Instructions to use CambridgeMolecularEngineering/bert-large-uncased-scmedium with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CambridgeMolecularEngineering/bert-large-uncased-scmedium with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CambridgeMolecularEngineering/bert-large-uncased-scmedium")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CambridgeMolecularEngineering/bert-large-uncased-scmedium") model = AutoModelForMaskedLM.from_pretrained("CambridgeMolecularEngineering/bert-large-uncased-scmedium") - Notebooks
- Google Colab
- Kaggle
Ctrl+K
- global_step4028
- 1.48 kB
- 666 Bytes
- 15 Bytes
- 1.34 GB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 14.6 kB xet
- 125 Bytes
- 711 kB
- 349 Bytes
- 13.7 kB
- 4.98 kB xet