bge-reranker-large

We have updated the new reranker, supporting larger lengths, more languages, and achieving better performance.** FlagEmbedding Model List | FAQ | Usage | Evaluation | Train | Citation | License More details please refer to our Github: FlagEmbedding.** English | 中文 FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently: **Long-Context LLM**: Activation Beacon **Fine-tuning of LM** : LM-Cocktail **Embedding Model**: Visualized-BGE, BGE-M3, LLM Embedder, BGE Embedding **Reranker Model**: llm rerankers, BGE Reranker **Benchmark**: C-MTEB News 3/18/2024: Release new rerankers, built upon powerful M3 and LLM (GEMMA and MiniCPM, not so large actually) backbones, supporitng multi-lingual processing and larger inputs, massive improvements of ranking performances on BEIR, C-MTEB/Retrieval, MIRACL, LlamaIndex Evaluation. 3/18/2024: Release Visualized-BGE, equipping BGE with visual capabilities. Visualized-BGE can be utilized to generate embeddings for hybrid image-text data. 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 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 11/23/2023: Release LM-Cocktail, a method to maintain general capabilities during fine-tuning by merging multiple language models. Technical Report :fire: 10/12/202