bart-base-finetuned-summarization-cnn-ver3
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: 2.9827
- Bertscore-mean-precision: 0.8811
- Bertscore-mean-recall: 0.8554
- Bertscore-mean-f1: 0.8679
- Bertscore-median-precision: 0.8809
- Bertscore-median-recall: 0.8545
- Bertscore-median-f1: 0.8669
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: 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: 1
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 |
|---|---|---|---|---|---|---|---|---|---|
| 3.632 | 1.0 | 5742 | 2.9827 | 0.8811 | 0.8554 | 0.8679 | 0.8809 | 0.8545 | 0.8669 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
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