Model Details
Full Model IDXenova/bart-large-mnli
Pipeline / Taskzero-shot-classification
Librarytransformers.js
Downloads (all-time)55.5K
Likes5
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/bart-large-mnli")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformers.jsonnxbarttext-classificationzero-shot-classificationbase_model:facebook/bart-large-mnlibase_model:quantized:facebook/bart-large-mnliregion:us
More zero-shot-classification Models
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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: 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
⬇ Downloads55.5K
❤️ Community Likes5
🛠️ 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.