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: 1.2368
- Rouge1 Precision: 61.1637
- Rouge1 Recall: 50.8564
- Rouge1 F1: 53.4468
- Rouge2 Precision: 43.7489
- Rouge2 Recall: 36.4625
- Rouge2 F1: 38.2205
- Rougel Precision: 53.1324
- Rougel Recall: 44.3405
- Rougel F1: 46.5079
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- 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 Precision | Rouge1 Recall | Rouge1 F1 | Rouge2 Precision | Rouge2 Recall | Rouge2 F1 | Rougel Precision | Rougel Recall | Rougel F1 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2.4339 | 1.0 | 302 | 1.3726 | 52.4552 | 30.2138 | 35.7525 | 34.2706 | 19.5416 | 23.154 | 45.1553 | 25.9579 | 30.6864 |
| 1.7536 | 2.0 | 604 | 1.2656 | 61.595 | 48.0181 | 51.6818 | 44.1499 | 34.4709 | 37.0359 | 53.7054 | 41.9632 | 45.111 |
| 1.644 | 3.0 | 906 | 1.2390 | 61.3387 | 50.3584 | 53.2007 | 43.9459 | 36.1575 | 38.0967 | 53.3489 | 43.9572 | 46.3486 |
| 1.6269 | 4.0 | 1208 | 1.2368 | 61.1637 | 50.8564 | 53.4468 | 43.7489 | 36.4625 | 38.2205 | 53.1324 | 44.3405 | 46.5079 |
Framework versions
- Transformers 4.57.6
- Pytorch 2.11.0+cu128
- Datasets 4.8.5
- Tokenizers 0.22.2