Instructions to use SDS23/text_highlighting_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SDS23/text_highlighting_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SDS23/text_highlighting_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("SDS23/text_highlighting_model") model = AutoModelForTokenClassification.from_pretrained("SDS23/text_highlighting_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from SDS23/text_highlighting_model: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/SDS23/text_highlighting_model/resolve/main/training_args.bin
- Command line
-
hf download hf://SDS23/text_highlighting_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/SDS23/text_highlighting_model/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 157cd2de4a0878d8cca4f147e6a99c2dd8d7d6d76d807854ff71811c3ab66e13
- Size of remote file:
- 5.37 kB
- SHA256:
- bb148fdf966b6c4c455f076a454b321a897e965c411265b3eb635f70c0e7f056
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