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DanSumT5-baseV_91333

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mt5
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metadata
license: apache-2.0
base_model: Danish-summarisation/DanSumT5-base
tags:
  - generated_from_trainer
metrics:
  - rouge
model-index:
  - name: DanSumT5-base-finetuned-test_6887
    results: []

DanSumT5-base-finetuned-test_6887

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

  • Loss: 2.5277
  • Rouge1: 31.3188
  • Rouge2: 7.8236
  • Rougel: 17.8296
  • Rougelsum: 28.6162
  • Gen Len: 127.0

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: 2e-05
  • train_batch_size: 3
  • eval_batch_size: 3
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 12
  • 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 0.99 66 2.7401 29.9221 6.3861 16.5877 27.0611 125.09
No log 1.99 133 2.6815 30.6874 6.9413 16.9609 27.8851 125.98
No log 3.0 200 2.6440 31.2045 7.4012 17.7421 28.3497 126.63
No log 4.0 267 2.6199 31.3329 7.4574 17.8549 28.643 126.98
No log 4.99 333 2.5984 31.5184 7.7763 17.9153 29.0627 127.0
No log 5.99 400 2.5822 31.8839 7.9755 18.0572 29.2282 126.65
No log 7.0 467 2.5677 31.5939 7.9515 17.865 29.2019 126.45
2.8684 8.0 534 2.5587 31.4931 7.6042 17.6853 28.8366 126.79
2.8684 8.99 600 2.5496 31.105 7.6714 17.5128 28.5242 126.78
2.8684 9.99 667 2.5423 31.6087 8.0358 17.9956 28.9514 126.78
2.8684 11.0 734 2.5364 31.411 7.9534 17.895 28.7595 127.0
2.8684 12.0 801 2.5326 31.4648 7.9777 17.9589 28.8168 127.0
2.8684 12.99 867 2.5296 31.374 7.8341 17.8341 28.8146 127.0
2.8684 13.99 934 2.5278 31.2822 7.7789 17.7983 28.5903 127.0
2.8684 14.83 990 2.5277 31.3188 7.8236 17.8296 28.6162 127.0

Framework versions

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