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:
- a1e334c7efb19c1b9becd9a1cebf702688515f3d50063975ee5fe38bffb37ccf
- Size of remote file:
- 755 Bytes
- SHA256:
- 45b3b580146c214c76fec277ac721b2de0f1f9a5f0c8096dad13f39340d15da1
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