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flan-t5-large-invoice

This model is a fine-tuned version of google/flan-t5-large on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.7345
  • Rouge1: 20.6872
  • Rouge2: 8.404
  • Rougel: 16.4285
  • Rougelsum: 18.8189
  • Gen Len: 19.0

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: 1
  • eval_batch_size: 1
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
1.779 1.0 933 1.3862 19.5863 7.7096 15.7946 18.3055 19.0
1.3799 2.0 1866 1.3274 19.6876 6.7441 15.804 17.8499 19.0
1.2505 3.0 2799 1.3099 19.8912 7.0276 16.3826 17.8505 19.0
1.1726 4.0 3732 1.3136 20.9896 8.5214 16.7944 19.2926 19.0
1.0985 5.0 4665 1.3205 20.5499 7.9224 16.3301 18.519 19.0
1.0439 6.0 5598 1.3297 20.8897 7.6685 16.6002 19.1149 19.0
0.9912 7.0 6531 1.3515 21.0205 7.9648 16.6663 19.1507 19.0
0.922 8.0 7464 1.3819 20.0508 7.0989 16.2521 18.2977 19.0
0.8729 9.0 8397 1.4222 20.3315 8.1219 16.5236 18.8624 19.0
0.8353 10.0 9330 1.4372 19.3277 7.4633 15.5985 17.6354 19.0
0.7928 11.0 10263 1.4851 20.1063 7.9988 16.0191 18.3571 19.0
0.7561 12.0 11196 1.5084 20.6322 7.2781 16.3066 18.8342 19.0
0.7251 13.0 12129 1.5437 21.4814 8.1047 17.0497 19.8034 19.0
0.692 14.0 13062 1.5802 21.1577 8.0934 16.738 19.2418 19.0
0.668 15.0 13995 1.6349 19.8489 7.7261 15.8122 18.0971 19.0
0.6237 16.0 14928 1.6246 20.8915 8.2484 16.8627 19.0533 19.0
0.6166 17.0 15861 1.6662 20.3532 8.2927 16.2535 18.5981 19.0
0.5973 18.0 16794 1.6963 20.4374 7.9296 16.2445 18.4927 19.0
0.581 19.0 17727 1.7168 20.6517 8.3175 16.4803 18.8024 19.0
0.5704 20.0 18660 1.7345 20.6872 8.404 16.4285 18.8189 19.0

Framework versions

  • Transformers 4.43.2
  • Pytorch 2.2.0a0+81ea7a4
  • Datasets 2.20.0
  • Tokenizers 0.19.1
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