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---
library_name: transformers
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-mt5-finetuned-final
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-mt5-finetuned-final
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: 1.1778
- Rouge1: 0.2833
- Rouge2: 0.1521
- Rougel: 0.2758
- Rougelsum: 0.2768
## 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: 0.0056
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 5.9607 | 1.0 | 100 | 4.8449 | 0.1763 | 0.0684 | 0.1763 | 0.1761 |
| 4.9088 | 2.0 | 200 | 3.9878 | 0.3076 | 0.1348 | 0.2803 | 0.2815 |
| 2.9924 | 3.0 | 300 | 2.2397 | 0.2790 | 0.1378 | 0.2575 | 0.2592 |
| 2.2734 | 4.0 | 400 | 1.9866 | 0.2987 | 0.1629 | 0.2868 | 0.2872 |
| 1.9431 | 5.0 | 500 | 1.7408 | 0.2251 | 0.1380 | 0.2231 | 0.2237 |
| 2.317 | 6.0 | 600 | 1.9235 | 0.2421 | 0.0922 | 0.2276 | 0.2282 |
| 1.8526 | 7.0 | 700 | 1.6342 | 0.3120 | 0.1636 | 0.2943 | 0.2944 |
| 1.7029 | 8.0 | 800 | 1.6244 | 0.2469 | 0.1361 | 0.2421 | 0.2427 |
| 1.6725 | 9.0 | 900 | 1.5803 | 0.2637 | 0.1362 | 0.2551 | 0.2560 |
| 1.5852 | 10.0 | 1000 | 1.5617 | 0.2963 | 0.1634 | 0.2907 | 0.2917 |
| 1.4625 | 11.0 | 1100 | 1.4049 | 0.2750 | 0.1383 | 0.2570 | 0.2576 |
| 1.3895 | 12.0 | 1200 | 1.4234 | 0.2969 | 0.1646 | 0.2917 | 0.2927 |
| 1.3584 | 13.0 | 1300 | 1.3807 | 0.3370 | 0.1601 | 0.3088 | 0.3099 |
| 1.2759 | 14.0 | 1400 | 1.3524 | 0.2890 | 0.1307 | 0.2654 | 0.2663 |
| 1.222 | 15.0 | 1500 | 1.3110 | 0.2718 | 0.1339 | 0.2566 | 0.2597 |
| 1.1515 | 16.0 | 1600 | 1.2297 | 0.3314 | 0.1626 | 0.3033 | 0.3038 |
| 1.0888 | 17.0 | 1700 | 1.1897 | 0.3028 | 0.1358 | 0.2769 | 0.2792 |
| 1.039 | 18.0 | 1800 | 1.1970 | 0.2833 | 0.1521 | 0.2758 | 0.2768 |
| 0.9907 | 19.0 | 1900 | 1.1790 | 0.2833 | 0.1521 | 0.2758 | 0.2768 |
| 0.9563 | 20.0 | 2000 | 1.1778 | 0.2833 | 0.1521 | 0.2758 | 0.2768 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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