flan-t5-base-samsum
This model is a fine-tuned version of google/flan-t5-base on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.3859
- Rouge1: 47.1046
- Rouge2: 23.264
- Rougel: 39.2757
- Rougelsum: 43.2598
- Gen Len: 17.3333
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: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.5121 | 0.08 | 50 | 1.4287 | 46.7868 | 22.863 | 38.971 | 42.8209 | 16.9634 |
| 1.46 | 0.16 | 100 | 1.4199 | 46.8031 | 22.8195 | 39.0708 | 42.8717 | 17.2393 |
| 1.4515 | 0.24 | 150 | 1.4147 | 46.6849 | 23.0376 | 38.9434 | 42.8344 | 17.1245 |
| 1.4679 | 0.33 | 200 | 1.4121 | 46.8756 | 22.8504 | 39.1671 | 43.1892 | 17.3431 |
| 1.451 | 0.41 | 250 | 1.4109 | 46.8572 | 23.09 | 39.2939 | 43.2955 | 17.2686 |
| 1.4434 | 0.49 | 300 | 1.4040 | 46.6829 | 23.071 | 39.3131 | 43.1432 | 16.9158 |
| 1.4417 | 0.57 | 350 | 1.4007 | 46.8637 | 23.0661 | 39.2462 | 43.1897 | 17.1172 |
| 1.4781 | 0.65 | 400 | 1.3952 | 46.8511 | 23.1134 | 39.3071 | 43.2164 | 17.2076 |
| 1.4626 | 0.73 | 450 | 1.3940 | 47.1533 | 23.2771 | 39.3094 | 43.2806 | 17.2222 |
| 1.4307 | 0.81 | 500 | 1.3955 | 46.9527 | 23.2227 | 39.2844 | 43.1903 | 17.2002 |
| 1.4586 | 0.9 | 550 | 1.3933 | 46.7523 | 23.1759 | 39.2675 | 43.1588 | 17.3040 |
| 1.4465 | 0.98 | 600 | 1.3905 | 46.855 | 23.3518 | 39.2879 | 43.2145 | 17.3468 |
| 1.381 | 1.06 | 650 | 1.3953 | 46.9719 | 22.9788 | 39.0886 | 43.1892 | 17.4066 |
| 1.4125 | 1.14 | 700 | 1.3922 | 46.535 | 23.0956 | 38.9275 | 42.9811 | 17.2381 |
| 1.3667 | 1.22 | 750 | 1.3922 | 47.3311 | 23.4123 | 39.5412 | 43.5624 | 17.2930 |
| 1.3878 | 1.3 | 800 | 1.3953 | 46.6737 | 23.2153 | 39.2982 | 43.2596 | 17.3358 |
| 1.3884 | 1.38 | 850 | 1.3931 | 46.9764 | 23.1561 | 39.1606 | 43.2115 | 17.3614 |
| 1.3766 | 1.47 | 900 | 1.3898 | 47.0466 | 23.1674 | 39.2822 | 43.293 | 17.3333 |
| 1.3727 | 1.55 | 950 | 1.3889 | 46.7311 | 23.0837 | 39.0882 | 43.0072 | 17.3211 |
| 1.4001 | 1.63 | 1000 | 1.3859 | 47.1046 | 23.264 | 39.2757 | 43.2598 | 17.3333 |
| 1.3894 | 1.71 | 1050 | 1.3874 | 47.2479 | 23.3762 | 39.4723 | 43.5241 | 17.3297 |
| 1.3697 | 1.79 | 1100 | 1.3860 | 47.1037 | 23.3894 | 39.3848 | 43.3875 | 17.3504 |
| 1.3886 | 1.87 | 1150 | 1.3862 | 47.0714 | 23.3937 | 39.4181 | 43.3841 | 17.3260 |
| 1.4037 | 1.95 | 1200 | 1.3861 | 47.0725 | 23.4085 | 39.3575 | 43.3676 | 17.3321 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.0+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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