| | --- |
| | base_model: facebook/bart-large-cnn |
| | library_name: peft |
| | license: mit |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: fine_tuned_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. --> |
| |
|
| | # fine_tuned_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: 0.7075 |
| |
|
| | ## 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: 4e-05 |
| | - train_batch_size: 16 |
| | - eval_batch_size: 16 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 64 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - num_epochs: 30 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:-----:|:----:|:---------------:| |
| | | No log | 1.0 | 8 | 0.7205 | |
| | | 0.4729 | 2.0 | 16 | 0.7109 | |
| | | 0.4794 | 3.0 | 24 | 0.7048 | |
| | | 0.4716 | 4.0 | 32 | 0.7081 | |
| | | 0.476 | 5.0 | 40 | 0.7095 | |
| | | 0.476 | 6.0 | 48 | 0.7174 | |
| | | 0.4751 | 7.0 | 56 | 0.7050 | |
| | | 0.4683 | 8.0 | 64 | 0.7047 | |
| | | 0.4583 | 9.0 | 72 | 0.7058 | |
| | | 0.474 | 10.0 | 80 | 0.7045 | |
| | | 0.474 | 11.0 | 88 | 0.7062 | |
| | | 0.4651 | 12.0 | 96 | 0.7047 | |
| | | 0.4523 | 13.0 | 104 | 0.7028 | |
| | | 0.4626 | 14.0 | 112 | 0.7049 | |
| | | 0.4634 | 15.0 | 120 | 0.7067 | |
| | | 0.4634 | 16.0 | 128 | 0.7091 | |
| | | 0.4543 | 17.0 | 136 | 0.7087 | |
| | | 0.4502 | 18.0 | 144 | 0.7084 | |
| | | 0.4604 | 19.0 | 152 | 0.7098 | |
| | | 0.4503 | 20.0 | 160 | 0.7065 | |
| | | 0.4503 | 21.0 | 168 | 0.7046 | |
| | | 0.4642 | 22.0 | 176 | 0.7033 | |
| | | 0.4334 | 23.0 | 184 | 0.7029 | |
| | | 0.4626 | 24.0 | 192 | 0.7037 | |
| | | 0.4584 | 25.0 | 200 | 0.7046 | |
| | | 0.4584 | 26.0 | 208 | 0.7063 | |
| | | 0.4508 | 27.0 | 216 | 0.7075 | |
| | | 0.4498 | 28.0 | 224 | 0.7078 | |
| | | 0.4532 | 29.0 | 232 | 0.7077 | |
| | | 0.4514 | 30.0 | 240 | 0.7075 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - PEFT 0.12.0 |
| | - Transformers 4.42.4 |
| | - Pytorch 2.3.1+cu121 |
| | - Datasets 2.20.0 |
| | - Tokenizers 0.19.1 |
