| --- |
| library_name: transformers |
| base_model: rosypossyyyy/bart-email-multitask |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: bart-email-multitask |
| 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. --> |
|
|
| # bart-email-multitask |
|
|
| This model is a fine-tuned version of [rosypossyyyy/bart-email-multitask](https://huggingface.co/rosypossyyyy/bart-email-multitask) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5448 |
| - Rouge1: 51.01 |
| - Rouge2: 36.53 |
| - Rougel: 44.46 |
| - Rougelsum: 44.91 |
| - Gen Len: 53.5864 |
|
|
| ## 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: 5e-06 |
| - train_batch_size: 4 |
| - eval_batch_size: 4 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 16 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 200 |
| - num_epochs: 2 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| |
| | 0.3719 | 0.1726 | 500 | 0.5641 | 49.82 | 35.56 | 43.37 | 43.9 | 55.6862 | |
| | 0.39 | 0.3452 | 1000 | 0.5499 | 50.66 | 36.17 | 44.07 | 44.56 | 53.0616 | |
| | 0.4325 | 0.5178 | 1500 | 0.5412 | 50.59 | 36.52 | 44.19 | 44.63 | 54.4194 | |
| | 0.4328 | 0.6904 | 2000 | 0.5556 | 50.8 | 36.6 | 44.19 | 44.7 | 54.1698 | |
| | 0.4133 | 0.8630 | 2500 | 0.5358 | 50.67 | 36.36 | 44.02 | 44.56 | 54.8808 | |
| | 0.3576 | 1.0356 | 3000 | 0.5371 | 51.14 | 36.78 | 44.59 | 45.09 | 53.4543 | |
| | 0.3878 | 1.2082 | 3500 | 0.5359 | 50.77 | 36.52 | 44.17 | 44.73 | 54.4243 | |
| | 0.3757 | 1.3808 | 4000 | 0.5294 | 50.76 | 36.73 | 44.34 | 44.9 | 54.2312 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.57.5 |
| - Pytorch 2.9.0+cu126 |
| - Datasets 4.5.0 |
| - Tokenizers 0.22.2 |
| |
