Model Details
Full Model IDAlibaba-NLP/gte-multilingual-reranker-base
Pipeline / Tasktext-ranking
Librarysentence-transformers
Downloads (all-time)253.4K
Likes183
Last Modified7/5/2025
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-multilingual-reranker-base")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
sentence-transformerssafetensorsnewtext-classificationtransformerstext-embeddings-inferencetext-rankingcustom_codeafarazbebgbncacebcscydadeeleneseteufafifrglgu
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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
⬇ Downloads253.4K
❤️ Community Likes183
🛠️ Requirements
- →Install: pip install sentence-transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.