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bart-samsum
This model is a fine-tuned version of ainize/bart-base-cnn on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.4587
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2901 | 0.64 | 500 | 1.2203 |
| 1.2057 | 1.28 | 1000 | 1.1384 |
| 1.1364 | 1.93 | 1500 | 1.1225 |
| 0.9711 | 2.57 | 2000 | 1.1362 |
| 0.786 | 3.21 | 2500 | 1.1461 |
| 0.818 | 3.85 | 3000 | 1.1298 |
| 0.7135 | 4.49 | 3500 | 1.1666 |
| 0.6222 | 5.14 | 4000 | 1.2114 |
| 0.64 | 5.78 | 4500 | 1.2103 |
| 0.5272 | 6.42 | 5000 | 1.2571 |
| 0.5057 | 7.06 | 5500 | 1.2963 |
| 0.4917 | 7.7 | 6000 | 1.2937 |
| 0.4291 | 8.35 | 6500 | 1.3286 |
| 0.4171 | 8.99 | 7000 | 1.3125 |
| 0.418 | 9.63 | 7500 | 1.3516 |
| 0.3576 | 10.27 | 8000 | 1.3778 |
| 0.3736 | 10.91 | 8500 | 1.3847 |
| 0.3443 | 11.56 | 9000 | 1.4215 |
| 0.2952 | 12.2 | 9500 | 1.4324 |
| 0.3236 | 12.84 | 10000 | 1.4355 |
| 0.2978 | 13.48 | 10500 | 1.4473 |
| 0.2828 | 14.13 | 11000 | 1.4557 |
| 0.304 | 14.77 | 11500 | 1.4587 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3
