|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: google/mt5-small |
|
tags: |
|
- summarization |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
model-index: |
|
- name: mt5-small-synthetic-data2 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# mt5-small-synthetic-data2 |
|
|
|
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.8493 |
|
- Rouge1: 0.5864 |
|
- Rouge2: 0.4472 |
|
- Rougel: 0.5690 |
|
- Rougelsum: 0.5675 |
|
|
|
## 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: 5.6e-05 |
|
- train_batch_size: 12 |
|
- eval_batch_size: 12 |
|
- 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: linear |
|
- num_epochs: 40 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
|
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
|
| 18.931 | 1.0 | 50 | 9.0942 | 0.0 | 0.0 | 0.0 | 0.0 | |
|
| 11.3652 | 2.0 | 100 | 5.2268 | 0.0025 | 0.0014 | 0.0025 | 0.0025 | |
|
| 7.6242 | 3.0 | 150 | 3.0583 | 0.0755 | 0.0247 | 0.0712 | 0.0697 | |
|
| 5.0038 | 4.0 | 200 | 2.1559 | 0.1720 | 0.0608 | 0.1415 | 0.1411 | |
|
| 3.4385 | 5.0 | 250 | 1.6094 | 0.2058 | 0.0858 | 0.1798 | 0.1801 | |
|
| 2.7359 | 6.0 | 300 | 1.4043 | 0.3742 | 0.2353 | 0.3549 | 0.3574 | |
|
| 2.2687 | 7.0 | 350 | 1.2929 | 0.4226 | 0.2639 | 0.3944 | 0.3962 | |
|
| 2.0252 | 8.0 | 400 | 1.2258 | 0.4436 | 0.2820 | 0.4129 | 0.4159 | |
|
| 1.8135 | 9.0 | 450 | 1.1667 | 0.4529 | 0.2932 | 0.4160 | 0.4176 | |
|
| 1.7448 | 10.0 | 500 | 1.1103 | 0.4729 | 0.3152 | 0.4391 | 0.4409 | |
|
| 1.5793 | 11.0 | 550 | 1.0840 | 0.5045 | 0.3557 | 0.4774 | 0.4787 | |
|
| 1.5258 | 12.0 | 600 | 1.0532 | 0.5266 | 0.3857 | 0.5053 | 0.5061 | |
|
| 1.4391 | 13.0 | 650 | 1.0176 | 0.5507 | 0.4182 | 0.5381 | 0.5367 | |
|
| 1.3783 | 14.0 | 700 | 1.0015 | 0.5595 | 0.4233 | 0.5387 | 0.5386 | |
|
| 1.318 | 15.0 | 750 | 0.9825 | 0.5699 | 0.4260 | 0.5476 | 0.5468 | |
|
| 1.2871 | 16.0 | 800 | 0.9581 | 0.5785 | 0.4334 | 0.5564 | 0.5554 | |
|
| 1.2305 | 17.0 | 850 | 0.9489 | 0.5766 | 0.4343 | 0.5540 | 0.5538 | |
|
| 1.2609 | 18.0 | 900 | 0.9362 | 0.5853 | 0.4432 | 0.5633 | 0.5633 | |
|
| 1.1928 | 19.0 | 950 | 0.9256 | 0.5847 | 0.4438 | 0.5637 | 0.5635 | |
|
| 1.1165 | 20.0 | 1000 | 0.9186 | 0.5712 | 0.4331 | 0.5535 | 0.5535 | |
|
| 1.1624 | 21.0 | 1050 | 0.9080 | 0.5763 | 0.4434 | 0.5581 | 0.5586 | |
|
| 1.0909 | 22.0 | 1100 | 0.9040 | 0.5774 | 0.4417 | 0.5596 | 0.5604 | |
|
| 1.0885 | 23.0 | 1150 | 0.8969 | 0.5827 | 0.4465 | 0.5642 | 0.5646 | |
|
| 1.1378 | 24.0 | 1200 | 0.8933 | 0.5855 | 0.4476 | 0.5668 | 0.5663 | |
|
| 0.9968 | 25.0 | 1250 | 0.8832 | 0.5851 | 0.4467 | 0.5664 | 0.5659 | |
|
| 1.0871 | 26.0 | 1300 | 0.8776 | 0.5848 | 0.4460 | 0.5661 | 0.5659 | |
|
| 1.0546 | 27.0 | 1350 | 0.8749 | 0.5825 | 0.4443 | 0.5635 | 0.5630 | |
|
| 0.9935 | 28.0 | 1400 | 0.8687 | 0.5842 | 0.4467 | 0.5682 | 0.5678 | |
|
| 1.0042 | 29.0 | 1450 | 0.8661 | 0.5834 | 0.4466 | 0.5669 | 0.5666 | |
|
| 0.9903 | 30.0 | 1500 | 0.8628 | 0.5843 | 0.4485 | 0.5655 | 0.5653 | |
|
| 0.9701 | 31.0 | 1550 | 0.8583 | 0.5822 | 0.4436 | 0.5630 | 0.5629 | |
|
| 0.9585 | 32.0 | 1600 | 0.8552 | 0.5783 | 0.4405 | 0.5610 | 0.5605 | |
|
| 0.9412 | 33.0 | 1650 | 0.8555 | 0.5897 | 0.4492 | 0.5696 | 0.5687 | |
|
| 0.9732 | 34.0 | 1700 | 0.8526 | 0.5853 | 0.4477 | 0.5661 | 0.5655 | |
|
| 0.9248 | 35.0 | 1750 | 0.8535 | 0.5828 | 0.4429 | 0.5646 | 0.5637 | |
|
| 0.9408 | 36.0 | 1800 | 0.8520 | 0.5868 | 0.4474 | 0.5695 | 0.5680 | |
|
| 0.9951 | 37.0 | 1850 | 0.8506 | 0.5834 | 0.4456 | 0.5656 | 0.5645 | |
|
| 0.9316 | 38.0 | 1900 | 0.8500 | 0.5846 | 0.4470 | 0.5667 | 0.5657 | |
|
| 0.9339 | 39.0 | 1950 | 0.8495 | 0.5864 | 0.4472 | 0.5690 | 0.5675 | |
|
| 0.9519 | 40.0 | 2000 | 0.8493 | 0.5864 | 0.4472 | 0.5690 | 0.5675 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.47.1 |
|
- Pytorch 2.5.1+cu121 |
|
- Datasets 3.2.0 |
|
- Tokenizers 0.21.0 |
|
|
