Instructions to use vikp/pdf_postprocessor_t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/pdf_postprocessor_t5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vikp/pdf_postprocessor_t5")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vikp/pdf_postprocessor_t5") model = AutoModelForTokenClassification.from_pretrained("vikp/pdf_postprocessor_t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| Postprocess markdown generated from a pdf to clean up newlines, spaces, etc. | |
| Used in [marker](https://github.com/VikParuchuri/marker). |