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:
- 3b9193316c4cf55e4c28c5870a3a5a1033d378dba4c3695875db93945c68d962
- Size of remote file:
- 436 kB
- SHA256:
- 146c64a03c446cc7c5a59b4d72edd099b526e1f4c9e777a1cbed7f6dd410a3b6
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