Instructions to use microsoft/git-base-textvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/git-base-textvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="microsoft/git-base-textvqa")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("microsoft/git-base-textvqa") model = AutoModelForImageTextToText.from_pretrained("microsoft/git-base-textvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec0e6564039d0fc2e1fad65320c4bb4f68acf8b708613d0e13524d8939481e48
- Size of remote file:
- 709 MB
- SHA256:
- 0247e8691306d4cba0b9a57a0cf1d4cb09981fa768377ef69bfb294a26ace29f
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