| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: google-t5/t5-small |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: my_first_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. --> |
|
|
| # my_first_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: 2.5001 |
| - Rouge1: 0.1369 |
| - Rouge2: 0.0522 |
| - Rougel: 0.1122 |
| - Rougelsum: 0.1123 |
| - Gen Len: 19.0 |
| |
| ## 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 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 | 2.7763 | 0.1125 | 0.0313 | 0.0938 | 0.0939 | 19.0 | |
| | No log | 2.0 | 124 | 2.5706 | 0.1291 | 0.0444 | 0.1059 | 0.1062 | 19.0 | |
| | No log | 3.0 | 186 | 2.5161 | 0.1366 | 0.0516 | 0.1118 | 0.112 | 19.0 | |
| | No log | 4.0 | 248 | 2.5001 | 0.1369 | 0.0522 | 0.1122 | 0.1123 | 19.0 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.46.3 |
| - Pytorch 2.5.1+cu121 |
| - Datasets 3.2.0 |
| - Tokenizers 0.20.3 |
| |
