How to use from the
Use from the
Transformers library
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("PuppetLover/scenegraph_image_captioning")
model = AutoModelForSeq2SeqLM.from_pretrained("PuppetLover/scenegraph_image_captioning")
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scenegraph_image_captioning

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

  • Loss: 3.3994
  • Bleu4: 10.148

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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
3.6494 1.0 4625 3.6095 8.3269
3.4809 2.0 9250 3.4953 9.0764
3.2703 3.0 13875 3.4439 9.185
3.3438 4.0 18500 3.4134 10.023
3.2965 5.0 23125 3.3911 10.0925
3.2711 6.0 27750 3.3830 10.4298
3.1469 7.0 32375 3.3745 10.5282
3.1113 8.0 37000 3.3760 10.292
3.0507 9.0 41625 3.3739 10.7014
3.0794 10.0 46250 3.3767 10.4073
3.0509 11.0 50875 3.3793 10.3126
3.0570 12.0 55500 3.3880 10.2934
2.9785 13.0 60125 3.3865 10.2243
2.9636 14.0 64750 3.3994 10.148

Framework versions

  • Transformers 5.10.2
  • Pytorch 2.11.0+cu128
  • Datasets 4.0.0
  • Tokenizers 0.22.2
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