Zero-Shot Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
text-classification
deberta-v3
deberta-v2`
deberta-mnli
Instructions to use NDugar/deberta-v2-xlarge-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NDugar/deberta-v2-xlarge-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="NDugar/deberta-v2-xlarge-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("NDugar/deberta-v2-xlarge-mnli") model = AutoModelForSequenceClassification.from_pretrained("NDugar/deberta-v2-xlarge-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e85daf9347d51f058d14bad895073d49b8887717ed4481a9d078bb9c704ab80c
- Size of remote file:
- 2.8 kB
- SHA256:
- 49fc87ca654f3e63fe7381a5b9e1d9f73d219885e1a7a116dbcf2a749c415f4b
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