Model Details
Full Model IDMoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c
Pipeline / Tasktext-classification
Librarytransformers
Downloads (all-time)1.0K
Likes12
Last Modified4/5/2023
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("text-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-docnli-ling-2c")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerspytorchsafetensorsdeberta-v2text-classificationzero-shot-classificationenarxiv:2104.07179arxiv:2106.09449arxiv:2006.03654arxiv:2111.09543license:mitendpoints_compatibledeploy:azureregion:us
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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: Text Classification
This model is designed for the Text Classification task. Explore more models for this use case.
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⬇ Downloads1.0K
❤️ Community Likes12
🛠️ 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.