Model Details
Full Model IDXenova/ms-marco-MiniLM-L-6-v2
Pipeline / Tasktext-classification
Librarytransformers.js
Downloads (all-time)890.2K
Likes9
Last Modified6/30/2025
Author / OrgXenova
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("text-classification", model="Xenova/ms-marco-MiniLM-L-6-v2")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformers.jsonnxberttext-classificationbase_model:cross-encoder/ms-marco-MiniLM-L6-v2base_model:quantized:cross-encoder/ms-marco-MiniLM-L6-v2region:us
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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 Classification
This model is designed for the Text Classification task. Explore more models for this use case.
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⬇ Downloads890.2K
❤️ Community Likes9
🛠️ Requirements
- →Install: pip install transformers.js
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.