Model Details
Full Model IDMoritzLaurer/mDeBERTa-v3-base-mnli-xnli
Pipeline / Taskzero-shot-classification
Librarytransformers
Downloads (all-time)241.9K
Likes304
Last Modified1/8/2024
Author / OrgMoritzLaurer
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="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerspytorchonnxsafetensorsdeberta-v2text-classificationzero-shot-classificationnlimultilingualenarbgdeelesfrhiruswthtrurvizhdataset:multi_nlidataset:xnliarxiv:2111.09543arxiv:1809.05053arxiv:1911.02116license:mit
More zero-shot-classification Models
See all →🚀 Use This Model
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
⬇ Downloads241.9K
❤️ Community Likes304
🛠️ 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.