bart-large-cnn-xsum
This model is a fine-tuned version of facebook/bart-large-cnn on the xsum dataset. It achieves the following results on the evaluation set:
- Loss: 2.0314
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-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.9458 | 0.3921 | 500 | 1.8663 |
| 1.7833 | 0.7842 | 1000 | 1.9308 |
| 1.3364 | 1.1762 | 1500 | 1.9378 |
| 1.3562 | 1.5683 | 2000 | 1.9538 |
| 1.3173 | 1.9604 | 2500 | 1.8672 |
| 0.9227 | 2.3525 | 3000 | 2.0590 |
| 0.8619 | 2.7446 | 3500 | 2.0314 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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