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text_shortening_model_v61
This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7370
- Rouge1: 0.6559
- Rouge2: 0.469
- Rougel: 0.6075
- Rougelsum: 0.6079
- Bert precision: 0.9075
- Bert recall: 0.9017
- Bert f1-score: 0.9041
- Average word count: 7.9152
- Max word count: 15
- Min word count: 3
- Average token count: 12.1741
- % shortened texts with length > 12: 6.6964
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: 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bert precision | Bert recall | Bert f1-score | Average word count | Max word count | Min word count | Average token count | % shortened texts with length > 12 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.2731 | 1.0 | 49 | 1.3305 | 0.3966 | 0.2328 | 0.3397 | 0.3396 | 0.7258 | 0.7385 | 0.7316 | 9.3438 | 19 | 0 | 16.3929 | 28.5714 |
| 1.3225 | 2.0 | 98 | 0.9829 | 0.6051 | 0.422 | 0.5558 | 0.5557 | 0.8863 | 0.879 | 0.8822 | 8.0491 | 17 | 0 | 12.6607 | 8.0357 |
| 1.0933 | 3.0 | 147 | 0.8678 | 0.6346 | 0.4487 | 0.5869 | 0.5875 | 0.9012 | 0.8928 | 0.8965 | 7.8527 | 15 | 0 | 12.1607 | 5.8036 |
| 0.9836 | 4.0 | 196 | 0.8145 | 0.6404 | 0.449 | 0.5911 | 0.5918 | 0.9034 | 0.8971 | 0.8997 | 8.0179 | 15 | 3 | 12.1964 | 8.4821 |
| 0.9182 | 5.0 | 245 | 0.7860 | 0.647 | 0.4598 | 0.597 | 0.5974 | 0.9055 | 0.8989 | 0.9017 | 7.8884 | 15 | 3 | 12.1116 | 7.1429 |
| 0.8756 | 6.0 | 294 | 0.7659 | 0.6479 | 0.4606 | 0.5999 | 0.5996 | 0.9054 | 0.8982 | 0.9013 | 7.8839 | 15 | 3 | 12.1205 | 7.1429 |
| 0.84 | 7.0 | 343 | 0.7517 | 0.6544 | 0.4688 | 0.6062 | 0.6061 | 0.9067 | 0.9008 | 0.9033 | 7.9196 | 15 | 3 | 12.1741 | 7.1429 |
| 0.8256 | 8.0 | 392 | 0.7424 | 0.6515 | 0.4644 | 0.6033 | 0.6033 | 0.9068 | 0.9001 | 0.903 | 7.8705 | 15 | 3 | 12.1473 | 6.25 |
| 0.8198 | 9.0 | 441 | 0.7386 | 0.656 | 0.469 | 0.6076 | 0.608 | 0.9076 | 0.9017 | 0.9041 | 7.9107 | 15 | 3 | 12.1696 | 6.6964 |
| 0.8058 | 10.0 | 490 | 0.7370 | 0.6559 | 0.469 | 0.6075 | 0.6079 | 0.9075 | 0.9017 | 0.9041 | 7.9152 | 15 | 3 | 12.1741 | 6.6964 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
