long_t5_test / README.md
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metadata
license: apache-2.0
base_model: google/long-t5-tglobal-base
tags:
  - generated_from_trainer
model-index:
  - name: long_t5_test
    results: []

long_t5_test

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

  • Loss: 1.3506
  • Rouge Rouge1: 0.4697
  • Rouge Rouge2: 0.1989
  • Rouge Rougel: 0.274
  • Rouge Rougelsum: 0.2736
  • Gen Len: 388.0152

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: 2
  • eval_batch_size: 2
  • 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 Rouge Rouge1 Rouge Rouge2 Rouge Rougel Rouge Rougelsum Gen Len
No log 1.0 394 1.9389 0.0284 0.0089 0.0167 0.0165 30.2273
3.6937 2.0 788 1.4702 0.4261 0.1598 0.254 0.2539 399.0
1.8772 3.0 1182 1.4362 0.4397 0.1699 0.2592 0.2591 398.5152
1.7418 4.0 1576 1.4204 0.4434 0.1779 0.2627 0.2628 397.7374
1.7418 5.0 1970 1.4108 0.4474 0.181 0.2631 0.263 394.798
1.6623 6.0 2364 1.3932 0.4546 0.1873 0.2675 0.2673 391.8586
1.6449 7.0 2758 1.3872 0.4559 0.1882 0.2665 0.2664 393.4848
1.5757 8.0 3152 1.3814 0.458 0.1906 0.2692 0.2692 397.1061
1.5527 9.0 3546 1.3718 0.4607 0.1912 0.2705 0.2706 391.7222
1.5527 10.0 3940 1.3703 0.4649 0.194 0.2717 0.2719 393.8788
1.5302 11.0 4334 1.3621 0.4664 0.197 0.2726 0.2724 386.2071
1.5142 12.0 4728 1.3537 0.4694 0.1977 0.2731 0.2731 388.9798
1.4721 13.0 5122 1.3528 0.4652 0.1961 0.2716 0.2714 390.2828
1.4745 14.0 5516 1.3550 0.4708 0.2009 0.2742 0.2739 393.8131
1.4745 15.0 5910 1.3500 0.471 0.199 0.2742 0.2741 385.4192
1.4799 16.0 6304 1.3505 0.4725 0.2008 0.2764 0.2761 387.6364
1.4558 17.0 6698 1.3535 0.4743 0.2032 0.2765 0.2764 389.4192
1.4426 18.0 7092 1.3494 0.4743 0.2042 0.278 0.2776 386.4394
1.4426 19.0 7486 1.3513 0.4719 0.2019 0.2753 0.2752 388.6515
1.4411 20.0 7880 1.3506 0.4697 0.1989 0.274 0.2736 388.0152

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.1.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.15.1