| --- |
| tags: |
| - summarization |
| - generated_from_trainer |
| model-index: |
| - name: NAS-bilingue |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # NAS-bilingue |
|
|
| This model was trained from scratch on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 3.7187 |
| - Rougelsum: 0.0922 |
|
|
| ## 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: 1.3739167643078955e-06 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - distributed_type: multi-GPU |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 15 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rougelsum | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:| |
| | No log | 1.0 | 5 | 4.5936 | 0.0759 | |
| | No log | 2.0 | 10 | 4.4276 | 0.0759 | |
| | No log | 3.0 | 15 | 4.2936 | 0.0759 | |
| | No log | 4.0 | 20 | 4.1820 | 0.0759 | |
| | No log | 5.0 | 25 | 4.0896 | 0.0881 | |
| | No log | 6.0 | 30 | 4.0121 | 0.0970 | |
| | No log | 7.0 | 35 | 3.9451 | 0.0918 | |
| | No log | 8.0 | 40 | 3.8875 | 0.0922 | |
| | No log | 9.0 | 45 | 3.8395 | 0.0922 | |
| | No log | 10.0 | 50 | 3.8011 | 0.0922 | |
| | No log | 11.0 | 55 | 3.7707 | 0.0922 | |
| | No log | 12.0 | 60 | 3.7480 | 0.0922 | |
| | No log | 13.0 | 65 | 3.7320 | 0.0922 | |
| | No log | 14.0 | 70 | 3.7223 | 0.0922 | |
| | No log | 15.0 | 75 | 3.7187 | 0.0922 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.26.1 |
| - Pytorch 1.13.1+cu117 |
| - Datasets 2.9.0 |
| - Tokenizers 0.13.2 |
| |
