bart-base-finetuned-summarization-cnn-ver1.3
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: 2.3148
- Bertscore-mean-precision: 0.8890
- Bertscore-mean-recall: 0.8603
- Bertscore-mean-f1: 0.8742
- Bertscore-median-precision: 0.8874
- Bertscore-median-recall: 0.8597
- Bertscore-median-f1: 0.8726
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: 4e-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 |
---|---|---|---|---|---|---|---|---|---|
2.3735 | 1.0 | 5742 | 2.2581 | 0.8831 | 0.8586 | 0.8705 | 0.8834 | 0.8573 | 0.8704 |
1.744 | 2.0 | 11484 | 2.2479 | 0.8920 | 0.8620 | 0.8765 | 0.8908 | 0.8603 | 0.8752 |
1.3643 | 3.0 | 17226 | 2.3148 | 0.8890 | 0.8603 | 0.8742 | 0.8874 | 0.8597 | 0.8726 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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