Model Details
Full Model IDfacebook/bart-large-mnli
Pipeline / Taskzero-shot-classification
Librarytransformers
Downloads (all-time)3.1M
Likes1.6K
Last Modified9/5/2023
Author / Orgfacebook
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="facebook/bart-large-mnli")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerspytorchjaxrustsafetensorsbarttext-classificationzero-shot-classificationdataset:multi_nliarxiv:1910.13461arxiv:1909.00161license:mitendpoints_compatibledeploy:sagemakerdeploy:azureregion: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
⬇ Downloads3.1M
❤️ Community Likes1.6K
🛠️ Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.