---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-mt5-small-v2
  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-finetuned-mt5-small-v2

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: 2.3614
- Rouge1: 0.1151
- Rouge2: 0.0251
- Rougel: 0.1143
- Rougelsum: 0.1144

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.5787        | 1.0   | 12500  | 2.7685          | 0.0863 | 0.0192 | 0.0858 | 0.0857    |
| 3.036         | 2.0   | 25000  | 2.6270          | 0.0911 | 0.0203 | 0.0905 | 0.0905    |
| 2.8761        | 3.0   | 37500  | 2.5564          | 0.101  | 0.0233 | 0.1004 | 0.1004    |
| 2.7709        | 4.0   | 50000  | 2.5080          | 0.1034 | 0.0231 | 0.1028 | 0.1028    |
| 2.6959        | 5.0   | 62500  | 2.4671          | 0.1068 | 0.0235 | 0.1061 | 0.1062    |
| 2.6328        | 6.0   | 75000  | 2.4539          | 0.11   | 0.026  | 0.1093 | 0.1093    |
| 2.5839        | 7.0   | 87500  | 2.4302          | 0.1101 | 0.0261 | 0.1092 | 0.1093    |
| 2.5418        | 8.0   | 100000 | 2.4083          | 0.1113 | 0.0252 | 0.1106 | 0.1108    |
| 2.5067        | 9.0   | 112500 | 2.3999          | 0.1115 | 0.0257 | 0.1107 | 0.1106    |
| 2.4762        | 10.0  | 125000 | 2.3857          | 0.1161 | 0.0264 | 0.1153 | 0.1153    |
| 2.4505        | 11.0  | 137500 | 2.3741          | 0.1141 | 0.0262 | 0.1133 | 0.1134    |
| 2.4281        | 12.0  | 150000 | 2.3737          | 0.1153 | 0.0259 | 0.1146 | 0.1147    |
| 2.4103        | 13.0  | 162500 | 2.3648          | 0.1156 | 0.0255 | 0.1148 | 0.1147    |
| 2.3961        | 14.0  | 175000 | 2.3652          | 0.1131 | 0.0246 | 0.1123 | 0.1123    |
| 2.3837        | 15.0  | 187500 | 2.3636          | 0.1141 | 0.0255 | 0.1133 | 0.1134    |
| 2.3772        | 16.0  | 200000 | 2.3614          | 0.1151 | 0.0251 | 0.1143 | 0.1144    |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.1.0+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1