bart-pt-asqa-ob / README.md
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metadata
license: mit
tags:
  - generated_from_trainer
model-index:
  - name: bart-pt-asqa-ob
    results: []

bart-pt-asqa-ob

This model is a fine-tuned version of vblagoje/bart_lfqa on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 2.4268
  • Rougelsum: 24.2407

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: 1e-05
  • 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: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Rougelsum
No log 1.0 355 1.6269 19.0683
1.6035 2.0 710 1.6400 19.8336
1.3505 3.0 1065 1.6525 20.9906
1.3505 4.0 1420 1.7070 21.5381
1.1756 5.0 1775 1.7348 22.6130
1.0148 6.0 2130 1.8440 22.8553
1.0148 7.0 2485 1.8460 23.1281
0.8886 8.0 2840 1.9321 23.4357
0.7687 9.0 3195 2.0124 23.3538
0.6779 10.0 3550 2.0809 23.7958
0.6779 11.0 3905 2.1312 23.5703
0.5933 12.0 4260 2.2144 24.0672
0.5283 13.0 4615 2.2463 23.9667
0.5283 14.0 4970 2.3022 24.0211
0.4885 15.0 5325 2.3010 24.2634
0.4379 16.0 5680 2.3311 24.2333
0.4085 17.0 6035 2.4048 24.2417
0.4085 18.0 6390 2.4118 24.2201
0.3821 19.0 6745 2.4237 24.2905
0.3699 20.0 7100 2.4268 24.2407

Framework versions

  • Transformers 4.23.0.dev0
  • Pytorch 1.12.1+cu102
  • Datasets 2.4.0
  • Tokenizers 0.12.1