vit-gpt2-medical-optimized2
This model is a fine-tuned version of nlpconnect/vit-gpt2-image-captioning on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 4.8438
- Bleu: 4.3587
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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 6
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|---|---|---|---|---|
| 5.4819 | 0.9412 | 250 | 5.3769 | 0.0 |
| 5.0593 | 1.8809 | 500 | 5.0407 | 2.0007 |
| 4.6768 | 2.8207 | 750 | 4.9012 | 1.0126 |
| 4.4763 | 3.7605 | 1000 | 4.8490 | 1.0126 |
| 4.2886 | 4.7002 | 1250 | 4.8331 | 1.0126 |
| 4.1603 | 5.64 | 1500 | 4.8438 | 4.3587 |
Framework versions
- Transformers 4.56.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0