t5-v1_1-small-finetuned-summarization-cnn-ver1
This model is a fine-tuned version of google/t5-v1_1-small on the cnn_dailymail dataset. It achieves the following results on the evaluation set:
- Loss: 2.7467
- Bertscore-mean-precision: 0.8764
- Bertscore-mean-recall: 0.8519
- Bertscore-mean-f1: 0.8639
- Bertscore-median-precision: 0.8746
- Bertscore-median-recall: 0.8518
- Bertscore-median-f1: 0.8632
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: 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: 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 |
|---|---|---|---|---|---|---|---|---|---|
| 4.6845 | 1.0 | 718 | 2.9003 | 0.8698 | 0.8456 | 0.8574 | 0.8693 | 0.8445 | 0.8570 |
| 3.7925 | 2.0 | 1436 | 2.7654 | 0.8765 | 0.8519 | 0.8639 | 0.8745 | 0.8512 | 0.8629 |
| 3.6332 | 3.0 | 2154 | 2.7467 | 0.8764 | 0.8519 | 0.8639 | 0.8746 | 0.8518 | 0.8632 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.0
- Tokenizers 0.13.2
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