@@ -18,116 +21,106 @@
- 
+ 
](https://flagopen.baai.ac.cn/)
-
-- News | - Installation | - Quick Start | - Community | - Projects | - Model List | - Contributor | + 更新 | + 安装 | + 快速开始 | + 社区 | + 项目 | + 模型列表 | + 贡献者 | Citation | License
+[English](https://github.com/FlagOpen/FlagEmbedding/blob/master/README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
-[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
+BGE (BAAI General Embedding) 专注于检索增强llm领域，目前包括以下项目:
+
+- **推理**: [Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/embedder), [Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker)
+- **微调**: [Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder), [Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/reranker)
+- **[评估](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/evaluation)**
+- **[数据集](https://github.com/FlagOpen/FlagEmbedding/tree/master/dataset)**
+- **[教程](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials)**
+- **[研究](https://github.com/FlagOpen/FlagEmbedding/tree/master/research)**
-## News
+## 更新
-- 3/6/2025: :fire::fire: Introduce **BGE-VL** ([HF repo](https://huggingface.co/collections/BAAI/megapairs-67c6bbe49c15a9e7a7c69d92)), State-Of-The-Art multimodal embedding models to support Any visual search applications (everything, including text-to-image, image-to-text, image&prompt-to-image, text-to-image&text, and more)! They are released under the MIT license and are completely free for both academic and commercial use. We also release **MegaPairs** ([repo](https://github.com/VectorSpaceLab/MegaPairs), [paper](https://arxiv.org/abs/2412.14475)), a massive synthetic dataset which empowers BGE-VL!
-- 12/5/2024: :book: We built the [BGE documentation](https://www.bge-model.com) for centralized BGE information and materials!
-- 10/29/2024: :earth_asia: We created WeChat group for BGE. Scan the [QR code](./imgs/BGE_WeChat_Group.png) to join the group chat! To get the first hand message about our updates and new release, or having any questions or ideas, join us now!
+- 10/29/2024: :earth_asia: 我们建立了[BGE技术交流群](./BGE_WeChat_Group.png)，欢迎扫码入群！
- 
-
-- 10/22/2024: We release another interesting model: [OmniGen](https://github.com/VectorSpaceLab/OmniGen), which is a unified image generation model supporting various tasks. OmniGen can accomplish complex image generation tasks without the need for additional plugins like ControlNet, IP-Adapter, or auxiliary models such as pose detection and face detection.
-- 9/10/2024: Introducing **MemoRAG**, a step forward towards RAG 2.0 on top of memory-inspired knowledge discovery (repo: https://github.com/qhjqhj00/MemoRAG, paper: https://arxiv.org/pdf/2409.05591v1)
-- 9/2/2024: Start to maintain the [tutorials](./Tutorials/). The contents within will be actively updated and eariched, stay tuned! :books:
-- 7/26/2024: Release a new embedding model [bge-en-icl](https://huggingface.co/BAAI/bge-en-icl), an embedding model that incorporates in-context learning capabilities, which, by providing task-relevant query-response examples, can encode semantically richer queries, further enhancing the semantic representation ability of the embeddings.
-- 7/26/2024: Release a new embedding model [bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2), a multilingual embedding model based on gemma-2-9b, which supports multiple languages and diverse downstream tasks, achieving new SOTA on multilingual benchmarks (MIRACL, MTEB-fr, and MTEB-pl).
-- 7/26/2024: Release a new lightweight reranker [bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight), a lightweight reranker based on gemma-2-9b, which supports token compression and layerwise lightweight operations, can still ensure good performance while saving a significant amount of resources. :fire:
+- 10/22/2024：我们发布了新的模型：[OmniGen](https://github.com/VectorSpaceLab/OmniGen)，这是一个支持各种任务的统一图像生成模型。OmniGen可以在不需要额外插件（如ControlNet、IP-Adapter）或辅助模型（如姿态检测和人脸检测）的情况下完成复杂的图像生成任务。 :fire:
+- 9/10/2024：我们推出了**MemoRAG**，这是一种基于记忆启发的知识发现技术，是迈向 RAG 2.0 的关键一步（仓库：https://github.com/qhjqhj00/MemoRAG，论文：https://arxiv.org/pdf/2409.05591v1） :fire:
+- 9/2/2024: 开始维护更新[教程](./Tutorials/)，教程文件夹中的内容会在未来不断丰富，欢迎持续关注！ :books:
+- 7/26/2024：发布[bge-en-icl](https://huggingface.co/BAAI/bge-en-icl)。这是一个结合了上下文学习能力的文本检索模型，通过提供与任务相关的查询-回答示例，可以编码语义更丰富的查询，进一步增强嵌入的语义表征能力。 :fire:
+- 7/26/2024: 发布[bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2)。这是一个基于gemma-2-9b的多语言文本向量模型，同时支持多种语言和多样的下游任务，在多语言检索数据集 MIRACL, MTEB-fr, MTEB-pl 上取得了迄今最好的实验结果。 :fire:
+- 7/26/2024：发布新的轻量级重排器[bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight)。这是一个基于gemma-2-9b的轻量级重排器，支持令牌压缩和分层轻量操作，在节省大量资源的同时，仍能确保良好的性能。:fire:
-BGE (BAAI General Embedding) focuses on retrieval-augmented LLMs, consisting of the following projects currently:
-
-
-
-- **Inference**: [Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/embedder), [Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker)
-- **Finetune**: [Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder), [Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/reranker)
-- **[Evaluation](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/evaluation)**
-- **[Dataset](https://github.com/FlagOpen/FlagEmbedding/tree/master/dataset)**
-- **[Tutorials](https://github.com/FlagOpen/FlagEmbedding/tree/master/Tutorials)**
-- **[research](https://github.com/FlagOpen/FlagEmbedding/tree/master/research)**
-
-
-## Installation
-### Using pip:
-If you do not want to finetune the models, you can install the package without the finetune dependency:
+## 安装
+### 使用pip:
+如果你不想微调模型，你可以直接安装包，不用finetune依赖：
```
pip install -U FlagEmbedding
```
-If you want to finetune the models, you can install the package with the finetune dependency:
+如果你想微调模型，你可以用finetune依赖安装：
```
pip install -U FlagEmbedding[finetune]
```
-### Install from sources:
+### 从源文件安装部署:
-Clone the repository and install
+克隆并安装FlagEmbedding：
```
git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
-# If you do not need to finetune the models, you can install the package without the finetune dependency:
+# 如果你不想微调模型，你可以直接安装包，不用finetune依赖：
pip install .
-# If you want to finetune the models, install the package with the finetune dependency:
+# 如果你想微调模型，你可以用finetune依赖安装：
# pip install .[finetune]
```
-For development in editable mode:
+在可编辑模式下安装:
```
-# If you do not need to finetune the models, you can install the package without the finetune dependency:
+# 如果你不想微调模型，你可以直接安装包，不用finetune依赖：
pip install -e .
-# If you want to finetune the models, install the package with the finetune dependency:
+# 如果你想微调模型，你可以用finetune依赖安装：
# pip install -e .[finetune]
```
-## Quick Start
-First, load one of the BGE embedding model:
+## 快速开始
+首先，加载一个BGE向量模型：
```
from FlagEmbedding import FlagAutoModel
@@ -135,76 +128,67 @@ model = FlagAutoModel.from_finetuned('BAAI/bge-base-en-v1.5',
query_instruction_for_retrieval="Represent this sentence for searching relevant passages:",
use_fp16=True)
```
-Then, feed some sentences to the model and get their embeddings:
+将语句作为模型输入，得到向量：
```
sentences_1 = ["I love NLP", "I love machine learning"]
sentences_2 = ["I love BGE", "I love text retrieval"]
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
```
-Once we get the embeddings, we can compute similarity by inner product:
+取得向量后，通过内积计算相似度：
```
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
-For more details, you can refer to [embedder inference](./examples/inference/embedder), [reranker inference](./examples/inference/reranker), [embedder finetune](./examples/finetune/embedder), [reranker fintune](./examples/finetune/reranker), [evaluation](./examples/evaluation).
+关于更多细节，可以参考[embedder推理](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/embedder), [reranker推理](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/inference/reranker), [embedder微调](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/embedder), [reranker微调](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune/reranker), [评估](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/evaluation), [研究](https://github.com/FlagOpen/FlagEmbedding/tree/master/research).
-If you're unfamiliar with any of related concepts, please check out the [tutorial](./Tutorials/). If it's not there, let us know.
+## 社区
-For more interesting topics related to BGE, take a look at [research](./research).
+我们将持续维护BGE及FlagEmbedding社区，有任何想法建议都欢迎告诉我们！
-## Community
+近期会持续更新[教学](./Tutorials/)中的内容，希望为文本检索以及RAG打造出完整且详细的教学，欢迎持续关注！
-We are actively maintaining the community of BGE and FlagEmbedding. Let us know if you have any suggessions or ideas!
+在未来将会更新以下内容：
-Currently we are updating the [tutorials](./Tutorials/), we aim to create a comprehensive and detailed tutorial for beginners on text retrieval and RAG. Stay tuned!
-
-The following contents are releasing in the upcoming weeks:
-
-- Evaluation
-- BGE-EN-ICL
+- RAG
More
-
-- 6/7/2024: Release a new benchmark [MLVU](https://github.com/JUNJIE99/MLVU), the first comprehensive benchmark specifically designed for long video understanding. MLVU features an extensive range of video durations, a diverse collection of video sources, and a set of evaluation tasks uniquely tailored for long-form video understanding. :fire:
-- 5/21/2024: Release a new benchmark [AIR-Bench](https://github.com/AIR-Bench/AIR-Bench) together with Jina AI, Zilliz, HuggingFace, and other partners. AIR-Bench focuses on a fair out-of-distribution evaluation for Neural IR & RAG. It generates the synthetic data for benchmarking w.r.t. diverse domains and languages. It is dynamic and will be updated on regular basis. [Leaderboard](https://huggingface.co/spaces/AIR-Bench/leaderboard) :fire:
-- 4/30/2024: Release [Llama-3-8B-Instruct-80K-QLoRA](https://huggingface.co/namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA), extending the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA training on a few synthesized long-context data. The model achieves remarkable performance on various long-context benchmarks. [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/Long_LLM/longllm_qlora) :fire:
-- 3/18/2024: Release new [rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_reranker), built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually :smiley:) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation :fire:
-- 3/18/2024: Release [Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/visual_bge), equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text data. :fire:
-- 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).
-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](https://arxiv.org/pdf/2402.03216.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/BGE_M3). :fire:
-- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/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)
-- 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) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LLARA)
-- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534)
-- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
-- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) 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.
-- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/baai_general_embedding): 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](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
-- 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](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
-
+- 6/7/2024: 发布首个专为长视频理解设计的全面评测基准[MLVU](https://github.com/JUNJIE99/MLVU)。MLVU拥有丰富的视频时长范围，多样化的视频来源，以及多个专为长视频理解设计的评估任务。 :fire:
+- 5/21/2024：联合 Jina AI、Zilliz、HuggingFace 等机构发布评测基准 [AIR-Bench](https://github.com/AIR-Bench/AIR-Bench)，针对检索任务和 RAG 场景设计。AIR-Bench 首次提出在检索任务中使用 LLMs 自动化生产评估数据，避免模型过拟合测试数据。AIR-Bench 不需要人工参与标注数据，因而可以更灵活覆盖更多垂直领域和不同语种。同时 AIR-Bench 会定期进行更新从而满足社区不断变化的评测需求。[Leaderboard](https://huggingface.co/spaces/AIR-Bench/leaderboard) :fire:
+- 4/30/2024: 发布[Llama-3-8B-Instruct-80K-QLoRA](https://huggingface.co/namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA), 其通过在少量合成的长文本数据上的QLoRA训练，有效地将Llama-3-8B-Instruct的上下文长度从8K扩展到80K。详见[代码](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/Long_LLM/longllm_qlora) :fire:
+- 3/18/2024: 发布新的[rerankers](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_reranker), 拥有更好的性能同时支持多语言和长文本。 :fire:
+- 3/18/2024: 发布[Visualized-BGE](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/visual_bge)，该项目通过引入image token embedding赋予BGE视觉编码能力。Visualized-BGE可以对混合图文数据进行编码，用于广泛的混合模态检索任务。 :fire:
+- 1/30/2024: 发布**BGE-M3**, 第一个具有多功能、多语言和多粒度特性的文本检索模型，高效支持多语言（100+语言）、长文本（至多8192长度的输入文本）、和混合检索（稠密、稀疏、多向量）。 详见[report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf)和[代码](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/BGE_M3) :fire:
+- 1/9/2024: 发布[Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/Long_LLM/activation_beacon), 一个有效、高效、兼容、低成本（训练）的扩展大预言模型上下文长度的方法。[技术报告](https://arxiv.org/abs/2401.03462)
+- 12/24/2023: 发布**LLaRA**, 一个基于LLaMA-7B的稠密检索模型, MS MARCO与BEIR上取得了迄今最好的实验结果. 模型与代码将会陆续开源. 敬请关注. [技术报告](https://arxiv.org/abs/2312.15503) 和 [代码](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LLARA)
+- 11/23/2023: 发布[LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/LM_Cocktail), 一种通过模型融合在微调时保持原有模型通用能力的方法. [技术报告](https://arxiv.org/abs/2311.13534)
+- 10/12/2023: 发布 [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_embedder), 专为大语言模型**各种检索增强任务设计**的英文向量模型。[技术报告](https://arxiv.org/pdf/2310.07554.pdf)
+- 09/15/2023: 发布 [技术报告](https://arxiv.org/pdf/2309.07597.pdf) 和 [数据集](https://data.baai.ac.cn/details/BAAI-MTP).
+- 09/12/2023: 更新：
+ - **新增重排模型**：开源交叉编码器模型bge-reranker，具有比向量模型更强大的排序能力。非常建议使用或者微调它来重新排序向量模型返回的top-k文档，提高最终结果的相关性。
+ - **更新向量模型**：发布bge-*-v1.5向量模型，缓解相似度分布问题，提升无指令情况下的检索能力（但检索任务仍建议使用指令）
+- 09/07/2023: 更新[微调代码](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/baai_general_embedding): 增加难负样本挖掘脚本，增加指令参数方便在微调中添加指令.
+- 08/09/2023: BGE模型整合入Langchain, 可以在langchain中非常简单的[使用它](#using-langchain); C-MTEB中文榜单已[在线更新](https://huggingface.co/spaces/mteb/leaderboard).
+- 08/05/2023: 发布更小的模型(base, small), **在同尺寸模型中取得最好的性能！ 🤗**
+- 08/02/2023: :tada: :tada: 发布中英文向量模型BGE(BAAI General Embedding的缩写), **在MTEB和C-MTEB榜单上取得最好的性能**
+- 08/01/2023: 发布大规模中文文本向量[评测榜单](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/C_MTEB) (**C-MTEB**), 其包括31个测试任务.
-
+## 模型列表
-## Model List
-
-`bge` is short for `BAAI general embedding`.
-
-| Model | Language | Description | query instruction for retrieval |
-|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|
-| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | A LLM-based embedding model with in-context learning capabilities, which can fully leverage the model's potential based on a few shot examples | Provide instructions and few-shot examples freely based on the given task. |
-| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | Multilingual | A LLM-based multilingual embedding model, trained on a diverse range of languages and tasks. | Provide instructions based on the given task. |
-| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
-| [LM-Cocktail](https://huggingface.co/Shitao) | English | fine-tuned models (Llama and BGE) which can be used to reproduce the results of LM-Cocktail | |
-| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_embedder) |
-| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | Multilingual | a lightweight cross-encoder model, possesses strong multilingual capabilities, easy to deploy, with fast inference. | |
-| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | Multilingual | a cross-encoder model which is suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. | |
-| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | Multilingual | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. | |
-| [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) | Multilingual | a cross-encoder model which is suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers, compress ratio and compress layers for output, facilitating accelerated inference. | |
-| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | a cross-encoder model which is more accurate but less efficient | |
-| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | a cross-encoder model which is more accurate but less efficient | |
-| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | 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](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | 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](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | 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](https://huggingface.co/BAAI/bge-large-en) | English | Embedding Model which map text into vector | `Represent this sentence for searching relevant passages: ` |
-| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | a base-scale model but with similar ability to `bge-large-en` | `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 | Embedding Model which map text into vector | `为这个句子生成表示以用于检索相关文章：` |
-| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | 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](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：` |
+| Model | Language | Description | query instruction for retrieval |
+|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|
+| [BAAI/bge-en-icl](https://huggingface.co/BAAI/bge-en-icl) | English | 基于大型语言模型的向量模型，具有上下文学习能力，能够基于少量示例充分发挥模型的潜力。 | 根据给定的任务自由提供指示和少数示例。 |
+| [BAAI/bge-multilingual-gemma2](https://huggingface.co/BAAI/bge-multilingual-gemma2) | Multilingual | 基于大型语言模型的多语言向量模型，在多种语言和任务上训练，适应多样化的下游场景。 | 根据给定的任务自由提供指示和少数示例。 |
+| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | 多功能（向量检索，稀疏检索，多表征检索）、多语言、多粒度（最大长度8192） | |
+| [LM-Cocktail](https://huggingface.co/Shitao) | English | 微调的Llama和BGE模型，可以用来复现LM-Cocktail论文的结果 | |
+| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | 专为大语言模型各种检索增强任务设计的向量模型 | 详见[README](https://github.com/FlagOpen/FlagEmbedding/tree/master/research/llm_embedder) |
+| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | Multilingual | 一个轻量级的交叉编码器模型，具有强大的多语言能力，易于部署，具有快速的推理能力。 | |
+| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) | Multilingual | 一个支持多语言的交叉编码器模型，在英文和多语言能力方面均表现出色。 | |
+| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | Multilingual | 一个支持多语言的交叉编码器模型，在英文和中文方面均表现良好，允许自由选择输出层，以便加速推理。 | |
+| [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) | Multilingual | 一个支持多语言的跨编码器模型，不仅在英文和中文上表现良好，还允许自由选择输出层、压缩比例和压缩层，从而便于加速推理。 | |
+| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | 交叉编码器模型，精度比向量模型更高但推理效率较低 | |
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | 交叉编码器模型，精度比向量模型更高但推理效率较低 | |
+| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | 1.5版本，相似度分布更加合理 | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | 1.5版本，相似度分布更加合理 | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | 1.5版本，相似度分布更加合理 | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | 1.5版本，相似度分布更加合理 | `为这个句子生成表示以用于检索相关文章：` |
+| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | 1.5版本，相似度分布更加合理 | `为这个句子生成表示以用于检索相关文章：` |
+| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | 1.5版本，相似度分布更加合理 | `为这个句子生成表示以用于检索相关文章：` |
+| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | 向量模型，将文本转换为向量 | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | base-scale 向量模型 | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | base-scale 向量模型 | `Represent this sentence for searching relevant passages: ` |
+| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | 向量模型，将文本转换为向量 | `为这个句子生成表示以用于检索相关文章：` |
+| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | base-scale 向量模型 | `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 | base-scale 向量模型 | `为这个句子生成表示以用于检索相关文章：` |
+## 贡献者:
-
-### Contributors:
-Thank all our contributors for their efforts and warmly welcome new members to join in!
+十分感谢所有参与FlagEmbedding社区成员的贡献，也欢迎新的成员加入！
The whole tutorial roadmap
+ 教程规划
@@ -212,21 +196,10 @@ Thank all our contributors for their efforts and warmly welcome new members to j
-
## Citation
-If you find this repository useful, please consider giving a star :star: and citation
-
+如果您觉得我们的工作有所帮助，请考虑点个星 :star: 和引用以下论文:
```
-@misc{bge_m3,
- title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
- author={Chen, Jianlv and Xiao, Shitao and Zhang, Peitian and Luo, Kun and Lian, Defu and Liu, Zheng},
- year={2023},
- eprint={2309.07597},
- archivePrefix={arXiv},
- primaryClass={cs.CL}
-}
-
@misc{cocktail,
title={LM-Cocktail: Resilient Tuning of Language Models via Model Merging},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Xingrun Xing},
@@ -256,5 +229,7 @@ If you find this repository useful, please consider giving a star :star: and cit
```
## License
-FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE).
+FlagEmbedding基于[MIT License](LICENSE)开源协议。
+
+