Model Details
Full Model IDcross-encoder/stsb-roberta-base
Pipeline / Tasktext-ranking
Librarysentence-transformers
Downloads (all-time)171.7K
Likes5
Last Modified4/11/2025
Author / Orgcross-encoder
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="cross-encoder/stsb-roberta-base")
# Run inference
result = pipe("Your input here")
print(result)����� Tags
sentence-transformerspytorchjaxonnxsafetensorsopenvinorobertatext-classificationtransformerstext-rankingendataset:sentence-transformers/stsbbase_model:FacebookAI/roberta-basebase_model:quantized:FacebookAI/roberta-baselicense:apache-2.0text-embeddings-inferenceendpoints_compatibledeploy:azureregion:us
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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.
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⬇ Downloads171.7K
����� Community Likes5
🛠�� 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.