Instructions to use brandaobrandisborges/layoutlm-synth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brandaobrandisborges/layoutlm-synth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="brandaobrandisborges/layoutlm-synth")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("brandaobrandisborges/layoutlm-synth") model = AutoModelForTokenClassification.from_pretrained("brandaobrandisborges/layoutlm-synth") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 103203e2a5c835c843af94dfd661ef2a1a78c7c8e6191d65541521468fe3bf8c
- Size of remote file:
- 3.58 kB
- SHA256:
- f1ebec62277c945a33d283dfac961c93169798237d04e5322b46c94396338e2f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.