Instructions to use microsoft/git-large-textvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/git-large-textvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="microsoft/git-large-textvqa")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("microsoft/git-large-textvqa") model = AutoModelForImageTextToText.from_pretrained("microsoft/git-large-textvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ba486f2a1c561480aabc79c2ad921347d70d759dc751477fb97609b69ef334c
- Size of remote file:
- 1.58 GB
- SHA256:
- 84cd56d1a6da7c53f2364e786c7f7578604112378b42c9e4aafba7bf5336075c
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