Instructions to use vikp/pdf_postprocessor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/pdf_postprocessor with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vikp/pdf_postprocessor")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vikp/pdf_postprocessor") model = AutoModelForTokenClassification.from_pretrained("vikp/pdf_postprocessor") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e4e56534a3c37d891ba629cee8bf53f469f5e4d9f58c094c713e83088e52f56
- Size of remote file:
- 1.12 GB
- SHA256:
- 3aeb135e663382ce0d773b86dc8c57e1c0efe40bf9b650114af8ee6994ec2e5b
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