| --- |
| license: apache-2.0 |
| base_model: t5-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: T5-small-summarization |
| 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. --> |
|
|
| # T5-small-summarization |
|
|
| This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the truncated version of Samsum dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 1.9294 |
| - Rouge1: 0.3772 |
| - Rouge2: 0.1453 |
| - Rougel: 0.3105 |
| - Rougelsum: 0.3106 |
| - Gen Len: 16.1832 |
|
|
| ## Model description |
|
|
| This model performs the summarization of Texts. |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 2e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | No log | 1.0 | 63 | 2.1165 | 0.338 | 0.1186 | 0.2811 | 0.2813 | 16.7595 | |
| | No log | 2.0 | 126 | 2.0210 | 0.3612 | 0.1338 | 0.2982 | 0.2985 | 16.5592 | |
| | No log | 3.0 | 189 | 1.9838 | 0.3652 | 0.1384 | 0.3034 | 0.304 | 16.1197 | |
| | No log | 4.0 | 252 | 1.9623 | 0.3715 | 0.142 | 0.3077 | 0.3079 | 16.2308 | |
| | No log | 5.0 | 315 | 1.9513 | 0.3727 | 0.1441 | 0.308 | 0.3084 | 16.1453 | |
| | No log | 6.0 | 378 | 1.9419 | 0.375 | 0.1438 | 0.309 | 0.3093 | 16.2234 | |
| | No log | 7.0 | 441 | 1.9376 | 0.3748 | 0.144 | 0.3102 | 0.3104 | 16.1465 | |
| | 2.2452 | 8.0 | 504 | 1.9324 | 0.3754 | 0.1451 | 0.3098 | 0.3099 | 16.1893 | |
| | 2.2452 | 9.0 | 567 | 1.9302 | 0.3769 | 0.1459 | 0.3112 | 0.3113 | 16.1966 | |
| | 2.2452 | 10.0 | 630 | 1.9294 | 0.3772 | 0.1453 | 0.3105 | 0.3106 | 16.1832 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.37.0 |
| - Pytorch 2.1.2 |
| - Datasets 2.1.0 |
| - Tokenizers 0.15.1 |
|
|
