Instructions to use benchaffe/Bert-RAdam-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benchaffe/Bert-RAdam-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="benchaffe/Bert-RAdam-Large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("benchaffe/Bert-RAdam-Large") model = AutoModelForTokenClassification.from_pretrained("benchaffe/Bert-RAdam-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from benchaffe/Bert-RAdam-Large: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/benchaffe/Bert-RAdam-Large/resolve/main/training_args.bin
- Command line
-
hf download hf://benchaffe/Bert-RAdam-Large/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/benchaffe/Bert-RAdam-Large/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 00e1546e78c8667e7048895d84230eeb9dc92e3f435c6795c619a2b8d7d685fc
- Size of remote file:
- 5.3 kB
- SHA256:
- df60415d449ff81c98398ac31f5b43e7a24f39f88ffbfde6f6a5affcf16b69ff
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