Instructions to use pabloOmega/ocr-layout with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pabloOmega/ocr-layout with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="pabloOmega/ocr-layout", device_map="auto")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("pabloOmega/ocr-layout") model = AutoModelForTokenClassification.from_pretrained("pabloOmega/ocr-layout", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8e83dff0ff50a2195fa57b1bcfa47704df60eab80c3c2c023b1d231376424604
- Size of remote file:
- 501 MB
- SHA256:
- 4e827ae33221bfe78182ceb7407458604fca96ecc419d0218c92a481dd75563c
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