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distilbart-cnn-12-6-finetuned-roundup-4-2
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0837
- Rouge1: 52.9859
- Rouge2: 33.2082
- Rougel: 34.2505
- Rougelsum: 50.4194
- Gen Len: 142.0
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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 1.0 | 398 | 1.1731 | 52.5053 | 33.0302 | 34.0812 | 49.9567 | 141.6481 |
| 1.4188 | 2.0 | 796 | 1.0837 | 52.9859 | 33.2082 | 34.2505 | 50.4194 | 142.0 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
