Model Details
Full Model IDXenova/distilbert-base-uncased-mnli
Pipeline / Taskzero-shot-classification
Librarytransformers.js
Downloads (all-time)41.4K
Likes3
Last Modified7/11/2025
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("zero-shot-classification", model="Xenova/distilbert-base-uncased-mnli")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformers.jsonnxdistilberttext-classificationzero-shot-classificationbase_model:typeform/distilbert-base-uncased-mnlibase_model:quantized:typeform/distilbert-base-uncased-mnliregion:us
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Open on Hugging Face →Browse Model Files ↗← Browse All Models🤖 Task: zero-shot-classification
This model is designed for the zero-shot-classification task. Explore more models for this use case.
All zero-shot-classification Models →📊 Popularity
⬇ Downloads41.4K
❤️ Community Likes3
🛠️ 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.