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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f0d6d5310bf3f737cb0b67f70d6769f998e47a5e43e79a3f2aba70d71306995d
- Size of remote file:
- 3.55 GB
- SHA256:
- 63a1f786762c51cfec4d1514e2a566964637358788a892dbd9592dc4123ac1cc
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