Model Details
Full Model IDXenova/all-MiniLM-L6-v2
Pipeline / Taskfeature-extraction
Librarytransformers.js
Downloads (all-time)3.7M
Likes120
Last Modified7/22/2025
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("feature-extraction", model="Xenova/all-MiniLM-L6-v2")
# Run inference
result = pipe("Your input here")
print(result)����� Tags
transformers.jsonnxbertfeature-extractionbase_model:sentence-transformers/all-MiniLM-L6-v2base_model:quantized:sentence-transformers/all-MiniLM-L6-v2license:apache-2.0region:us
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Open on Hugging Face →Browse Model Files ↗�� Browse All Models🤖 Task: feature-extraction
This model is designed for the feature-extraction task. Explore more models for this use case.
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⬇ Downloads3.7M
����� Community Likes120
🛠�� 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.