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
root
data cleaning, blast, and splitting code with source data, also deleting unnecessary files
6efd653 - Xet hash:
- 9ffb61b6a9d82aa53e558f44c1bda8a96c5cd23cb0631a63b5e41030b7df2760
- Size of remote file:
- 819 Bytes
- SHA256:
- 5802d962e638004536d5473ccb7d5f3150a481d1295e1abf9fed55fe28311aea
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.