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 pytorch_model.bin from GEOcite/DocumentSegmentationModel: direct link, hf CLI and curl.
- Browser
- Download file 1.48 GB
-
https://huggingface.co/GEOcite/DocumentSegmentationModel/resolve/5bc73442cfa797e7c1f029432a67ed64b8c23dfd/pytorch_model.bin
- Command line
-
hf download hf://GEOcite/DocumentSegmentationModel@5bc73442cfa797e7c1f029432a67ed64b8c23dfd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/GEOcite/DocumentSegmentationModel/resolve/5bc73442cfa797e7c1f029432a67ed64b8c23dfd/pytorch_model.bin
1.48 GB
- Xet hash:
- 8532a114fca546facc92c21957938372d38b0d98a8511c929ade9ea0734634b5
- Size of remote file:
- 1.48 GB
- SHA256:
- 18986506185ee3b7d25bfc3e0956189c380fe639729f61266c5dcad0b81c1b2f
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