Instructions to use beomus/layoutxlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use beomus/layoutxlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="beomus/layoutxlm")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("beomus/layoutxlm") model = AutoModelForTokenClassification.from_pretrained("beomus/layoutxlm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ff9e389cda8108ab245b11e2f07a5c0daf8fe775cb15ff58aae37f50e03c9616
- Size of remote file:
- 1.48 GB
- SHA256:
- 8f18956a963845cb93658266b9f41895f110f93e31669a7c10b9c7beed240448
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.