Model Details
Full Model IDcross-encoder/mmarco-mMiniLMv2-L12-H384-v1
Pipeline / Tasktext-ranking
Librarysentence-transformers
Downloads (all-time)658.8K
Likes72
Last Modified4/15/2025
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/mmarco-mMiniLMv2-L12-H384-v1")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
sentence-transformerspytorchonnxsafetensorsopenvinoxlm-robertatext-classificationtransformerstext-rankingenarzhnlfrdehiinitjaptruesvimultilingualdataset:unicamp-dl/mmarcobase_model:nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Largebase_model:quantized:nreimers/mMiniLMv2-L12-H384-distilled-from-XLMR-Largelicense:apache-2.0text-embeddings-inferenceendpoints_compatible
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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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⬇ Downloads658.8K
❤️ Community Likes72
🛠️ 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.