| --- |
| license: mit |
| base_model: facebook/bart-large-cnn |
| tags: |
| - generated_from_trainer |
| metrics: |
| - rouge |
| model-index: |
| - name: 02_ToS-BART |
| 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. --> |
|
|
| # 02_ToS-BART |
| |
| This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5697 |
| - Rouge1: 0.6086 |
| - Rouge2: 0.4577 |
| - Rougel: 0.5072 |
| - Rougelsum: 0.5071 |
| - Gen Len: 110.7293 |
| |
| ## 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: 3e-05 |
| - train_batch_size: 2 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 6 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:| |
| | No log | 1.0 | 360 | 0.5018 | 0.5957 | 0.44 | 0.4873 | 0.4876 | 110.8398 | |
| | 0.049 | 2.0 | 720 | 0.5468 | 0.5923 | 0.4364 | 0.4812 | 0.4813 | 111.6133 | |
| | 0.0789 | 3.0 | 1080 | 0.5157 | 0.6035 | 0.4439 | 0.4933 | 0.4934 | 110.1768 | |
| | 0.0789 | 4.0 | 1440 | 0.5905 | 0.5873 | 0.4279 | 0.4781 | 0.4781 | 110.8343 | |
| | 0.044 | 5.0 | 1800 | 0.5581 | 0.6046 | 0.4544 | 0.5023 | 0.502 | 110.8674 | |
| | 0.0231 | 6.0 | 2160 | 0.5697 | 0.6086 | 0.4577 | 0.5072 | 0.5071 | 110.7293 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
|
|
