---
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