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BART_corrector_15
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: 0.0214
- Rouge1: 80.3263
- Rouge2: 78.1274
- Rougel: 80.3215
- Rougelsum: 80.3039
- Gen Len: 19.3993
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 0.0597 | 1.0 | 2365 | 0.0367 | 79.3503 | 76.3308 | 79.32 | 79.3005 | 19.3992 |
| 0.0322 | 2.0 | 4730 | 0.0276 | 79.9515 | 77.4211 | 79.9331 | 79.9164 | 19.3983 |
| 0.0212 | 3.0 | 7095 | 0.0241 | 80.1413 | 77.8084 | 80.129 | 80.1098 | 19.3992 |
| 0.0148 | 4.0 | 9460 | 0.0219 | 80.2625 | 78.035 | 80.2579 | 80.2372 | 19.4 |
| 0.0111 | 5.0 | 11825 | 0.0214 | 80.3263 | 78.1274 | 80.3215 | 80.3039 | 19.3993 |
Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
