Instructions to use brandaobrandisborges/layoutlm-synthchecking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brandaobrandisborges/layoutlm-synthchecking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="brandaobrandisborges/layoutlm-synthchecking")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("brandaobrandisborges/layoutlm-synthchecking") model = AutoModelForTokenClassification.from_pretrained("brandaobrandisborges/layoutlm-synthchecking") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6410850ffae523686da3356802a5ed630672910410625e52048f8c23da28d43b
- Size of remote file:
- 1.36 GB
- SHA256:
- 9b75b6919e85196758f4cfde9787122408458897b8c5255ddd8c6be097aa2a28
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.