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text_shortening_model_v34
This model is a fine-tuned version of facebook/bart-large-xsum on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.7697
- Rouge1: 0.4731
- Rouge2: 0.253
- Rougel: 0.4166
- Rougelsum: 0.416
- Bert precision: 0.8697
- Bert recall: 0.8697
- Average word count: 8.7087
- Max word count: 17
- Min word count: 5
- Average token count: 16.3093
- % shortened texts with length > 12: 6.6066
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: 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 | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.4675 | 1.0 | 19 | 3.1777 | 0.4029 | 0.1769 | 0.3503 | 0.3498 | 0.8509 | 0.857 | 9.6577 | 17 | 5 | 15.4324 | 10.2102 |
| 1.1669 | 2.0 | 38 | 1.9224 | 0.4506 | 0.2396 | 0.4184 | 0.4181 | 0.864 | 0.8688 | 8.6306 | 15 | 5 | 14.2613 | 4.2042 |
| 0.9292 | 3.0 | 57 | 1.7461 | 0.4654 | 0.2556 | 0.4186 | 0.419 | 0.8654 | 0.8722 | 9.0751 | 17 | 5 | 14.9099 | 4.2042 |
| 0.7876 | 4.0 | 76 | 1.9057 | 0.4003 | 0.207 | 0.367 | 0.366 | 0.8539 | 0.8516 | 8.1021 | 13 | 5 | 16.2883 | 1.2012 |
| 0.5976 | 5.0 | 95 | 1.7603 | 0.4776 | 0.2636 | 0.4254 | 0.4248 | 0.8659 | 0.8754 | 9.1952 | 16 | 5 | 15.0961 | 6.006 |
| 0.469 | 6.0 | 114 | 2.1107 | 0.4675 | 0.2542 | 0.4077 | 0.4081 | 0.856 | 0.8776 | 11.1802 | 20 | 5 | 18.4505 | 31.5315 |
| 0.4291 | 7.0 | 133 | 1.7980 | 0.4701 | 0.2509 | 0.4202 | 0.4195 | 0.8647 | 0.8723 | 9.1832 | 15 | 5 | 14.7267 | 6.3063 |
| 0.3673 | 8.0 | 152 | 1.9170 | 0.4669 | 0.2574 | 0.4188 | 0.4187 | 0.8678 | 0.8698 | 8.6306 | 18 | 5 | 14.3093 | 3.9039 |
| 0.3432 | 9.0 | 171 | 2.0268 | 0.4804 | 0.2691 | 0.4254 | 0.4249 | 0.8682 | 0.8753 | 9.2402 | 18 | 5 | 14.6847 | 9.3093 |
| 0.3094 | 10.0 | 190 | 2.1107 | 0.4809 | 0.2724 | 0.4353 | 0.4337 | 0.8689 | 0.8739 | 9.2883 | 17 | 4 | 16.2162 | 9.009 |
| 0.4402 | 11.0 | 209 | 2.2507 | 0.4816 | 0.268 | 0.428 | 0.4278 | 0.8668 | 0.8743 | 9.4805 | 18 | 4 | 16.6126 | 10.8108 |
| 0.3691 | 12.0 | 228 | 2.1652 | 0.4784 | 0.2637 | 0.4286 | 0.4277 | 0.8683 | 0.8714 | 8.7988 | 15 | 5 | 14.5105 | 6.006 |
| 0.1853 | 13.0 | 247 | 2.3660 | 0.4705 | 0.259 | 0.4119 | 0.4115 | 0.8686 | 0.8695 | 8.7898 | 17 | 5 | 16.2432 | 6.6066 |
| 0.3186 | 14.0 | 266 | 2.3237 | 0.4817 | 0.27 | 0.4273 | 0.4271 | 0.8698 | 0.8738 | 8.973 | 17 | 5 | 16.5976 | 9.3093 |
| 0.1745 | 15.0 | 285 | 2.2675 | 0.4672 | 0.2577 | 0.4177 | 0.4165 | 0.8698 | 0.8694 | 8.6066 | 16 | 5 | 14.7117 | 3.9039 |
| 0.1304 | 16.0 | 304 | 2.5157 | 0.4726 | 0.253 | 0.418 | 0.4167 | 0.8691 | 0.8688 | 8.6517 | 17 | 4 | 15.8468 | 3.9039 |
| 0.1432 | 17.0 | 323 | 2.4798 | 0.4744 | 0.2614 | 0.4204 | 0.4196 | 0.869 | 0.8725 | 8.9189 | 17 | 5 | 15.5015 | 6.006 |
| 0.1116 | 18.0 | 342 | 2.5924 | 0.4772 | 0.2589 | 0.4222 | 0.4221 | 0.87 | 0.8717 | 8.7508 | 17 | 5 | 15.6096 | 6.9069 |
| 0.0921 | 19.0 | 361 | 2.6547 | 0.4733 | 0.2541 | 0.4205 | 0.4199 | 0.8694 | 0.8694 | 8.6787 | 16 | 5 | 15.4204 | 6.006 |
| 0.0679 | 20.0 | 380 | 2.7697 | 0.4731 | 0.253 | 0.4166 | 0.416 | 0.8697 | 0.8697 | 8.7087 | 17 | 5 | 16.3093 | 6.6066 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
