Model Details
Full Model IDsileod/deberta-v3-base-tasksource-nli
Pipeline / Taskzero-shot-classification
Librarytransformers
Downloads (all-time)119.0K
Likes133
Last Modified8/13/2024
Author / Orgsileod
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="sileod/deberta-v3-base-tasksource-nli")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerspytorchsafetensorsdeberta-v2text-classificationdeberta-v3-basedeberta-v3debertanlinatural-language-inferencemultitaskmulti-taskpipelineextreme-multi-taskextreme-mtltasksourcezero-shotrlhfzero-shot-classificationendataset:gluedataset:nyu-mll/multi_nlidataset:multi_nlidataset:super_gluedataset:anlidataset:tasksource/babi_nlidataset:sickdataset:snlidataset:scitaildataset:OpenAssistant/oasst1
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
⬇ Downloads119.0K
❤️ Community Likes133
🛠️ 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.