<!-- 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_v75
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2113
- Bert precision: 0.8889
- Bert recall: 0.8883
- Bert f1-score: 0.8881
- Average word count: 6.8466
- Max word count: 15
- Min word count: 1
- Average token count: 10.892
- % shortened texts with length > 12: 1.9632
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: 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: 40
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.4857 | 1.0 | 30 | 1.9604 | 0.8298 | 0.8444 | 0.8359 | 9.1436 | 19 | 1 | 13.7337 | 14.2331 |
| 2.1772 | 2.0 | 60 | 1.7312 | 0.8337 | 0.839 | 0.8349 | 8.1264 | 19 | 1 | 12.3264 | 10.5521 |
| 1.9897 | 3.0 | 90 | 1.6036 | 0.8513 | 0.8528 | 0.8508 | 7.6528 | 19 | 1 | 11.8748 | 8.3436 |
| 1.8748 | 4.0 | 120 | 1.5274 | 0.8616 | 0.8583 | 0.8589 | 7.1988 | 17 | 1 | 11.4368 | 6.0123 |
| 1.7948 | 5.0 | 150 | 1.4678 | 0.8709 | 0.8669 | 0.868 | 7.0086 | 17 | 1 | 11.1914 | 4.4172 |
| 1.7436 | 6.0 | 180 | 1.4245 | 0.8763 | 0.8726 | 0.8737 | 6.9681 | 16 | 1 | 11.1387 | 3.8037 |
| 1.6914 | 7.0 | 210 | 1.3948 | 0.8808 | 0.8792 | 0.8793 | 6.9706 | 18 | 1 | 11.0773 | 3.9264 |
| 1.6484 | 8.0 | 240 | 1.3716 | 0.8846 | 0.8814 | 0.8824 | 6.789 | 15 | 2 | 10.8687 | 2.9448 |
| 1.6177 | 9.0 | 270 | 1.3534 | 0.8858 | 0.8827 | 0.8836 | 6.8294 | 16 | 2 | 10.8712 | 3.0675 |
| 1.6034 | 10.0 | 300 | 1.3371 | 0.8854 | 0.8826 | 0.8834 | 6.8528 | 16 | 2 | 10.865 | 2.9448 |
| 1.5696 | 11.0 | 330 | 1.3237 | 0.8863 | 0.8842 | 0.8847 | 6.8393 | 16 | 2 | 10.8577 | 2.6994 |
| 1.5474 | 12.0 | 360 | 1.3115 | 0.8874 | 0.8844 | 0.8853 | 6.7669 | 16 | 2 | 10.7742 | 2.5767 |
| 1.5354 | 13.0 | 390 | 1.3011 | 0.8867 | 0.8836 | 0.8846 | 6.7607 | 16 | 2 | 10.7644 | 2.3313 |
| 1.5173 | 14.0 | 420 | 1.2916 | 0.8872 | 0.8834 | 0.8847 | 6.7067 | 16 | 2 | 10.7117 | 2.0859 |
| 1.5061 | 15.0 | 450 | 1.2822 | 0.8873 | 0.8833 | 0.8848 | 6.6969 | 16 | 2 | 10.6945 | 1.9632 |
| 1.4861 | 16.0 | 480 | 1.2742 | 0.8882 | 0.8846 | 0.8858 | 6.692 | 16 | 2 | 10.7043 | 1.5951 |
| 1.4793 | 17.0 | 510 | 1.2673 | 0.8881 | 0.8848 | 0.8859 | 6.719 | 16 | 1 | 10.7325 | 1.9632 |
| 1.4736 | 18.0 | 540 | 1.2621 | 0.8888 | 0.8856 | 0.8867 | 6.7399 | 16 | 1 | 10.7571 | 1.9632 |
| 1.4592 | 19.0 | 570 | 1.2563 | 0.8889 | 0.8863 | 0.8871 | 6.7497 | 16 | 1 | 10.7755 | 1.9632 |
| 1.459 | 20.0 | 600 | 1.2514 | 0.8885 | 0.8863 | 0.8868 | 6.773 | 16 | 1 | 10.7902 | 1.9632 |
| 1.4446 | 21.0 | 630 | 1.2472 | 0.8883 | 0.8859 | 0.8865 | 6.7571 | 16 | 1 | 10.7546 | 1.8405 |
| 1.4324 | 22.0 | 660 | 1.2431 | 0.888 | 0.8864 | 0.8866 | 6.7779 | 16 | 1 | 10.7853 | 1.8405 |
| 1.431 | 23.0 | 690 | 1.2396 | 0.8881 | 0.8866 | 0.8868 | 6.7828 | 16 | 1 | 10.8098 | 1.8405 |
| 1.4233 | 24.0 | 720 | 1.2358 | 0.8885 | 0.8869 | 0.8872 | 6.784 | 16 | 1 | 10.8123 | 1.9632 |
| 1.4218 | 25.0 | 750 | 1.2322 | 0.8887 | 0.8874 | 0.8875 | 6.8135 | 16 | 1 | 10.8417 | 1.8405 |
| 1.4086 | 26.0 | 780 | 1.2295 | 0.8885 | 0.8878 | 0.8876 | 6.8356 | 16 | 1 | 10.8982 | 1.9632 |
| 1.4104 | 27.0 | 810 | 1.2267 | 0.8883 | 0.8877 | 0.8875 | 6.8491 | 16 | 1 | 10.9166 | 1.9632 |
| 1.4046 | 28.0 | 840 | 1.2242 | 0.888 | 0.8877 | 0.8873 | 6.8577 | 16 | 1 | 10.9411 | 1.9632 |
| 1.4034 | 29.0 | 870 | 1.2222 | 0.8882 | 0.8881 | 0.8876 | 6.8626 | 16 | 1 | 10.9436 | 1.9632 |
| 1.3942 | 30.0 | 900 | 1.2204 | 0.8883 | 0.8881 | 0.8877 | 6.8577 | 16 | 1 | 10.935 | 2.0859 |
| 1.3909 | 31.0 | 930 | 1.2182 | 0.8885 | 0.8881 | 0.8878 | 6.8368 | 15 | 1 | 10.908 | 1.8405 |
| 1.385 | 32.0 | 960 | 1.2167 | 0.8889 | 0.8884 | 0.8882 | 6.838 | 15 | 1 | 10.9006 | 1.8405 |
| 1.3833 | 33.0 | 990 | 1.2149 | 0.889 | 0.8884 | 0.8882 | 6.8368 | 15 | 1 | 10.8945 | 1.8405 |
| 1.3831 | 34.0 | 1020 | 1.2139 | 0.8891 | 0.8885 | 0.8883 | 6.8454 | 15 | 1 | 10.9018 | 1.8405 |
| 1.3811 | 35.0 | 1050 | 1.2129 | 0.8891 | 0.8884 | 0.8882 | 6.8356 | 15 | 1 | 10.8908 | 1.8405 |
| 1.3869 | 36.0 | 1080 | 1.2124 | 0.8891 | 0.8883 | 0.8881 | 6.8294 | 15 | 1 | 10.8785 | 1.8405 |
| 1.3696 | 37.0 | 1110 | 1.2120 | 0.889 | 0.8881 | 0.8881 | 6.8233 | 15 | 1 | 10.8663 | 1.8405 |
| 1.3791 | 38.0 | 1140 | 1.2116 | 0.8889 | 0.8881 | 0.888 | 6.8307 | 15 | 1 | 10.8748 | 1.8405 |
| 1.3755 | 39.0 | 1170 | 1.2113 | 0.8889 | 0.8881 | 0.888 | 6.8331 | 15 | 1 | 10.8773 | 1.8405 |
| 1.3668 | 40.0 | 1200 | 1.2113 | 0.8889 | 0.8883 | 0.8881 | 6.8466 | 15 | 1 | 10.892 | 1.9632 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
