Instructions to use clementWizard/layout-lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use clementWizard/layout-lm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="clementWizard/layout-lm")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("clementWizard/layout-lm") model = AutoModelForTokenClassification.from_pretrained("clementWizard/layout-lm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd3f03e30e9f295e348c60de13384da69e5f274e14b81f4cdb8fe5d4c6ba0f76
- Size of remote file:
- 451 MB
- SHA256:
- defb8a92fbb731a6f08b8822714ac46818dd9837904ceee64b4ee4a5d0098cc0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.