Instructions to use DayCardoso/modernbert-base-multi-head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DayCardoso/modernbert-base-multi-head with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, ModernBERTForMultiLabelMultiHead tokenizer = AutoTokenizer.from_pretrained("DayCardoso/modernbert-base-multi-head") model = ModernBERTForMultiLabelMultiHead.from_pretrained("DayCardoso/modernbert-base-multi-head", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from DayCardoso/modernbert-base-multi-head: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/DayCardoso/modernbert-base-multi-head/resolve/main/training_args.bin
- Command line
-
hf download hf://DayCardoso/modernbert-base-multi-head/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/DayCardoso/modernbert-base-multi-head/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- fca61d5a7a7f99d45d3e0e9c6d5eeb71303eda6b48e88cb0e12c5c2158202efe
- Size of remote file:
- 5.43 kB
- SHA256:
- c02e137d51221d3963771c77113cb2d01f421398c807078f284d8547e859e342
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.