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gte-reranker-modernbert-base

We are excited to introduce the `gte-modernbert` series of models, which are built upon the latest modernBERT pre-trained encoder-only foundation models. The `gte-modernbert` series models include both text embedding models and rerank models. The `gte-modernbert` models demonstrates competitive performance in several text embedding and text retrieval evaluation tasks when compared to similar-scale models from the current open-source community. This includes assessments such as **MTEB**, **LoCO**, and **COIR** evaluation. - Developed by: Tongyi Lab, Alibaba Group - Model Type: Text reranker - Primary Language: English - Model Size: 149M - Max Input Length: 8192 tokens

We are excited to introduce the `gte-modernbert` series of models, which are built upon the latest modernBERT pre-trained encoder-only foundation models. The `gte-modernbert` series models include both text embedding models and rerank models. The `gte-modernbert` models demonstrates competitive performance in several text embedding and text retrieval evaluation tasks when compared to similar-scale models from the current open-source community. This includes assessments such as **MTEB**, **LoCO**, and **COIR** evaluation. - Developed by: Tongyi Lab, Alibaba Group - Model Type: Text reranker - Primary Language: English - Model Size: 149M - Max Input Length: 8192 tokens

Sources

huggingface.co ↗