mt5-small-finetuned-persian-news-summarization-with-additional-hyperparameters
This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.1164
- Rouge1 F1: 56.7956
- Rouge1 Precision: 57.5473
- Rouge1 Recall: 58.6205
- Rouge2 F1: 40.1594
- Rouge2 Precision: 40.4966
- Rouge2 Recall: 41.7103
- Rougel F1: 49.1004
- Rougel Precision: 49.6461
- Rougel Recall: 50.8065
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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 F1 | Rouge1 Precision | Rouge1 Recall | Rouge2 F1 | Rouge2 Precision | Rouge2 Recall | Rougel F1 | Rougel Precision | Rougel Recall |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.7072 | 1.0 | 1205 | 1.2617 | 51.8527 | 61.6615 | 48.1083 | 37.2937 | 44.4226 | 34.639 | 45.3777 | 53.9129 | 42.1657 |
| 1.4721 | 2.0 | 2410 | 1.1533 | 55.6793 | 58.3728 | 56.0708 | 39.5451 | 41.3271 | 40.0375 | 48.2426 | 50.4432 | 48.7276 |
| 1.4029 | 3.0 | 3615 | 1.1242 | 56.6985 | 56.856 | 59.0696 | 39.961 | 39.8776 | 41.9066 | 48.8833 | 48.9108 | 51.0644 |
| 1.4023 | 4.0 | 4820 | 1.1164 | 56.7956 | 57.5473 | 58.6205 | 40.1594 | 40.4966 | 41.7103 | 49.1004 | 49.6461 | 50.8065 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2