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results

This model is a fine-tuned version of t5-small on the multi_news dataset. It achieves the following results on the evaluation set:

  • Loss: 2.9028
  • Rouge1: 37.3599
  • Rouge2: 12.1820
  • Rougel: 21.4068
  • Rougelsum: 21.3827
  • Gen Len: 141.366

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: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 313 3.0888 33.8257 10.0913 19.3859 19.3966 131.264
3.487 2.0 626 3.0216 36.0141 11.1691 20.4601 20.4538 138.12
3.487 3.0 939 2.9906 36.2470 11.3578 20.6635 20.6692 138.632
3.2354 4.0 1252 2.9727 36.7252 11.5422 20.9492 20.9458 139.433
3.1863 5.0 1565 2.9586 36.6970 11.6533 20.9281 20.9236 140.189
3.1863 6.0 1878 2.9511 36.8584 11.7427 21.1395 21.1377 140.747
3.1624 7.0 2191 2.9441 36.9490 11.8362 21.2388 21.2508 140.994
3.1462 8.0 2504 2.9406 37.0855 11.8388 21.2447 21.2583 141.331
3.1462 9.0 2817 2.9383 37.0757 11.8588 21.2306 21.2472 140.901
3.1409 10.0 3130 2.9376 37.1450 11.9259 21.3013 21.3147 141.081

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 2.21.0
  • Tokenizers 0.19.1
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Dataset used to train Procit004/T5_For_Text_Summarization

Evaluation results