Instructions to use PeanutCoding/Layouttest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PeanutCoding/Layouttest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="PeanutCoding/Layouttest")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("PeanutCoding/Layouttest") model = AutoModelForTokenClassification.from_pretrained("PeanutCoding/Layouttest") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a8522b9803184b5a4108041eb2e7973906eed57572a2f03338ac30b1a14b37e
- Size of remote file:
- 5.78 kB
- SHA256:
- 0be0ae88320f55956e053b783b0b6a4d0ff33503d76387508abbf86324a28181
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