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 model.safetensors from benchaffe/Bert-RAdam-XL: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/benchaffe/Bert-RAdam-XL/resolve/main/model.safetensors
- Command line
-
hf download hf://benchaffe/Bert-RAdam-XL/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/benchaffe/Bert-RAdam-XL/resolve/main/model.safetensors
431 MB
- Xet hash:
- 80b9e2fdf1be25296379e1145e6267b7c38afac6b496b6cdea704f6cd120ec63
- Size of remote file:
- 431 MB
- SHA256:
- 52bbac4428f8fab8e96f3670c95c36cfe0046532ecfedd5ac57180aff8df7e9e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.