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:
- 2c6bbc8a10730ed773ada35f7792b62df9487baebcdb81a4a1b62c388db0a3f2
- Size of remote file:
- 8.83 kB
- SHA256:
- beb45c1e4eaf6e767e3db8e184a976f840afc72988e303455f2755be3273abe5
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