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 pytorch_model.bin from vikp/pdf_postprocessor: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/vikp/pdf_postprocessor/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vikp/pdf_postprocessor/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vikp/pdf_postprocessor/resolve/main/pytorch_model.bin
1.12 GB
- Xet hash:
- 17dd7bdab6fb0d6d4beae44542111a3c35db6315d5dca357e51baad1207cd045
- Size of remote file:
- 1.12 GB
- SHA256:
- c488ef46c7cae9eb927814bcb658588ea483e4ed6f7be5606ab47ca6ac40f2b6
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