clinical-t5
This is a finetuned T5-small model from Google, a checkpoint with 60 million parameters, for clinical note summarization. It was finetuned with the augmented-clinical-notes dataset, available in the Hugging Face.
Intended uses & limitations
The model was created for learning purposes. Hence, although being briefly evaluated in this notebook, it should be further refined.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Framework versions
- Transformers 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.13.3