From 7dc688608399ea81853a77d61d3226485b447d74 Mon Sep 17 00:00:00 2001 From: zhengliu <46858936+ZhengLiu101@users.noreply.github.com> Date: Sat, 8 Mar 2025 12:31:22 +0800 Subject: [PATCH] Update README.md --- README.md | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/README.md b/README.md index 1153d16..618077a 100644 --- a/README.md +++ b/README.md @@ -40,29 +40,18 @@ -BGE (BAAI General Embedding) focuses on retrieval-augmented LLMs, consisting of the following projects currently: - -![projects](./imgs/projects.png) - -- **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)** - ## News -- 3/6/2025: :fire: Release **MegaPairs** ([repo](https://github.com/VectorSpaceLab/MegaPairs), [paper](https://arxiv.org/abs/2412.14475)), massive data synthesis for universal multimodal retrieval. And corresponding [BGE-VL](https://huggingface.co/collections/BAAI/megapairs-67c6bbe49c15a9e7a7c69d92) series multimodel embedding models! +- 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! - bge_wechat_group -- 10/22/2024: :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. -- 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) :fire: +- 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. :fire: -- 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). :fire: +- 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:
@@ -95,6 +84,19 @@ It is the first embedding model which supports all three retrieval methods, achi
+ +BGE (BAAI General Embedding) focuses on retrieval-augmented LLMs, consisting of the following projects currently: + +![projects](./imgs/projects.png) + +- **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: