---
language:
- en
- ko
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation
metrics:
- bleu
pipeline_tag: translation
widget:
- text: 'translate_en2ko: The Seoul Metropolitan Government said Wednesday that it
    would develop an AI-based congestion monitoring system to provide better information
    to passengers about crowd density at each subway station.'
  example_title: Sample 1
- text: 'translate_en2ko: According to Seoul Metro, the operator of the subway service
    in Seoul, the new service will help analyze the real-time flow of passengers and
    crowd levels in subway compartments, improving operational efficiency.'
  example_title: Sample 2
base_model: KETI-AIR/long-ke-t5-base
model-index:
- name: en2ko
  results:
  - task:
      type: translation
      name: Translation
    dataset:
      name: KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation
        koen,none,none,none,none
      type: KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation
      args: koen,none,none,none,none
    metrics:
    - type: bleu
      value: 42.463
      name: Bleu
---

<!-- 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. -->

# en2ko

This model is a fine-tuned version of [KETI-AIR/long-ke-t5-base](https://huggingface.co/KETI-AIR/long-ke-t5-base) on the KETI-AIR/aihub_koenzh_food_translation,KETI-AIR/aihub_scitech_translation,KETI-AIR/aihub_scitech20_translation,KETI-AIR/aihub_socialtech20_translation,KETI-AIR/aihub_spoken_language_translation koen,none,none,none,none dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6000
- Bleu: 42.463
- Gen Len: 30.6512

## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Bleu    | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 0.6989        | 1.0   | 93762  | 0.6666          | 20.3697 | 18.1258 |
| 0.6143        | 2.0   | 187524 | 0.6181          | 21.2903 | 18.1428 |
| 0.5544        | 3.0   | 281286 | 0.6000          | 21.9763 | 18.1424 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.0
- Datasets 2.8.0
- Tokenizers 0.13.2