Instructions to use benchaffe/Bert-RAdam-XL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use benchaffe/Bert-RAdam-XL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="benchaffe/Bert-RAdam-XL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("benchaffe/Bert-RAdam-XL") model = AutoModelForTokenClassification.from_pretrained("benchaffe/Bert-RAdam-XL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from benchaffe/Bert-RAdam-XL: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/benchaffe/Bert-RAdam-XL/resolve/main/training_args.bin
- Command line
-
hf download hf://benchaffe/Bert-RAdam-XL/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/benchaffe/Bert-RAdam-XL/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- de90308541d854dd50613133e7ef4309f838eae65f5612af1cd7c8e57b5cb5ab
- Size of remote file:
- 5.24 kB
- SHA256:
- 5cac3986e35c2213bd6fe55e9edd9fd0b8ac1890c8427cc7fc6455f73f5b6af0
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