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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8573a2a1791e3daacad32f41eb2c6869c3b29962f4d36112bd5aee0972dab478
- Size of remote file:
- 504 MB
- SHA256:
- 4fee33de540afd7a5ead04b84f97fc27e020f1f43c58ca4371f16b8babbe9d1a
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