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")# pip install -U transformers accelerate # 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
Download tokenizer.json from rubentito/layoutlmv3-base-mpdocvqa: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/rubentito/layoutlmv3-base-mpdocvqa/resolve/main/tokenizer.json
- Command line
-
hf download hf://rubentito/layoutlmv3-base-mpdocvqa/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/rubentito/layoutlmv3-base-mpdocvqa/resolve/main/tokenizer.json
2.11 MB
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