Model Details
Full Model IDantoinelouis/crossencoder-camembert-base-mmarcoFR
Pipeline / Tasktext-ranking
Librarysentence-transformers
Downloads (all-time)199.7K
Likes7
Last Modified4/21/2025
Author / Organtoinelouis
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="antoinelouis/crossencoder-camembert-base-mmarcoFR")
# Run inference
result = pipe("Your input here")
print(result)����� Tags
sentence-transformerssafetensorscamembertpassage-rerankingtext-rankingfrdataset:unicamp-dl/mmarcobase_model:almanach/camembert-basebase_model:finetune:almanach/camembert-baselicense:mitmodel-indextext-embeddings-inferenceendpoints_compatibleregion:us
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This model is designed for the text-ranking task. Explore more models for this use case.
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⬇ Downloads199.7K
����� Community Likes7
🛠�� 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.