| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-t5/t5-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: summarization_model |
| 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. --> |
|
|
| # summarization_model |
| |
| This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 2.5574 |
| - Rouge1: 0.1496 |
| - Rouge2: 0.0554 |
| - Rougel: 0.1221 |
| - Rougelsum: 0.1221 |
| - Gen Len: 20.0 |
| |
| ## 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: 2e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - 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: 4 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | No log | 1.0 | 62 | 2.8486 | 0.1367 | 0.041 | 0.1133 | 0.1132 | 20.0 | |
| | No log | 2.0 | 124 | 2.6347 | 0.148 | 0.0525 | 0.1198 | 0.12 | 20.0 | |
| | No log | 3.0 | 186 | 2.5750 | 0.1484 | 0.0551 | 0.1208 | 0.121 | 20.0 | |
| | No log | 4.0 | 248 | 2.5574 | 0.1496 | 0.0554 | 0.1221 | 0.1221 | 20.0 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.53.2 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.21.4 |
| |
