Model Details
Full Model IDAlibaba-NLP/gte-reranker-modernbert-base
Pipeline / Tasktext-ranking
Librarytransformers
Downloads (all-time)1.5M
Likes96
Last Modified7/4/2025
Author / OrgAlibaba-NLP
PrivateNo � public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("text-ranking", model="Alibaba-NLP/gte-reranker-modernbert-base")
# Run inference
result = pipe("Your input here")
print(result)����� Tags
transformersonnxsafetensorsmodernberttext-classificationsentence-transformerstransformers.jstext-embeddings-inferencetext-rankingenarxiv:2308.03281base_model:answerdotai/ModernBERT-basebase_model:finetune:answerdotai/ModernBERT-baselicense:apache-2.0endpoints_compatibledeploy:azureregion:us
More text-ranking Models
See all →🚀 Use This Model
Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗�� Browse All Models🤖 Task: text-ranking
This model is designed for the text-ranking task. Explore more models for this use case.
All text-ranking Models →📊 Popularity
⬇ Downloads1.5M
����� Community Likes96
🛠�� Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.