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:
- b177e9a5b3e2d2de1b7f8bd7cba38a44c11e4f6e45407265b620dddf0ea835a4
- Size of remote file:
- 709 MB
- SHA256:
- d483a6bf9d687cdcafbf33638f446e1fd41063c6716cf9d8467942f8c3a1d5bf
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