGE Reranker |
|
|
|
Cross-encoder will perform full-attention over the input pair, |
|
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. |
|
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) |
|
|
|
|
|
## Contact |
|
If you have any question or suggestion related to this project, feel free to open an issue or pull request. |
|
You also can email Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). |
|
|
|
|
|
## Citation |
|
|
|
If you find this repository useful, please consider giving a star :star: and citation |
|
|
|
``` |
|
@misc{bge_embedding, |
|
title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, |
|
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, |
|
year={2023}, |
|
eprint={2309.07597}, |
|
archivePrefix={arXiv}, |
|
primaryClass={cs.CL} |
|
} |
|
``` |
|
|
|
## License |
|
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge. |
|
|
|
|
