Instructions to use yhavinga/t5-base-36L-dutch-english-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yhavinga/t5-base-36L-dutch-english-cased with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("yhavinga/t5-base-36L-dutch-english-cased") model = AutoModelForMultimodalLM.from_pretrained("yhavinga/t5-base-36L-dutch-english-cased") - Notebooks
- Google Colab
- Kaggle
| INFO:__main__: Optimizer = adafactor | |
| INFO:__main__: Learning rate (peak) = 0.009 | |
| INFO:__main__: Num examples = 94558172 | |
| INFO:__main__: Num tokenized group examples 109037136 | |
| INFO:__main__: Num Epochs = 1 | |
| INFO:__main__: Instantaneous batch size per device = 4 | |
| INFO:__main__: Total train batch size (w. parallel & grad accum) = 512 | |
| INFO:__main__: Steps per epoch = 212963 (x grad accum (16) = 3407408) | |
| INFO:__main__: Total optimization steps = 212963 (x grad accum (16) = 3407408) | |