retnet-xsum
This model is a fine-tuned version of on the xsum dataset. It achieves the following results on the evaluation set:
- Loss: 4.0200
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: 0.0006
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 5.3257 | 1.0 | 2187 | 4.6412 |
| 4.5863 | 2.0 | 4375 | 4.3474 |
| 4.3703 | 3.0 | 6562 | 4.2111 |
| 4.2404 | 4.0 | 8750 | 4.1213 |
| 4.1568 | 5.0 | 10937 | 4.0673 |
| 4.0975 | 6.0 | 13125 | 4.0371 |
| 4.0618 | 7.0 | 15312 | 4.0219 |
| 4.045 | 8.0 | 17496 | 4.0200 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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