bart-base-finetuned-summarization-cnn-ver1.2
This model is a fine-tuned version of facebook/bart-base on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 2.2476
- Bertscore-mean-precision: 0.8904
- Bertscore-mean-recall: 0.8611
- Bertscore-mean-f1: 0.8753
- Bertscore-median-precision: 0.8891
- Bertscore-median-recall: 0.8600
- Bertscore-median-f1: 0.8741
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: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
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 |
|---|---|---|---|---|---|---|---|---|---|
| 2.3305 | 1.0 | 5742 | 2.2125 | 0.8845 | 0.8587 | 0.8713 | 0.8840 | 0.8577 | 0.8706 |
| 1.7751 | 2.0 | 11484 | 2.2028 | 0.8910 | 0.8616 | 0.8759 | 0.8903 | 0.8603 | 0.8744 |
| 1.4564 | 3.0 | 17226 | 2.2476 | 0.8904 | 0.8611 | 0.8753 | 0.8891 | 0.8600 | 0.8741 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2