Instructions to use StKirill/layoutlmv2-base-uncased_finetuned_docvqa_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use StKirill/layoutlmv2-base-uncased_finetuned_docvqa_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="StKirill/layoutlmv2-base-uncased_finetuned_docvqa_v2")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("StKirill/layoutlmv2-base-uncased_finetuned_docvqa_v2") model = AutoModelForDocumentQuestionAnswering.from_pretrained("StKirill/layoutlmv2-base-uncased_finetuned_docvqa_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96bcfecf6c3efb6d190ceffaecd2f992cd6fff4c1e62862c2839e6525e90ddde
- Size of remote file:
- 802 MB
- SHA256:
- 353e24f44d6a52cb198936d34eb692de2cc2728401c109ae70b4a87c0a0239fd
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