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text_shortening_model_v40
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: 3.3335
- Rouge1: 0.4511
- Rouge2: 0.2377
- Rougel: 0.4039
- Rougelsum: 0.4038
- Bert precision: 0.8635
- Bert recall: 0.8629
- Average word count: 8.5826
- Max word count: 16
- Min word count: 5
- Average token count: 16.5616
- % shortened texts with length > 12: 4.8048
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: 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: 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3.0922 | 1.0 | 73 | 2.2144 | 0.4539 | 0.2272 | 0.4068 | 0.4055 | 0.8657 | 0.8684 | 8.7027 | 15 | 5 | 14.3423 | 4.2042 |
| 1.75 | 2.0 | 146 | 2.0055 | 0.4658 | 0.2381 | 0.4085 | 0.4088 | 0.8654 | 0.8656 | 8.7087 | 16 | 5 | 15.1652 | 4.8048 |
| 1.311 | 3.0 | 219 | 2.0021 | 0.456 | 0.2257 | 0.4124 | 0.4117 | 0.8644 | 0.8646 | 8.6396 | 15 | 5 | 15.9279 | 5.1051 |
| 1.0163 | 4.0 | 292 | 2.0698 | 0.467 | 0.2403 | 0.4159 | 0.4162 | 0.8636 | 0.8699 | 9.2973 | 16 | 5 | 17.2162 | 9.9099 |
| 0.8546 | 5.0 | 365 | 2.0707 | 0.4527 | 0.2392 | 0.4129 | 0.4126 | 0.8637 | 0.8647 | 8.4895 | 17 | 4 | 16.3153 | 4.8048 |
| 0.7222 | 6.0 | 438 | 2.1452 | 0.4562 | 0.2349 | 0.4077 | 0.4064 | 0.8693 | 0.8623 | 8.021 | 15 | 4 | 14.1051 | 1.2012 |
| 0.5723 | 7.0 | 511 | 2.3520 | 0.4563 | 0.2403 | 0.4142 | 0.413 | 0.8666 | 0.8658 | 8.5916 | 16 | 5 | 16.5465 | 6.9069 |
| 0.5274 | 8.0 | 584 | 2.2896 | 0.4502 | 0.2434 | 0.4077 | 0.4078 | 0.8639 | 0.8639 | 8.5586 | 14 | 5 | 14.8048 | 2.1021 |
| 0.3767 | 9.0 | 657 | 2.2928 | 0.4565 | 0.2368 | 0.4125 | 0.4114 | 0.8682 | 0.8623 | 8.0691 | 14 | 4 | 14.4204 | 1.8018 |
| 0.2987 | 10.0 | 730 | 2.5411 | 0.4539 | 0.2383 | 0.4057 | 0.4056 | 0.8652 | 0.8631 | 8.5826 | 15 | 5 | 15.6637 | 4.5045 |
| 0.2319 | 11.0 | 803 | 2.8995 | 0.4513 | 0.2367 | 0.4069 | 0.4068 | 0.8631 | 0.8622 | 8.6607 | 17 | 5 | 16.4535 | 5.7057 |
| 0.2167 | 12.0 | 876 | 2.7950 | 0.4632 | 0.2521 | 0.4163 | 0.4162 | 0.8673 | 0.8679 | 8.7267 | 16 | 4 | 16.3243 | 6.3063 |
| 0.1952 | 13.0 | 949 | 2.6240 | 0.4537 | 0.2396 | 0.406 | 0.4059 | 0.8632 | 0.8648 | 8.8258 | 18 | 5 | 16.2613 | 7.8078 |
| 0.1395 | 14.0 | 1022 | 2.8894 | 0.4588 | 0.2412 | 0.4141 | 0.4144 | 0.864 | 0.8658 | 8.6216 | 15 | 5 | 16.6426 | 3.6036 |
| 0.1298 | 15.0 | 1095 | 2.7580 | 0.4562 | 0.2384 | 0.4085 | 0.4088 | 0.8661 | 0.8659 | 8.5586 | 15 | 5 | 16.3634 | 5.4054 |
| 0.1044 | 16.0 | 1168 | 2.7724 | 0.466 | 0.2527 | 0.4175 | 0.4171 | 0.8677 | 0.8694 | 8.7387 | 15 | 4 | 16.4535 | 5.1051 |
| 0.0944 | 17.0 | 1241 | 2.9161 | 0.4429 | 0.232 | 0.3986 | 0.3986 | 0.8619 | 0.8621 | 8.6306 | 16 | 5 | 16.5255 | 5.4054 |
| 0.077 | 18.0 | 1314 | 3.1718 | 0.4549 | 0.2372 | 0.4054 | 0.4052 | 0.863 | 0.8639 | 8.6456 | 15 | 5 | 16.7447 | 4.8048 |
| 0.0561 | 19.0 | 1387 | 3.2650 | 0.4581 | 0.2413 | 0.4092 | 0.4089 | 0.866 | 0.865 | 8.5195 | 16 | 5 | 16.4174 | 4.8048 |
| 0.0542 | 20.0 | 1460 | 3.3335 | 0.4511 | 0.2377 | 0.4039 | 0.4038 | 0.8635 | 0.8629 | 8.5826 | 16 | 5 | 16.5616 | 4.8048 |
Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
