<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
text_shortening_model_v65
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1783
- Bert precision: 0.8964
- Bert recall: 0.8977
- Bert f1-score: 0.8966
- Average word count: 6.4565
- Max word count: 16
- Min word count: 2
- Average token count: 10.5686
- % shortened texts with length > 12: 2.002
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Bert precision | Bert recall | Bert f1-score | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.7747 | 1.0 | 146 | 1.3200 | 0.8806 | 0.8825 | 0.881 | 6.7818 | 18 | 2 | 10.6827 | 2.1021 |
| 1.3684 | 2.0 | 292 | 1.2106 | 0.8857 | 0.8858 | 0.8852 | 6.5335 | 18 | 2 | 10.4835 | 1.7017 |
| 1.2448 | 3.0 | 438 | 1.1635 | 0.8862 | 0.8883 | 0.8868 | 6.6246 | 18 | 1 | 10.6817 | 2.1021 |
| 1.1406 | 4.0 | 584 | 1.1386 | 0.8897 | 0.8923 | 0.8905 | 6.6697 | 18 | 2 | 10.6767 | 2.2022 |
| 1.0623 | 5.0 | 730 | 1.1373 | 0.889 | 0.893 | 0.8905 | 6.6897 | 18 | 2 | 10.7568 | 1.5015 |
| 1.0034 | 6.0 | 876 | 1.1111 | 0.8923 | 0.8953 | 0.8933 | 6.5876 | 18 | 2 | 10.6927 | 1.7017 |
| 0.9391 | 7.0 | 1022 | 1.1037 | 0.8927 | 0.8947 | 0.8932 | 6.5455 | 18 | 2 | 10.6196 | 1.3013 |
| 0.8868 | 8.0 | 1168 | 1.0997 | 0.8949 | 0.8959 | 0.895 | 6.4805 | 18 | 2 | 10.5836 | 1.4014 |
| 0.8443 | 9.0 | 1314 | 1.1011 | 0.8939 | 0.8965 | 0.8947 | 6.5626 | 18 | 2 | 10.6386 | 1.5015 |
| 0.8117 | 10.0 | 1460 | 1.0997 | 0.8957 | 0.8981 | 0.8965 | 6.4865 | 16 | 2 | 10.6066 | 1.001 |
| 0.7844 | 11.0 | 1606 | 1.1153 | 0.8976 | 0.8979 | 0.8973 | 6.4404 | 18 | 2 | 10.5345 | 1.5015 |
| 0.7593 | 12.0 | 1752 | 1.1126 | 0.8946 | 0.8988 | 0.8962 | 6.6356 | 18 | 2 | 10.7698 | 1.9019 |
| 0.7249 | 13.0 | 1898 | 1.1047 | 0.8968 | 0.8991 | 0.8975 | 6.5335 | 16 | 2 | 10.6396 | 1.4014 |
| 0.7048 | 14.0 | 2044 | 1.1127 | 0.8961 | 0.8984 | 0.8968 | 6.5275 | 16 | 2 | 10.6336 | 1.4014 |
| 0.6828 | 15.0 | 2190 | 1.1237 | 0.8965 | 0.8982 | 0.8969 | 6.4675 | 16 | 2 | 10.5906 | 1.7017 |
| 0.6558 | 16.0 | 2336 | 1.1221 | 0.8975 | 0.8972 | 0.8969 | 6.3634 | 16 | 1 | 10.4985 | 1.2012 |
| 0.6296 | 17.0 | 2482 | 1.1296 | 0.8962 | 0.8982 | 0.8968 | 6.4775 | 16 | 1 | 10.6496 | 1.9019 |
| 0.6304 | 18.0 | 2628 | 1.1334 | 0.8981 | 0.898 | 0.8976 | 6.3724 | 16 | 1 | 10.4755 | 1.6016 |
| 0.6124 | 19.0 | 2774 | 1.1463 | 0.898 | 0.9006 | 0.8989 | 6.5075 | 15 | 2 | 10.6246 | 1.5015 |
| 0.6001 | 20.0 | 2920 | 1.1547 | 0.8982 | 0.8997 | 0.8984 | 6.4925 | 16 | 2 | 10.5766 | 1.9019 |
| 0.5834 | 21.0 | 3066 | 1.1551 | 0.8972 | 0.8973 | 0.8967 | 6.3323 | 16 | 2 | 10.4705 | 1.7017 |
| 0.5707 | 22.0 | 3212 | 1.1687 | 0.897 | 0.899 | 0.8976 | 6.4665 | 16 | 2 | 10.6026 | 1.7017 |
| 0.5667 | 23.0 | 3358 | 1.1656 | 0.8965 | 0.8981 | 0.8968 | 6.4585 | 16 | 2 | 10.5726 | 2.002 |
| 0.5519 | 24.0 | 3504 | 1.1747 | 0.8968 | 0.8984 | 0.8971 | 6.4885 | 16 | 2 | 10.5616 | 2.1021 |
| 0.5538 | 25.0 | 3650 | 1.1754 | 0.8967 | 0.8983 | 0.897 | 6.4735 | 16 | 2 | 10.5676 | 2.002 |
| 0.5403 | 26.0 | 3796 | 1.1734 | 0.8968 | 0.8983 | 0.8971 | 6.4835 | 16 | 2 | 10.6036 | 1.9019 |
| 0.5371 | 27.0 | 3942 | 1.1735 | 0.8964 | 0.8982 | 0.8968 | 6.4865 | 16 | 2 | 10.5696 | 2.1021 |
| 0.5381 | 28.0 | 4088 | 1.1767 | 0.8968 | 0.8982 | 0.897 | 6.4735 | 16 | 2 | 10.5926 | 1.9019 |
| 0.5278 | 29.0 | 4234 | 1.1771 | 0.8966 | 0.8975 | 0.8966 | 6.4454 | 16 | 2 | 10.5556 | 2.002 |
| 0.5249 | 30.0 | 4380 | 1.1783 | 0.8964 | 0.8977 | 0.8966 | 6.4565 | 16 | 2 | 10.5686 | 2.002 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
