Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v3-base
deberta-v3
deberta
nli
natural-language-inference
multitask
multi-task
pipeline
extreme-multi-task
extreme-mtl
tasksource
zero-shot
rlhf
Eval Results (legacy)
Instructions to use sileod/deberta-v3-base-tasksource-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/deberta-v3-base-tasksource-nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="sileod/deberta-v3-base-tasksource-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/deberta-v3-base-tasksource-nli") model = AutoModelForSequenceClassification.from_pretrained("sileod/deberta-v3-base-tasksource-nli") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2d374ca0ff8caaee286aa2a967a573fc0aa129cf8634498c36acee105d5b161f
- Size of remote file:
- 738 MB
- SHA256:
- 7884ba2939b9cdcba1dbcd214c476fff61d9ed98e78223ad76ee08c832b5d0b2
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