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text_shortening_model_v3
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4219
- Rouge1: 0.593
- Rouge2: 0.3643
- Rougel: 0.5423
- Rougelsum: 0.5412
- Bert precision: 0.8882
- Bert recall: 0.9022
- Average word count: 11.9
- Max word count: 17
- Min word count: 6
- Average token count: 17.2857
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Average word count | Max word count | Min word count | Average token count |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.6953 | 1.0 | 8 | 1.8235 | 0.5468 | 0.3281 | 0.4997 | 0.4987 | 0.8808 | 0.886 | 11.5786 | 18 | 6 | 16.8286 |
| 1.4749 | 2.0 | 16 | 1.6832 | 0.5482 | 0.3138 | 0.4936 | 0.4934 | 0.8776 | 0.8889 | 12.1429 | 18 | 5 | 17.2929 |
| 1.3967 | 3.0 | 24 | 1.6181 | 0.5653 | 0.3362 | 0.5121 | 0.512 | 0.8833 | 0.894 | 11.9143 | 18 | 5 | 17.0286 |
| 1.3533 | 4.0 | 32 | 1.5757 | 0.5631 | 0.338 | 0.5133 | 0.5133 | 0.8838 | 0.8948 | 11.8786 | 18 | 4 | 16.9929 |
| 1.3 | 5.0 | 40 | 1.5398 | 0.5748 | 0.3463 | 0.5256 | 0.525 | 0.8863 | 0.8977 | 11.95 | 18 | 4 | 16.9857 |
| 1.2528 | 6.0 | 48 | 1.5159 | 0.58 | 0.3475 | 0.5261 | 0.5247 | 0.8855 | 0.8988 | 11.9571 | 18 | 5 | 17.0429 |
| 1.2234 | 7.0 | 56 | 1.4974 | 0.5823 | 0.3515 | 0.5301 | 0.5289 | 0.8864 | 0.8993 | 11.8929 | 18 | 6 | 17.05 |
| 1.2024 | 8.0 | 64 | 1.4819 | 0.5846 | 0.3575 | 0.5326 | 0.5312 | 0.8876 | 0.9014 | 11.9143 | 18 | 6 | 17.1429 |
| 1.1665 | 9.0 | 72 | 1.4680 | 0.5881 | 0.3593 | 0.5367 | 0.5359 | 0.8877 | 0.9014 | 11.8571 | 17 | 6 | 17.1429 |
| 1.1589 | 10.0 | 80 | 1.4567 | 0.5873 | 0.359 | 0.5314 | 0.5305 | 0.8873 | 0.9004 | 11.7929 | 17 | 6 | 17.0429 |
| 1.1411 | 11.0 | 88 | 1.4501 | 0.5891 | 0.3627 | 0.5386 | 0.5373 | 0.8888 | 0.9017 | 11.85 | 17 | 6 | 17.1286 |
| 1.1188 | 12.0 | 96 | 1.4460 | 0.5911 | 0.364 | 0.5399 | 0.5391 | 0.8881 | 0.9024 | 11.95 | 17 | 6 | 17.2786 |
| 1.1061 | 13.0 | 104 | 1.4396 | 0.5908 | 0.3648 | 0.5395 | 0.5386 | 0.8881 | 0.9024 | 11.9071 | 17 | 6 | 17.3071 |
| 1.0939 | 14.0 | 112 | 1.4328 | 0.5904 | 0.3625 | 0.5392 | 0.5384 | 0.8876 | 0.9018 | 11.9071 | 17 | 6 | 17.3 |
| 1.0863 | 15.0 | 120 | 1.4305 | 0.5899 | 0.3633 | 0.5387 | 0.5379 | 0.8875 | 0.9015 | 11.8714 | 17 | 6 | 17.2714 |
| 1.0792 | 16.0 | 128 | 1.4286 | 0.5908 | 0.3636 | 0.5401 | 0.5392 | 0.8875 | 0.9018 | 11.8929 | 17 | 6 | 17.3 |
| 1.0871 | 17.0 | 136 | 1.4255 | 0.5908 | 0.3628 | 0.5401 | 0.5392 | 0.8878 | 0.9017 | 11.8714 | 17 | 6 | 17.2571 |
| 1.057 | 18.0 | 144 | 1.4229 | 0.5928 | 0.365 | 0.5427 | 0.5414 | 0.8886 | 0.9022 | 11.85 | 17 | 6 | 17.2357 |
| 1.0554 | 19.0 | 152 | 1.4221 | 0.593 | 0.3643 | 0.5423 | 0.5412 | 0.8882 | 0.9022 | 11.9 | 17 | 6 | 17.2857 |
| 1.06 | 20.0 | 160 | 1.4219 | 0.593 | 0.3643 | 0.5423 | 0.5412 | 0.8882 | 0.9022 | 11.9 | 17 | 6 | 17.2857 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
