bge-small-en

More details please refer to our Github: FlagEmbedding.

If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using bge-m3.

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

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

News

• 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 :fire:

• 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.

• 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.

Model List

bge is short for BAAI general embedding.