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chunwoolee0
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mt5_small_kde4_en_ko

Text2Text Generation
Transformers
PyTorch
mt5
Generated from Trainer
Eval Results
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mt5_small_kde4_en_ko
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---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
datasets:
- kde4
metrics:
- rouge
- sacrebleu
model-index:
- name: mt5_small_kde4_en_ko
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: kde4
      type: kde4
      config: en-ko
      split: train
      args: en-ko
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.0832
    - name: Sacrebleu
      type: sacrebleu
      value: 3.3559
---

<!-- 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_kde4_en_ko

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1644
- Rouge1: 0.0832
- Rouge2: 0.0195
- Rougel: 0.0826
- Sacrebleu: 3.3559

## Model description

This model tries to achieve translation from English to Korean using google's mt5 multilingual model.

## Intended uses & limitations

Translation from English to Korean

## Usage

You can use this model directly with a pipeline for translation language modeling:

```python
>>> from transformers import pipeline
>>> translator = pipeline('translation', model='chunwoolee0/ke_t5_base_bongsoo_en_ko')

>>> translator("Let us go for a walk after lunch.")
[{'translation_text': '오류를 방문하십시오.'}]

 The translation fails completely.

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Sacrebleu |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 15.8735       | 0.46  | 500  | 6.5322          | 0.0101 | 0.0004 | 0.0102 | 0.464     |
| 7.183         | 0.93  | 1000 | 4.2298          | 0.0203 | 0.0012 | 0.02   | 0.6102    |
| 5.4447        | 1.39  | 1500 | 3.5600          | 0.0399 | 0.005  | 0.0396 | 1.5798    |
| 4.8372        | 1.85  | 2000 | 3.3343          | 0.0537 | 0.0088 | 0.0533 | 3.0115    |
| 4.5579        | 2.32  | 2500 | 3.2131          | 0.0732 | 0.016  | 0.0729 | 3.3743    |
| 4.4532        | 2.78  | 3000 | 3.1644          | 0.0832 | 0.0195 | 0.0826 | 3.3559    |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3