t5-small-finetuned-summarization-cnn-ver3
This model is a fine-tuned version of t5-small on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 2.1072
- Bertscore-mean-precision: 0.8861
- Bertscore-mean-recall: 0.8592
- Bertscore-mean-f1: 0.8723
- Bertscore-median-precision: 0.8851
- Bertscore-median-recall: 0.8582
- Bertscore-median-f1: 0.8719
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: 0.0003
- 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
Training results
| Training Loss | Epoch | Step | Validation Loss | Bertscore-mean-precision | Bertscore-mean-recall | Bertscore-mean-f1 | Bertscore-median-precision | Bertscore-median-recall | Bertscore-median-f1 |
|---|---|---|---|---|---|---|---|---|---|
| 2.0168 | 1.0 | 718 | 2.0528 | 0.8870 | 0.8591 | 0.8727 | 0.8864 | 0.8578 | 0.8724 |
| 1.8387 | 2.0 | 1436 | 2.0610 | 0.8863 | 0.8591 | 0.8723 | 0.8848 | 0.8575 | 0.8712 |
| 1.7302 | 3.0 | 2154 | 2.0659 | 0.8856 | 0.8588 | 0.8719 | 0.8847 | 0.8569 | 0.8717 |
| 1.6459 | 4.0 | 2872 | 2.0931 | 0.8860 | 0.8592 | 0.8722 | 0.8850 | 0.8570 | 0.8718 |
| 1.5907 | 5.0 | 3590 | 2.1072 | 0.8861 | 0.8592 | 0.8723 | 0.8851 | 0.8582 | 0.8719 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2