Instructions to use rhlprj/layoutlm-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rhlprj/layoutlm-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rhlprj/layoutlm-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("rhlprj/layoutlm-finetuned") model = AutoModelForTokenClassification.from_pretrained("rhlprj/layoutlm-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0838694e9815ad2301eeca8468a6590c6620823041c6ec4c6331c0fafff8ef93
- Size of remote file:
- 511 MB
- SHA256:
- 29dcb3adcc68f91eb6ff7898ccd639b370ec0a29d0b8836a7547f2498d8a0b87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.