Instructions to use dehannoor3199/data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dehannoor3199/data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="dehannoor3199/data")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("dehannoor3199/data") model = AutoModelForVisualQuestionAnswering.from_pretrained("dehannoor3199/data") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 488196a85af63a0640ea90bb098bc20cf869eeaf8736afd32b2f6ce08ced3a92
- Size of remote file:
- 4.86 kB
- SHA256:
- e5570fcde9971c0b50786899f2085b5da78d08419d5ad9d7401d364316de4012
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.