medical-caption-model-v1

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: 5.0189
• Bleu: 0.0332
• Meteor: 5.8196
• Rougel: 10.2378

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: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Bleu	Meteor	Rougel
5.1913	2.0	200	5.2321	0.0396	7.0115	10.6658
4.6615	4.0	400	4.9947	0.0495	6.1088	10.9570
4.3567	6.0	600	4.9261	0.0384	5.7674	11.0691
4.0884	8.0	800	4.9632	0.0344	5.9190	10.6889
3.9311	10.0	1000	5.0189	0.0332	5.8196	10.2378

Framework versions

• Transformers 4.56.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0