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:
- 4008d5cf184efd9b6a8680b00841600162db9dd60450d7ee3f6a79d8b64b8651
- Size of remote file:
- 54.5 kB
- SHA256:
- 5fd936ad5daed767e3815dac6315d92abcabb4fe506c169e6a958031a4fd2d97
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