mt5-base-finetuned-xsum
This model is a fine-tuned version of google/mt5-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 1.5134
- Rouge2: 0.2001
- Rougel: 1.4917
- Rougelsum: 1.4788
- Gen Len: 8.6992
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: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.0 | 1.0 | 559 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 2.0 | 1118 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 3.0 | 1677 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 4.0 | 2236 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 5.0 | 2795 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 6.0 | 3354 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 7.0 | 3913 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 8.0 | 4472 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 9.0 | 5031 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 10.0 | 5590 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 11.0 | 6149 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 12.0 | 6708 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 13.0 | 7267 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 14.0 | 7826 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 15.0 | 8385 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 16.0 | 8944 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 17.0 | 9503 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 18.0 | 10062 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 19.0 | 10621 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 20.0 | 11180 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 21.0 | 11739 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 22.0 | 12298 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 23.0 | 12857 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 24.0 | 13416 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 25.0 | 13975 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 26.0 | 14534 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 27.0 | 15093 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 28.0 | 15652 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 29.0 | 16211 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 30.0 | 16770 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 31.0 | 17329 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 32.0 | 17888 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 33.0 | 18447 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 34.0 | 19006 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 35.0 | 19565 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 36.0 | 20124 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 37.0 | 20683 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 38.0 | 21242 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 39.0 | 21801 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 40.0 | 22360 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 41.0 | 22919 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 42.0 | 23478 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 43.0 | 24037 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 44.0 | 24596 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 45.0 | 25155 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 46.0 | 25714 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 47.0 | 26273 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 48.0 | 26832 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 49.0 | 27391 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
| 0.0 | 50.0 | 27950 | nan | 1.5134 | 0.2001 | 1.4917 | 1.4788 | 8.6992 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3