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:
- 02e6cd7cda60e865f3905e986c883fe8a93fd2900f5a0b2d714cb38c705696d6
- Size of remote file:
- 5.3 kB
- SHA256:
- 025b0f108ba0720b61745449e628b8539ac5f7848442b3c82252a22dffd52b37
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