---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
datasets:
- xlsum
metrics:
- rouge
model-index:
- name: mt5-small-xlsum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: xlsum
      type: xlsum
      config: ukrainian
      split: train
      args: ukrainian
    metrics:
    - name: Rouge1
      type: rouge
      value: 1.1945
---

<!-- 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-xlsum

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8395
- Rouge1: 1.1945
- Rouge2: 0.1467
- Rougel: 1.1902
- Rougelsum: 1.196

## 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 11.9992       | 1.0   | 125  | 4.0495          | 0.3829 | 0.0    | 0.3905 | 0.3905    |
| 5.8176        | 2.0   | 250  | 3.3431          | 0.491  | 0.0667 | 0.4988 | 0.4821    |
| 4.9907        | 3.0   | 375  | 3.1548          | 0.6481 | 0.08   | 0.6766 | 0.6655    |
| 4.6486        | 4.0   | 500  | 3.0347          | 1.0105 | 0.1467 | 1.0398 | 1.0274    |
| 4.4541        | 5.0   | 625  | 2.9414          | 0.9581 | 0.1467 | 0.951  | 0.9643    |
| 4.3195        | 6.0   | 750  | 2.8837          | 1.1129 | 0.1467 | 1.1245 | 1.1193    |
| 4.2618        | 7.0   | 875  | 2.8473          | 1.1019 | 0.1467 | 1.1048 | 1.1224    |
| 4.2228        | 8.0   | 1000 | 2.8395          | 1.1945 | 0.1467 | 1.1902 | 1.196     |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1