Document Question Answering
Transformers
PyTorch
English
layoutlmv3
DocVQA
Document Question Answering
Document Visual Question Answering
Instructions to use rubentito/layoutlmv3-base-mpdocvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rubentito/layoutlmv3-base-mpdocvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="rubentito/layoutlmv3-base-mpdocvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("rubentito/layoutlmv3-base-mpdocvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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base_model: microsoft/layoutlmv3-base
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license:
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tags:
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- DocVQA
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- Document Question Answering
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base_model: microsoft/layoutlmv3-base
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license: cc-by-nc-sa-4.0
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tags:
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- DocVQA
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- Document Question Answering
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