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---
language:
- en
- ko
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
model-index:
- name: en2ko
results:
- task:
name: Translation
type: 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:
- name: Bleu
type: bleu
value: 42.463
license: apache-2.0
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"
---
<!-- 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 |