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it5-efficient-small-el32-ilgiornale-to-repubblica

Text2Text Generation
Transformers
PyTorch
TensorFlow
JAX
TensorBoard
Italian
t5
italian
sequence-to-sequence
newspaper
efficient
ilgiornale
repubblica
style-transfer
Eval Results
text-generation-inference
Model card Files Metrics Community
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it5-efficient-small-el32-ilgiornale-to-repubblica / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
datasets:
  - it5/datasets
metrics:
  - rouge
model-index:
  - name: it5-efficient-small-el32-st_g2r-0.0003
    results:
      - task:
          name: Summarization
          type: summarization
        dataset:
          name: it5/datasets st_g2r
          type: it5/datasets
          args: st_g2r
        metrics:
          - name: Rouge1
            type: rouge
            value: 29.8455

it5-efficient-small-el32-st_g2r-0.0003

This model is a fine-tuned version of stefan-it/it5-efficient-small-el32 on the it5/datasets st_g2r dataset. It achieves the following results on the evaluation set:

  • Loss: 2.6892
  • Rouge1: 29.8455
  • Rouge2: 11.735
  • Rougel: 26.6048
  • Rougelsum: 26.8553
  • Gen Len: 14.6131

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: 0.0003
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10.0

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
3.2179 0.74 5000 2.7813 25.8006 9.3551 23.386 23.5287 13.5337
2.9248 1.49 10000 2.6914 27.0409 10.0228 24.4581 24.6197 13.243
2.6813 2.23 15000 2.6462 27.5333 10.3641 24.8696 25.0564 14.3052
2.691 2.98 20000 2.6205 28.3681 10.8961 25.5144 25.722 14.5279
2.5127 3.72 25000 2.6043 28.5979 11.0477 25.759 25.9605 14.0721
2.3331 4.47 30000 2.6283 28.9106 11.3727 25.9338 26.1387 14.4519
2.2034 5.21 35000 2.6400 29.099 11.2376 26.1221 26.3568 13.8715
2.2137 5.96 40000 2.6340 29.2641 11.3565 26.2012 26.4214 14.5981
2.1104 6.7 45000 2.6362 29.6204 11.6807 26.5976 26.8261 13.888
2.003 7.45 50000 2.6541 29.5679 11.6334 26.5095 26.7418 14.2246
1.8955 8.19 55000 2.6940 29.6748 11.5897 26.4862 26.7581 14.3902
1.912 8.94 60000 2.6883 29.7285 11.6448 26.5368 26.7806 14.3574
1.8581 9.68 65000 2.6874 29.7373 11.6532 26.4799 26.738 14.3821

Framework versions

  • Transformers 4.15.0
  • Pytorch 1.10.0+cu102
  • Datasets 1.17.0
  • Tokenizers 0.10.3