Zero-Shot Classification
Transformers
PyTorch
English
deberta
text-classification
deberta-v1
deberta-mnli
Instructions to use Narsil/deberta-large-mnli-zero-cls with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Narsil/deberta-large-mnli-zero-cls with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Narsil/deberta-large-mnli-zero-cls")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Narsil/deberta-large-mnli-zero-cls") model = AutoModelForSequenceClassification.from_pretrained("Narsil/deberta-large-mnli-zero-cls") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e40e5d5a25cc3305ebb66126114a7751c8a8e320e45603afbf06d7e5695eb7f
- Size of remote file:
- 1.62 GB
- SHA256:
- 3abd875c9e6dd137a689a1fa1a433f0c2d6bc7462afc42a0095878f88f23be87
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