Instructions to use universalner/uner_dan_ddt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalner/uner_dan_ddt with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="universalner/uner_dan_ddt")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("universalner/uner_dan_ddt") model = AutoModelForTokenClassification.from_pretrained("universalner/uner_dan_ddt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from universalner/uner_dan_ddt: direct link, hf CLI and curl.
- Browser
- Download file 4.02 kB
-
https://huggingface.co/universalner/uner_dan_ddt/resolve/main/training_args.bin
- Command line
-
hf download hf://universalner/uner_dan_ddt/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/universalner/uner_dan_ddt/resolve/main/training_args.bin
4.02 kB
- Xet hash:
- 9ef3b6cc9aa029473afe0ff4d71d6ee8aa304a1dbde732182d23bc4c49381f4a
- Size of remote file:
- 4.02 kB
- SHA256:
- 474d55d44611d5f773518ff11aa15915bb400ab63ba99a6b4247f3e56f1902ab
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