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:
- 4810e78d0e293a6c9bda3332b2a071d53402167225ec8633a974a29c8f11a17b
- Size of remote file:
- 32.2 MB
- SHA256:
- 9590919f2c09430dc6f8b617b8a738f7e174fb04fdae1f35ceaa0351ea05612f
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