metadata

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

en2ko

This model is a fine-tuned version of 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