Instructions to use Master-AI-Lab/EnergyBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Master-AI-Lab/EnergyBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Master-AI-Lab/EnergyBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Master-AI-Lab/EnergyBERT") model = AutoModelForMaskedLM.from_pretrained("Master-AI-Lab/EnergyBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 627f6a1a2f37b9255ea6c789cfb103b7d85816e2fc85a974e1fb32aa4a5e6095
- Size of remote file:
- 438 MB
- SHA256:
- 634dc4f076623e34706c34c7980750e2d5f7d0acd1d6260790ec11d9118a8eca
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.