medical_jargons_simplifier
This model is a fine-tuned version of luqh/ClinicalT5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4405
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.2542 | 0.2017 | 500 | 0.5136 |
| 0.5482 | 0.4034 | 1000 | 0.4729 |
| 0.5112 | 0.6051 | 1500 | 0.4599 |
| 0.5042 | 0.8068 | 2000 | 0.4532 |
| 0.5043 | 1.0085 | 2500 | 0.4488 |
| 0.5085 | 1.2102 | 3000 | 0.4448 |
| 0.4692 | 1.4119 | 3500 | 0.4432 |
| 0.495 | 1.6136 | 4000 | 0.4416 |
| 0.4798 | 1.8152 | 4500 | 0.4405 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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Model tree for nadika/medical_jargons_simplifier
Base model
luqh/ClinicalT5-base