Instructions to use Sharka/CIVQA_LayoutLMv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sharka/CIVQA_LayoutLMv2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="Sharka/CIVQA_LayoutLMv2")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("Sharka/CIVQA_LayoutLMv2") model = AutoModelForDocumentQuestionAnswering.from_pretrained("Sharka/CIVQA_LayoutLMv2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 45b9b7b8c062f771357aa7b8045e2c93ab3a9cb69452faa00bf590b2c301ca9a
- Size of remote file:
- 5.77 MB
- SHA256:
- 229c60ccb6d9279863c5548ab836d784759720048bdf71a285d35797a44f18b3
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