Instructions to use aiface/vit5-base_nli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aiface/vit5-base_nli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aiface/vit5-base_nli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aiface/vit5-base_nli") model = AutoModelForSequenceClassification.from_pretrained("aiface/vit5-base_nli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cb3659722fda5b72e45a6b7db74c16896148b33584bb72c367c974ba6cfdbfd0
- Size of remote file:
- 5.91 kB
- SHA256:
- 92458ce979c8825f59cb9edd757937c8834eacf0cd1dab89d741a06492f7135a
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