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text_shortening_model_v62
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2562
- Rouge1: 0.532
- Rouge2: 0.3351
- Rougel: 0.4834
- Rougelsum: 0.4837
- Bert precision: 0.8674
- Bert recall: 0.8593
- Bert f1-score: 0.8627
- Average word count: 8.3527
- Max word count: 16
- Min word count: 0
- Average token count: 13.2455
- % shortened texts with length > 12: 13.8393
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.5656 | 1.0 | 49 | 2.0627 | 0.2906 | 0.1424 | 0.2578 | 0.2581 | 0.7433 | 0.7729 | 0.7567 | 9.942 | 18 | 0 | 16.6964 | 27.6786 |
| 2.1428 | 2.0 | 98 | 1.7604 | 0.2976 | 0.1484 | 0.2654 | 0.2647 | 0.7512 | 0.7871 | 0.7674 | 9.9062 | 17 | 1 | 16.6696 | 25.4464 |
| 1.886 | 3.0 | 147 | 1.5671 | 0.4045 | 0.2327 | 0.3717 | 0.3712 | 0.8053 | 0.8179 | 0.8102 | 9.2411 | 17 | 0 | 15.2143 | 25.4464 |
| 1.7092 | 4.0 | 196 | 1.4529 | 0.4575 | 0.2743 | 0.4224 | 0.4234 | 0.8309 | 0.8276 | 0.8282 | 8.4777 | 16 | 0 | 14.1339 | 17.4107 |
| 1.61 | 5.0 | 245 | 1.3795 | 0.4869 | 0.2867 | 0.4435 | 0.4445 | 0.8476 | 0.8402 | 0.8431 | 8.4196 | 17 | 0 | 13.5982 | 17.4107 |
| 1.5541 | 6.0 | 294 | 1.3272 | 0.5085 | 0.3053 | 0.466 | 0.4664 | 0.857 | 0.8473 | 0.8514 | 8.2455 | 17 | 0 | 13.2768 | 16.5179 |
| 1.5157 | 7.0 | 343 | 1.2940 | 0.5227 | 0.3225 | 0.4752 | 0.4763 | 0.8583 | 0.8504 | 0.8537 | 8.2946 | 17 | 0 | 13.2679 | 14.7321 |
| 1.456 | 8.0 | 392 | 1.2721 | 0.5272 | 0.3269 | 0.4782 | 0.479 | 0.8653 | 0.857 | 0.8605 | 8.3839 | 17 | 0 | 13.2411 | 14.2857 |
| 1.4422 | 9.0 | 441 | 1.2600 | 0.527 | 0.3315 | 0.4793 | 0.4807 | 0.8656 | 0.8576 | 0.8609 | 8.3304 | 16 | 0 | 13.2679 | 13.8393 |
| 1.4384 | 10.0 | 490 | 1.2562 | 0.532 | 0.3351 | 0.4834 | 0.4837 | 0.8674 | 0.8593 | 0.8627 | 8.3527 | 16 | 0 | 13.2455 | 13.8393 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
