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")# pip install -U transformers accelerate # 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
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This model is pretrained on a corpus of papers on photcatalysis. For more detailed training procedures, see
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"How beneficial is pre-training on a narrow domain-specific corpus for information extraction about photocatalytic water splitting?" by Taketomo Isazawa and Jacqueline M. Cole.
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# photocatalysisbert
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This model is pretrained on a corpus of papers on photcatalysis. For more detailed training procedures, see
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"How beneficial is pre-training on a narrow domain-specific corpus for information extraction about photocatalytic water splitting?" by Taketomo Isazawa and Jacqueline M. Cole.
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