Instructions to use BigSalmon/FormalBerta3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BigSalmon/FormalBerta3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BigSalmon/FormalBerta3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/FormalBerta3") model = AutoModelForMaskedLM.from_pretrained("BigSalmon/FormalBerta3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from BigSalmon/FormalBerta3: direct link, hf CLI and curl.
- Browser
- Download file 2.86 kB
-
https://huggingface.co/BigSalmon/FormalBerta3/resolve/main/training_args.bin
- Command line
-
hf download hf://BigSalmon/FormalBerta3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/BigSalmon/FormalBerta3/resolve/main/training_args.bin
2.86 kB
- Xet hash:
- 11bfd523df0009fa5a3721ed83979975a57c340338c516da40165880e379a0e0
- Size of remote file:
- 2.86 kB
- SHA256:
- 0fc6c87b4f0295eff69144242ebda25279f9fa6b414bbd7bfb9de733936ff4e8
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