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: 1.1063
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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2901 | 0.64 | 500 | 1.2203 |
| 1.1988 | 1.28 | 1000 | 1.1378 |
| 1.1228 | 1.93 | 1500 | 1.1128 |
| 0.9573 | 2.57 | 2000 | 1.1129 |
| 0.8102 | 3.21 | 2500 | 1.1158 |
| 0.8209 | 3.85 | 3000 | 1.1063 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.0
- Tokenizers 0.13.3