Token Classification
Transformers
Safetensors
English
roberta
social-science
computational-social-science
clause-segmentation
discourse-modes
isaac
disco
Eval Results (legacy)
Instructions to use ISAAC-corpus/isaac-generalization-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ISAAC-corpus/isaac-generalization-segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ISAAC-corpus/isaac-generalization-segmentation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ISAAC-corpus/isaac-generalization-segmentation") model = AutoModelForTokenClassification.from_pretrained("ISAAC-corpus/isaac-generalization-segmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ISAAC-corpus/isaac-generalization-segmentation: direct link, hf CLI and curl.
- Browser
- Download file 3.39 kB
-
https://huggingface.co/ISAAC-corpus/isaac-generalization-segmentation/resolve/main/training_args.bin
- Command line
-
hf download hf://ISAAC-corpus/isaac-generalization-segmentation/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ISAAC-corpus/isaac-generalization-segmentation/resolve/main/training_args.bin
3.39 kB
- Xet hash:
- fcf5985745121dcd3198340b925ff5fc873f4f00cb6f69c83819485384e05308
- Size of remote file:
- 3.39 kB
- SHA256:
- af2ab12a1d9c72d6fedfd51aae32ee599bdfe2a9f7e1fb85470f0eda6ebec841
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