gte-reranker-modernbert-base

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. Model Overview Developed by: Tongyi Lab, Alibaba Group Model Type: Text reranker Primary Language: English Model Size: 149M Max Input Length: 8192 tokens Model list | Models | Language | Model Type | Model Size | Max Seq. Length | Dimension | MTEB-en | BEIR | LoCo | CoIR | |:--------------------------------------------------------------------------------------:|:--------:|:----------------------:|:----------:|:---------------:|:---------:|:-------:|:----:|:----:|:----:| | `gte-modernbert-base` | English | text embedding | 149M | 8192 | 768 | 64.38 | 55.33 | 87.57 | 79.31 | | `gte-reranker-modernbert-base` | English | text reranker | 149M | 8192 | - | - | 56.19 | 90.68 | 79.99 | Usage [!TIP] For `transformers` and `sentence-transformers`, if your GPU supports it, the efficient Flash Attention 2 will be used automatically if you have `flash_attn` installed. It is not mandatory. Use with `transformers` Use with `sentence-transformers`: Before you start, install the sentence-transformers libraries: Use with `transformers.js` Additionally, you can also deploy `Alibaba-NLP/gte-reranker-modernbert-base` with Text Embeddings Inference (TEI) as follows: CPU GPU Then you can send requests to the deployed API via the `/rerank` route (see the Text Embeddings Inference OpenAPI Specification for more details): Training Details The `gte-modernbert` series of models follows the training scheme of the previous GTE models, with the only d