metadata
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: text_shortening_model_v2
results: []
text_shortening_model_v2
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.4449
- Rouge1: 0.581
- Rouge2: 0.3578
- Rougel: 0.5324
- Rougelsum: 0.5317
- Bert precision: 0.8885
- Bert recall: 0.8981
- Average word count: 11.5929
- Max word count: 17
- Min word count: 3
- Average token count: 16.7071
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
No "summarize" prefix
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.7498 | 1.0 | 8 | 1.9424 | 0.4725 | 0.2644 | 0.4207 | 0.4216 | 0.8343 | 0.8502 | 11.7357 | 18 | 0 | 17.5143 |
| 1.5236 | 2.0 | 16 | 1.7731 | 0.5185 | 0.2961 | 0.4661 | 0.4665 | 0.8566 | 0.8646 | 11.05 | 18 | 0 | 16.6143 |
| 1.4381 | 3.0 | 24 | 1.6880 | 0.5459 | 0.3212 | 0.4947 | 0.4942 | 0.8773 | 0.8862 | 11.5857 | 18 | 3 | 16.8143 |
| 1.3895 | 4.0 | 32 | 1.6405 | 0.5537 | 0.3275 | 0.506 | 0.5061 | 0.8815 | 0.8894 | 11.7 | 18 | 3 | 16.6571 |
| 1.353 | 5.0 | 40 | 1.5941 | 0.5579 | 0.3347 | 0.5124 | 0.5119 | 0.8839 | 0.8933 | 11.7643 | 18 | 4 | 16.7429 |
| 1.3026 | 6.0 | 48 | 1.5568 | 0.5585 | 0.3379 | 0.5132 | 0.5129 | 0.8823 | 0.8945 | 11.9714 | 18 | 4 | 16.95 |
| 1.2624 | 7.0 | 56 | 1.5359 | 0.5696 | 0.3466 | 0.5202 | 0.5195 | 0.8837 | 0.897 | 12.0143 | 18 | 5 | 17.1143 |
| 1.2481 | 8.0 | 64 | 1.5186 | 0.5736 | 0.3517 | 0.5241 | 0.523 | 0.8849 | 0.898 | 12.0214 | 17 | 6 | 17.1714 |
| 1.2089 | 9.0 | 72 | 1.5055 | 0.5732 | 0.3499 | 0.5256 | 0.5246 | 0.8846 | 0.8979 | 12.0357 | 17 | 5 | 17.2214 |
| 1.1845 | 10.0 | 80 | 1.4898 | 0.5761 | 0.3548 | 0.5284 | 0.5276 | 0.886 | 0.8977 | 11.9 | 17 | 5 | 17.0786 |
| 1.1882 | 11.0 | 88 | 1.4787 | 0.5768 | 0.3573 | 0.5291 | 0.5288 | 0.8862 | 0.8986 | 11.8071 | 17 | 5 | 17.05 |
| 1.1649 | 12.0 | 96 | 1.4720 | 0.5784 | 0.3592 | 0.5319 | 0.531 | 0.8868 | 0.8988 | 11.7786 | 17 | 5 | 17.0 |
| 1.1643 | 13.0 | 104 | 1.4637 | 0.5785 | 0.3592 | 0.5314 | 0.5308 | 0.8875 | 0.8977 | 11.6571 | 17 | 3 | 16.8214 |
| 1.129 | 14.0 | 112 | 1.4565 | 0.5794 | 0.3585 | 0.5324 | 0.5315 | 0.8883 | 0.8984 | 11.6571 | 17 | 3 | 16.8 |
| 1.136 | 15.0 | 120 | 1.4516 | 0.5826 | 0.3598 | 0.537 | 0.5363 | 0.8898 | 0.8995 | 11.5857 | 17 | 3 | 16.6786 |
| 1.1191 | 16.0 | 128 | 1.4491 | 0.5828 | 0.3579 | 0.5357 | 0.535 | 0.8895 | 0.899 | 11.5929 | 17 | 3 | 16.6857 |
| 1.1192 | 17.0 | 136 | 1.4471 | 0.5794 | 0.355 | 0.5312 | 0.5307 | 0.8883 | 0.898 | 11.6143 | 17 | 3 | 16.7286 |
| 1.1085 | 18.0 | 144 | 1.4456 | 0.5808 | 0.3557 | 0.5315 | 0.5307 | 0.8883 | 0.8982 | 11.6286 | 17 | 3 | 16.7429 |
| 1.1063 | 19.0 | 152 | 1.4451 | 0.5808 | 0.3571 | 0.5321 | 0.5314 | 0.8884 | 0.8981 | 11.6 | 17 | 3 | 16.7143 |
| 1.0965 | 20.0 | 160 | 1.4449 | 0.581 | 0.3578 | 0.5324 | 0.5317 | 0.8885 | 0.8981 | 11.5929 | 17 | 3 | 16.7071 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3