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1 | | -<h1 align="center">FlagEmbedding</h1> |
| 1 | +<h1 align="center">⚡️BGE: One-Stop Retrieval Toolkit For Search and RAG</h1> |
2 | 2 | <p align="center"> |
3 | 3 | <a href="https://huggingface.co/collections/BAAI/bge-66797a74476eb1f085c7446d"> |
4 | 4 | <img alt="Build" src="https://img.shields.io/badge/BGE_series-🤗-yellow"> |
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12 | 12 | <a href="https://huggingface.co/C-MTEB"> |
13 | 13 | <img alt="Build" src="https://img.shields.io/badge/C_MTEB-🤗-yellow"> |
14 | 14 | </a> |
15 | | - <a href="https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding"> |
| 15 | + <a href="https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/baai_general_embedding"> |
16 | 16 | <img alt="Build" src="https://img.shields.io/badge/FlagEmbedding-1.1-red"> |
17 | 17 | </a> |
18 | 18 | </p> |
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30 | 30 | <p> |
31 | 31 | </h4> |
32 | 32 |
|
33 | | - |
34 | | - |
35 | 33 | [English](README.md) | [中文](https://github.com/hanhainebula/FlagEmbedding/blob/new-flagembedding-v1/README_zh.md) |
36 | 34 |
|
37 | | -FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: |
| 35 | +BGE (BAAI General Embedding) focuses on retrieval-augmented LLMs, consisting of the following projects currently: |
| 36 | + |
| 37 | + |
38 | 38 |
|
39 | 39 | - **Inference**: [Embedder](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/inference/embedder), [Reranker](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/inference/reranker) |
40 | 40 | - **Finetune**: [Embedder](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/finetune/embedder), [Reranker](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/finetune/reranker) |
41 | | -- **Evaluation**: [MTEB](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#1-mteb), [BEIR](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#2-beir), [MSMARCO](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#3-msmarco), [MIRACL](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#4-miracl), [MLDR](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#5-mldr), [MKQA](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#6-mkqa), [AIR-Bench](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#7-air-bench), [Custom Dataset](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation#8-custom-dataset) |
42 | | -- **[Dataset](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/dataset)**: [MLDR](https://huggingface.co/datasets/Shitao/MLDR), [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data), [public-data](https://huggingface.co/datasets/cfli/bge-e5data), [full-data](https://huggingface.co/datasets/cfli/bge-full-data), [reranker-data](Shitao/bge-reranker-data) |
| 41 | +- **[Evaluation](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/examples/evaluation)** |
| 42 | +- **[Dataset](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/dataset)** |
43 | 43 | - **[Tutorials](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/Tutorials)** |
44 | | -- **research**: |
45 | | - - **Long-Context LLM**: [Activation Beacon](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/Long_LLM/activation_beacon), [LongLLM QLoRA](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/Long_LLM/longllm_qlora) |
46 | | - - **Fine-tuning of LM** : [LM-Cocktail](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/LM_Cocktail) |
47 | | - - **Embedding Model**: [Visualized-BGE](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/visual_bge), [BGE-M3](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/BGE_M3), [LLM Embedder](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/llm_embedder), [BGE Embedding](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/baai_general_embedding) |
48 | | - - **Reranker Model**: [llm rerankers](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/llm_reranker), [BGE Reranker](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/reranker) |
49 | | - - **Benchmark**: [C-MTEB](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research/C_MTEB), [AIR-Bench](https://github.com/AIR-Bench/AIR-Bench), [MLVU](https://github.com/JUNJIE99/MLVU) |
| 44 | +- **[research](https://github.com/hanhainebula/FlagEmbedding/tree/new-flagembedding-v1/research)** |
50 | 45 |
|
51 | 46 | ## News |
52 | 47 | - 22/10/2024: :fire: 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. |
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