📋 BUOD: distilBART Transformer Model
This model is a fine-tuned version of sshleifer/distilbart-cnn-12-6 on the KAMI-3000 dataset, for the task of Filipino Text Summarization.
It achieves the following results on the evaluation set:
- Loss: 1.8049
- Rouge1: 50.5143
- Rouge2: 23.2481
- Rougel: 34.135
- Rougelsum: 46.4261
🔧 Finetuning/ Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| 2.1377 | 1.0 | 586 | 1.8792 | 49.8737 | 22.7881 | 33.6698 | 45.8037 |
| 1.5731 | 2.0 | 1172 | 1.8049 | 50.5143 | 23.2481 | 34.135 | 46.4261 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
Inference Providers
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