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DanSumT5-base-finetuned-test_6887-finetuned-test_1006-finetuned-test_11009

Text Generation
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PyTorch
mt5
text2text-generation
Generated from Trainer
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DanSumT5-base-finetuned-test_6887-finetuned-test_1006-finetuned-test_11009 / README.md
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metadata
license: apache-2.0
base_model: emilstabil/DanSumT5-base-finetuned-test_6887-finetuned-test_1006
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: DanSumT5-base-finetuned-test_6887-finetuned-test_1006-finetuned-test_11009
    results: []

DanSumT5-base-finetuned-test_6887-finetuned-test_1006-finetuned-test_11009

This model is a fine-tuned version of emilstabil/DanSumT5-base-finetuned-test_6887-finetuned-test_1006 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3782
  • Rouge1: 32.39
  • Rouge2: 8.6259
  • Rougel: 18.9711
  • Rougelsum: 29.8246
  • Gen Len: 126.34

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: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 15

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
No log 1.0 100 2.3679 32.1169 8.3429 18.77 29.6623 126.64
No log 2.0 200 2.3731 32.3698 8.6912 18.9051 29.7509 126.33
No log 3.0 300 2.3613 31.6641 8.1301 18.0445 29.15 126.93
No log 4.0 400 2.3572 32.2198 8.4769 18.4906 29.7567 126.98
2.0202 5.0 500 2.3665 32.3042 8.3662 18.508 29.4379 126.47
2.0202 6.0 600 2.3637 32.1451 8.7682 18.8803 29.6716 126.0
2.0202 7.0 700 2.3640 32.1651 8.509 18.7387 29.588 125.97
2.0202 8.0 800 2.3667 32.0836 8.5881 18.7982 29.7275 126.21
2.0202 9.0 900 2.3733 32.0533 8.4997 18.6971 29.4086 125.88
1.864 10.0 1000 2.3741 31.7214 8.226 18.3299 29.3011 125.79
1.864 11.0 1100 2.3723 32.1068 8.5369 18.7853 29.4877 126.67
1.864 12.0 1200 2.3784 32.6049 8.8493 19.2296 30.1329 126.99
1.864 13.0 1300 2.3745 32.3626 8.6869 19.0018 29.7956 126.42
1.864 14.0 1400 2.3771 32.8879 8.8559 18.9569 30.255 126.02
1.7909 15.0 1500 2.3782 32.39 8.6259 18.9711 29.8246 126.34

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.0
  • Datasets 2.12.0
  • Tokenizers 0.13.3