bert_large_cnn_daily2
This model is a fine-tuned version of alexdg19/bert_large_cnn_daily on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 1.3008
- Rouge1: 0.4504
- Rouge2: 0.2337
- Rougel: 0.3294
- Rougelsum: 0.424
- Gen Len: 60.2728
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: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| 1.1882 | 1.0 | 1021 | 1.1904 | 0.4379 | 0.223 | 0.318 | 0.41 | 61.3551 |
| 0.9513 | 2.0 | 2042 | 1.1891 | 0.4506 | 0.2353 | 0.3312 | 0.4239 | 59.6771 |
| 0.7581 | 3.0 | 3064 | 1.2440 | 0.4488 | 0.2317 | 0.3273 | 0.4214 | 59.9909 |
| 0.6364 | 4.0 | 4084 | 1.3008 | 0.4504 | 0.2337 | 0.3294 | 0.424 | 60.2728 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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