Instructions to use GEOcite/DocumentSegmentationModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GEOcite/DocumentSegmentationModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="GEOcite/DocumentSegmentationModel")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("GEOcite/DocumentSegmentationModel") model = AutoModelForTokenClassification.from_pretrained("GEOcite/DocumentSegmentationModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from GEOcite/DocumentSegmentationModel: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/GEOcite/DocumentSegmentationModel/resolve/5bc73442cfa797e7c1f029432a67ed64b8c23dfd/training_args.bin
- Command line
-
hf download hf://GEOcite/DocumentSegmentationModel@5bc73442cfa797e7c1f029432a67ed64b8c23dfd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/GEOcite/DocumentSegmentationModel/resolve/5bc73442cfa797e7c1f029432a67ed64b8c23dfd/training_args.bin
3.39 kB
- Xet hash:
- 92f37ce1587c75f424985df79e2a646abcec5fd6178e8423998e2d2d0883b11d
- Size of remote file:
- 3.39 kB
- SHA256:
- c71ff2ada507147ca6290265ee3e9dbbb3bbc28e014e9c973d8af8afc117f780
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