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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0037746b5d094ec80f4722a3b0ff0b86d7cbd7da2312b2aa9ec06f2818ec5fed
- Size of remote file:
- 4.73 kB
- SHA256:
- 5a96d618c2d8b90761a6a25bd4048f99f940729ddca028520d064c51e16b3110
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.