Instructions to use ChatterjeeLab/FusOn-pLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChatterjeeLab/FusOn-pLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ChatterjeeLab/FusOn-pLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ChatterjeeLab/FusOn-pLM") model = AutoModelForMaskedLM.from_pretrained("ChatterjeeLab/FusOn-pLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3db8ac51ad008fa229493a9eb308304a5ba65b940777e9052e0cf0d8b23d270e
- Size of remote file:
- 44.9 MB
- SHA256:
- 1be258917ebd87f7cf71223e6fa340e7b6228464c42ae0b630c24efea8d2bd14
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.