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 pytorch_model.bin from BigSalmon/FormalBerta3: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/BigSalmon/FormalBerta3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://BigSalmon/FormalBerta3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/BigSalmon/FormalBerta3/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- bacb6d9813919ec738f1ce7a25963ca5175b90b76ad74294cf22beb59b01ba63
- Size of remote file:
- 499 MB
- SHA256:
- 61398bffc94aeb0803229a77da029c26709fc25e87df353705f26197860498ae
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