| license: apache-2.0 | |
| base_model: t5-small | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - rouge | |
| model-index: | |
| - name: my_billsum_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_billsum_model | |
| This model is a fine-tuned version of [t5-small](https://huggingface.co./t5-small) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 2.5803 | |
| - Rouge1: 0.1414 | |
| - Rouge2: 0.0501 | |
| - Rougel: 0.1176 | |
| - Rougelsum: 0.1176 | |
| - 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - training_steps: 200 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | |
| | 3.9137 | 0.65 | 40 | 3.0404 | 0.1351 | 0.044 | 0.1138 | 0.114 | 19.0 | | |
| | 3.0852 | 1.29 | 80 | 2.7349 | 0.1363 | 0.0453 | 0.1143 | 0.1144 | 19.0 | | |
| | 2.9298 | 1.94 | 120 | 2.6341 | 0.1405 | 0.0471 | 0.1162 | 0.1164 | 19.0 | | |
| | 2.8389 | 2.58 | 160 | 2.5929 | 0.1413 | 0.049 | 0.1176 | 0.118 | 19.0 | | |
| | 2.8414 | 3.23 | 200 | 2.5803 | 0.1414 | 0.0501 | 0.1176 | 0.1176 | 19.0 | | |
| ### Framework versions | |
| - Transformers 4.35.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.17.0 | |
| - Tokenizers 0.15.1 | |