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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vikp/pdf_postprocessor") model = AutoModelForTokenClassification.from_pretrained("vikp/pdf_postprocessor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from vikp/pdf_postprocessor: direct link, hf CLI and curl.
- Browser
- Download file 14.5 MB
-
https://huggingface.co/vikp/pdf_postprocessor/resolve/main/tokenizer.json
- Command line
-
hf download hf://vikp/pdf_postprocessor/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/vikp/pdf_postprocessor/resolve/main/tokenizer.json
14.5 MB
- Xet hash:
- 6c21e9666e897e46d97ee9f1fe6ad56639936c0ff441d032978a49bb0e0fd08a
- Size of remote file:
- 14.5 MB
- SHA256:
- 17a208233d2ee8d8c83b23bc214df737c44806a1919f444e89b31e586cd956ba
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.