Instructions to use Mikhail1313/layoutlmv2-base-uncased_finetuned_docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mikhail1313/layoutlmv2-base-uncased_finetuned_docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="Mikhail1313/layoutlmv2-base-uncased_finetuned_docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("Mikhail1313/layoutlmv2-base-uncased_finetuned_docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("Mikhail1313/layoutlmv2-base-uncased_finetuned_docvqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7e2ae2038c7a3515fa02baa5124eee71842d5b2894b3918e1f5e8db49fb1dad6
- Size of remote file:
- 802 MB
- SHA256:
- b6e9e9b307b1e14e67e5945e49de99bee3df97814084e324515ab65aec7af580
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.