Instructions to use TusharGoel/LayoutLMv2-finetuned-docvqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TusharGoel/LayoutLMv2-finetuned-docvqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("document-question-answering", model="TusharGoel/LayoutLMv2-finetuned-docvqa")# Load model directly from transformers import AutoProcessor, AutoModelForDocumentQuestionAnswering processor = AutoProcessor.from_pretrained("TusharGoel/LayoutLMv2-finetuned-docvqa") model = AutoModelForDocumentQuestionAnswering.from_pretrained("TusharGoel/LayoutLMv2-finetuned-docvqa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 713f20c419a2577cedd8c47057ec07990e7a36c8a30955d630e7e980992f0264
- Size of remote file:
- 802 MB
- SHA256:
- 61dce98ddcb0c697fe833dcb50f8391e69d23a8b8705c10d67ef3457a25c3e59
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.