xsum_and_billsum_and_samsum
This model is a fine-tuned version of psin/xsum_and_billsum on the samsum dataset. It achieves the following results on the evaluation set:
- Loss: 1.8894
- Rouge1: 0.3798
- Rouge2: 0.15
- Rougel: 0.316
- Rougelsum: 0.3158
- Gen Len: 16.1416
- Precision: 0.4394
- Recall: 0.3237
- F1: 0.0
- Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled
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: 72
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1 | Hashcode |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.1137 | 1.0 | 1842 | 1.8894 | 0.3798 | 0.15 | 0.316 | 0.3158 | 16.1416 | 0.4394 | 0.3237 | 0.0 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.28.1)-rescaled |
Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3