BART-model
This model is a fine-tuned version of amagzari/pegasus-cnn_dailymail-finetuned-samsum-v2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9435
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4132 | 0.99 | 16 | 1.1373 |
| 1.3997 | 1.98 | 32 | 1.1288 |
| 1.3814 | 2.97 | 48 | 1.1147 |
| 1.5235 | 3.95 | 64 | 1.0936 |
| 1.1194 | 4.94 | 80 | 1.0703 |
| 1.356 | 5.99 | 97 | 1.0459 |
| 1.2157 | 6.98 | 113 | 1.0206 |
| 0.9685 | 7.97 | 129 | 0.9902 |
| 1.2886 | 8.96 | 145 | 0.9639 |
| 1.2373 | 9.88 | 160 | 0.9435 |
Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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