Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v3-large
nli
natural-language-inference
multitask
multi-task
pipeline
extreme-multi-task
extreme-mtl
tasksource
zero-shot
rlhf
Instructions to use sileod/deberta-v3-large-tasksource-nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sileod/deberta-v3-large-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-large-tasksource-nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sileod/deberta-v3-large-tasksource-nli") model = AutoModelForSequenceClassification.from_pretrained("sileod/deberta-v3-large-tasksource-nli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 951ded4efcbcff4052c9900dfa18449b27872a6d6897893e02e66a23e9031d73
- Size of remote file:
- 1.74 GB
- SHA256:
- 4f13d15a0f69363c22218bc974a0cb5efea92a30b719aeb4cfb45e9eedf28191
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