Instructions to use thaind/layoutlmv2-jaen-gemai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thaind/layoutlmv2-jaen-gemai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="thaind/layoutlmv2-jaen-gemai")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("thaind/layoutlmv2-jaen-gemai") model = AutoModelForTokenClassification.from_pretrained("thaind/layoutlmv2-jaen-gemai") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a2a3cd927b786326567e37098ec39270c36811137c6aa0f666fcf669ffee8e4e
- Size of remote file:
- 802 MB
- SHA256:
- 92b0a6a29a0ec61a1a7b4068e1c4b4340199b7fe072aad69e13d979c81501837
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