Instructions to use CambridgeMolecularEngineering/photocatalysisbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CambridgeMolecularEngineering/photocatalysisbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CambridgeMolecularEngineering/photocatalysisbert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CambridgeMolecularEngineering/photocatalysisbert") model = AutoModelForMaskedLM.from_pretrained("CambridgeMolecularEngineering/photocatalysisbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21ca9d10d89a5ccff7bab94e0665fbbca7ab52d3fbd2a0ef466d8b4dfac566f7
- Size of remote file:
- 438 MB
- SHA256:
- 7df55897a38cf818d86199a39d009676b7e4ebdddd92de7fbc9470104a4d0dd9
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