How to use from the
Use from the
Transformers library
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("PuppetLover/image_captioning_model", dtype="auto")
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image_captioning_model

This model is a fine-tuned version of facebook/mbart-large-50 on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 3.9561
  • Bleu4: 4.6602

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • 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
  • lr_scheduler_warmup_steps: 350
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Bleu4
12.4864 1.0 113 12.2093 0.9329
5.5024 2.0 226 5.1222 1.4283
4.8103 3.0 339 4.5379 2.3729
4.4941 4.0 452 4.2987 3.4861
4.2601 5.0 565 4.1685 4.2715
4.0996 6.0 678 4.1062 4.5653
4.0649 7.0 791 4.0509 4.4991
3.9869 8.0 904 4.0288 4.2192
3.8730 9.0 1017 3.9991 4.2306
3.7958 10.0 1130 3.9973 4.1558
3.7842 11.0 1243 3.9754 5.1463
3.7156 12.0 1356 3.9678 4.0594
3.6771 13.0 1469 3.9686 4.5274
3.6874 14.0 1582 3.9671 4.4852
3.6374 15.0 1695 3.9548 4.309
3.5811 16.0 1808 3.9561 4.6602

Framework versions

  • Transformers 5.0.0
  • Pytorch 2.11.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2
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