t5-small-finetuned-cnndm-wikihow
This model is a fine-tuned version of Sevil/t5-small-finetuned-cnndm_3epoch_v2 on the wikihow dataset. It achieves the following results on the evaluation set:
- Loss: 2.2653
- Rouge1: 27.5037
- Rouge2: 10.8442
- Rougel: 23.4674
- Rougelsum: 26.7997
- Gen Len: 18.5558
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 2.8459 | 0.13 | 5000 | 2.5755 | 25.2929 | 8.7852 | 21.2379 | 24.5649 | 18.4758 |
| 2.7251 | 0.25 | 10000 | 2.5189 | 25.33 | 9.0505 | 21.4892 | 24.6523 | 18.4513 |
| 2.6696 | 0.38 | 15000 | 2.4805 | 26.3909 | 9.6858 | 22.3589 | 25.7297 | 18.4649 |
| 2.647 | 0.51 | 20000 | 2.4491 | 25.9234 | 9.3936 | 22.0086 | 25.2342 | 18.5558 |
| 2.5973 | 0.64 | 25000 | 2.4251 | 26.4988 | 9.8197 | 22.6201 | 25.8407 | 18.3438 |
| 2.5916 | 0.76 | 30000 | 2.4022 | 26.3149 | 9.8432 | 22.3695 | 25.6581 | 18.4506 |
| 2.5691 | 0.89 | 35000 | 2.3801 | 26.4198 | 9.8848 | 22.4856 | 25.7847 | 18.5381 |
| 2.5365 | 1.02 | 40000 | 2.3755 | 26.5846 | 10.0287 | 22.667 | 25.9606 | 18.5608 |
| 2.4649 | 1.14 | 45000 | 2.3663 | 26.5925 | 10.0569 | 22.6191 | 25.9247 | 18.5803 |
| 2.4539 | 1.27 | 50000 | 2.3490 | 26.9735 | 10.2389 | 22.9536 | 26.282 | 18.5126 |
| 2.4578 | 1.4 | 55000 | 2.3374 | 26.7878 | 10.2275 | 22.849 | 26.1188 | 18.6162 |
| 2.4365 | 1.53 | 60000 | 2.3266 | 27.1171 | 10.403 | 23.0596 | 26.4284 | 18.6128 |
| 2.428 | 1.65 | 65000 | 2.3209 | 27.1762 | 10.578 | 23.1577 | 26.5007 | 18.5246 |
| 2.4293 | 1.78 | 70000 | 2.3145 | 27.0896 | 10.5146 | 23.1502 | 26.4338 | 18.4604 |
| 2.4335 | 1.91 | 75000 | 2.2979 | 27.3373 | 10.6273 | 23.2944 | 26.6725 | 18.5403 |
| 2.3981 | 2.03 | 80000 | 2.3008 | 27.1857 | 10.6455 | 23.1333 | 26.5203 | 18.5412 |
| 2.3395 | 2.16 | 85000 | 2.2908 | 27.3123 | 10.7063 | 23.3126 | 26.626 | 18.4265 |
| 2.3463 | 2.29 | 90000 | 2.2869 | 27.5328 | 10.7662 | 23.4527 | 26.8613 | 18.5664 |
| 2.3481 | 2.42 | 95000 | 2.2802 | 27.4799 | 10.7826 | 23.4538 | 26.7912 | 18.5449 |
| 2.3345 | 2.54 | 100000 | 2.2774 | 27.3182 | 10.724 | 23.3276 | 26.669 | 18.5908 |
| 2.3254 | 2.67 | 105000 | 2.2713 | 27.3942 | 10.777 | 23.3918 | 26.7036 | 18.5681 |
| 2.3369 | 2.8 | 110000 | 2.2666 | 27.5976 | 10.9144 | 23.5832 | 26.9147 | 18.5471 |
| 2.3269 | 2.93 | 115000 | 2.2653 | 27.5037 | 10.8442 | 23.4674 | 26.7997 | 18.5558 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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