gte-reranker-modernbert

About

What gte-reranker-modernbert is

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 Usage [!TIP] For transformers and sentence-transformers, if your GPU supports it, the efficient Flash Attention 2 will be used automatically if you have flashattn installed. It is not mandatory. bash pip install flashattn 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 difference being that the pre-training language model base has been replaced from GTE-MLM to ModernBert. For more training details, please refer to our paper: mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval Evaluation MTEB The results of other models are retrieved from MTEB leaderboard. Given that all models in the gte-modernbert series have a size of less than 1B parameters, we focused exclusively on the results of models under 1B from the MTEB leaderboard. LoCo (Long Document Retrieval) COIR (Code Retrieval Task) BEIR Hiring We have open positions for Research Interns and Full-Time Researchers to join our team at Tongyi Lab. We are seeking passionate in

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys gte-reranker-modernbert for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (gte-reranker-modernbert below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"gte-reranker-modernbert","input":"text to embed"}'

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