transformers pytorch mt5 text2text-generation summarization generated_from_trainer dataset:xlsum base_model:kravchenko/uk-mt5-base base_model:finetune:kravchenko/uk-mt5-base model-index autotrain_compatible endpoints_compatible region:us
---
base_model: kravchenko/uk-mt5-base
datasets:
- xlsum
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: uk-mt5-base-xlsum-v2
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: xlsum
      type: xlsum
      config: ukrainian
      split: validation
      args: ukrainian
    metrics:
    - type: rouge
      value: 4.4311
      name: Rouge1
---

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# uk-mt5-base-xlsum-v2

This model is a fine-tuned version of [kravchenko/uk-mt5-base](https://huggingface.co/kravchenko/uk-mt5-base) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0401
- Rouge1: 4.4311
- Rouge2: 0.8944
- Rougel: 4.4294
- Rougelsum: 4.4527

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 3.2519        | 1.0   | 2000  | 2.0993          | 4.1141 | 0.5944 | 4.1014 | 4.11      |
| 2.5587        | 2.0   | 4000  | 2.0428          | 4.5015 | 0.6167 | 4.4863 | 4.518     |
| 2.3299        | 3.0   | 6000  | 2.0175          | 4.4642 | 1.0833 | 4.4528 | 4.5167    |
| 2.1543        | 4.0   | 8000  | 2.0183          | 4.3294 | 0.9444 | 4.3408 | 4.3611    |
| 2.0276        | 5.0   | 10000 | 2.0039          | 4.6694 | 0.9444 | 4.6264 | 4.6527    |
| 1.9119        | 6.0   | 12000 | 2.0139          | 4.9447 | 1.0675 | 4.8908 | 4.9633    |
| 1.8305        | 7.0   | 14000 | 2.0134          | 4.9385 | 1.1595 | 4.8774 | 4.9294    |
| 1.7669        | 8.0   | 16000 | 2.0253          | 4.2697 | 0.9667 | 4.2524 | 4.3167    |
| 1.7141        | 9.0   | 18000 | 2.0354          | 4.4527 | 0.9    | 4.448  | 4.4941    |
| 1.681         | 10.0  | 20000 | 2.0401          | 4.4311 | 0.8944 | 4.4294 | 4.4527    |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
