Instructions to use dandelin/vilt-b32-finetuned-vqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dandelin/vilt-b32-finetuned-vqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="dandelin/vilt-b32-finetuned-vqa")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dandelin/vilt-b32-finetuned-vqa", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b84af9e9a0d724aad9114e232bd474bd9ebd5d54375dc910901c95f9604f525
- Size of remote file:
- 470 MB
- SHA256:
- 4d5f3409947b0369487ece7c5868f0040ceb67d25735dbb4ac5e99e03bab3a19
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