metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikihow
metrics:
- rouge
model-index:
- name: t5-small-finetuned-wikihow_3epoch_b4_lr3e-4
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wikihow
type: wikihow
args: all
metrics:
- name: Rouge1
type: rouge
value: 27.4024
t5-small-finetuned-wikihow_3epoch_b4_lr3e-4
This model is a fine-tuned version of t5-small on the wikihow dataset. It achieves the following results on the evaluation set:
- Loss: 2.2757
- Rouge1: 27.4024
- Rouge2: 10.7065
- Rougel: 23.3153
- Rougelsum: 26.7336
- Gen Len: 18.5506
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 |
|---|---|---|---|---|---|---|---|---|
| 2.8424 | 0.13 | 5000 | 2.5695 | 25.2232 | 8.7617 | 21.2019 | 24.4949 | 18.4151 |
| 2.7334 | 0.25 | 10000 | 2.5229 | 25.3739 | 9.0477 | 21.5054 | 24.7553 | 18.3802 |
| 2.6823 | 0.38 | 15000 | 2.4857 | 26.341 | 9.6711 | 22.3446 | 25.7256 | 18.449 |
| 2.6607 | 0.51 | 20000 | 2.4540 | 26.0269 | 9.4722 | 22.0822 | 25.3602 | 18.4704 |
| 2.6137 | 0.64 | 25000 | 2.4326 | 26.2966 | 9.6815 | 22.4422 | 25.6326 | 18.3517 |
| 2.6077 | 0.76 | 30000 | 2.4108 | 26.0981 | 9.6221 | 22.1189 | 25.454 | 18.5079 |
| 2.5847 | 0.89 | 35000 | 2.3879 | 26.2675 | 9.6435 | 22.3738 | 25.6122 | 18.4838 |
| 2.5558 | 1.02 | 40000 | 2.3827 | 26.3458 | 9.7844 | 22.4718 | 25.7388 | 18.5097 |
| 2.4902 | 1.14 | 45000 | 2.3725 | 26.4987 | 9.9634 | 22.5398 | 25.8399 | 18.5912 |
| 2.4785 | 1.27 | 50000 | 2.3549 | 26.884 | 10.1136 | 22.8212 | 26.2262 | 18.4763 |
| 2.4822 | 1.4 | 55000 | 2.3467 | 26.8635 | 10.2266 | 22.9161 | 26.2252 | 18.5847 |
| 2.46 | 1.53 | 60000 | 2.3393 | 26.8602 | 10.1785 | 22.8453 | 26.1917 | 18.548 |
| 2.4523 | 1.65 | 65000 | 2.3330 | 26.91 | 10.237 | 22.9309 | 26.2372 | 18.5154 |
| 2.4525 | 1.78 | 70000 | 2.3203 | 27.073 | 10.4317 | 23.1355 | 26.4528 | 18.5063 |
| 2.4566 | 1.91 | 75000 | 2.3109 | 27.3853 | 10.5413 | 23.3455 | 26.7408 | 18.5258 |
| 2.4234 | 2.03 | 80000 | 2.3103 | 27.0836 | 10.4857 | 23.0538 | 26.409 | 18.5326 |
| 2.3686 | 2.16 | 85000 | 2.2986 | 27.311 | 10.6038 | 23.3068 | 26.6636 | 18.4874 |
| 2.3758 | 2.29 | 90000 | 2.2969 | 27.3509 | 10.6502 | 23.2764 | 26.6832 | 18.5438 |
| 2.3777 | 2.42 | 95000 | 2.2907 | 27.39 | 10.5842 | 23.3601 | 26.7433 | 18.5444 |
| 2.3624 | 2.54 | 100000 | 2.2875 | 27.3717 | 10.6098 | 23.3326 | 26.7232 | 18.5521 |
| 2.3543 | 2.67 | 105000 | 2.2811 | 27.4188 | 10.6919 | 23.3022 | 26.7426 | 18.564 |
| 2.366 | 2.8 | 110000 | 2.2763 | 27.4872 | 10.7079 | 23.4135 | 26.829 | 18.5399 |
| 2.3565 | 2.93 | 115000 | 2.2757 | 27.4024 | 10.7065 | 23.3153 | 26.7336 | 18.5506 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6