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")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("benchaffe/Bert-RAdam-XL") model = AutoModelForTokenClassification.from_pretrained("benchaffe/Bert-RAdam-XL") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80b9e2fdf1be25296379e1145e6267b7c38afac6b496b6cdea704f6cd120ec63
- Size of remote file:
- 431 MB
- SHA256:
- 52bbac4428f8fab8e96f3670c95c36cfe0046532ecfedd5ac57180aff8df7e9e
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