Instructions to use pardeepSF/layoutlm-vqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pardeepSF/layoutlm-vqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="pardeepSF/layoutlm-vqa")# Load model directly from transformers import AutoTokenizer, AutoModelForDocumentQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("pardeepSF/layoutlm-vqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("pardeepSF/layoutlm-vqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f43c1f9131cec45bfca0643e4fb2b13926fef9508ef939e946457fbbacf1502a
- Size of remote file:
- 1.58 kB
- SHA256:
- 9518cb20ba30d0bc101127dbdfff2574f450d60c3031967b86272a24d6ee0123
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