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:
- ae9c758c7f5ae05c6cbb3c398d29b4173bfcb1ad5d0391466d76d68a92e5ccee
- Size of remote file:
- 19.3 kB
- SHA256:
- 4b4fae3b8ab9661500ac34dc5e96069f76118f79ceac8e101d5361fe5e46d4b4
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