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 model.safetensors from benchaffe/Bert-RAdam-Large: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/benchaffe/Bert-RAdam-Large/resolve/main/model.safetensors
- Command line
-
hf download hf://benchaffe/Bert-RAdam-Large/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/benchaffe/Bert-RAdam-Large/resolve/main/model.safetensors
431 MB
- Xet hash:
- fc397224cbe549ef90352fdaaaeb8381c996992e6b41d1c6ad67233ef4dd3f6f
- Size of remote file:
- 431 MB
- SHA256:
- 1b190a48d4182595f261883e3d416825e20f639f7d2c972b3d7185b0c475c695
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