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WafaaFraih/
vit-gpt2-medical-optimized2

Name: vit-gpt2-medical-optimized2
Availability: InStock

transformers

safetensors

vision-encoder-decoder

image-to-text

generated_from_trainer

nlpconnect/vit-gpt2-image-captioning

finetune

apache-2.0

endpoints_compatible

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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

WafaaFraih/vit-gpt2-medical-optimized2

vit-gpt2-medical-optimized2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

WafaaFraih/
vit-gpt2-medical-optimized2