This model is a fine-tuned version of allenai/led-base-16384 on the SGH news articles and summaries dataset. It achieves the following results on the evaluation set:
- Loss: 0.8163
- Rouge2 Precision: 0.3628
- Rouge2 Recall: 0.3589
- Rouge2 Fmeasure: 0.3316
Model description
This model was created to generate summaries of news articles.
Intended uses & limitations
The model takes up to maximum article length of 768 tokens and generates a summary of maximum length of 512 tokens, and minimum length of 100 tokens.
Training and evaluation data
This model was trained on 100+ articles and summaries from SGH.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|---|---|---|---|---|---|---|
| 1.5952 | 0.23 | 10 | 1.0414 | 0.2823 | 0.3908 | 0.3013 |
| 1.8116 | 0.47 | 20 | 0.9171 | 0.3728 | 0.273 | 0.3056 |
| 1.6289 | 0.7 | 30 | 0.8553 | 0.3284 | 0.2892 | 0.291 |
| 1.5074 | 0.93 | 40 | 0.8163 | 0.3628 | 0.3589 | 0.3316 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 1.2.1
- Tokenizers 0.12.1