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--- |
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language: |
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- ko |
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- en |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- 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 |
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metrics: |
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- bleu |
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pipeline_tag: translation |
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widget: |
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- text: 'translate_ko2en: IBM 왓슨X는 AI 및 데이터 플랫폼이다. 신뢰할 수 있는 데이터, 속도, 거버넌스를 갖고 파운데이션 |
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모델 및 머신 러닝 기능을 포함한 AI 모델을 학습시키고, 조정해, 조직 전체에서 활용하기 위한 전 과정을 아우르는 기술과 서비스를 제공한다.' |
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example_title: Sample 1 |
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- text: 'translate_ko2en: 이용자는 신뢰할 수 있고 개방된 환경에서 자신의 데이터에 대해 자체적인 AI를 구축하거나, 시장에 출시된 |
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AI 모델을 정교하게 조정할 수 있다. 대규모로 활용하기 위한 도구 세트, 기술, 인프라 및 전문 컨설팅 서비스를 활용할 수 있다.' |
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example_title: Sample 2 |
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base_model: KETI-AIR/long-ke-t5-base |
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model-index: |
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- name: ko2en |
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results: |
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- task: |
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type: translation |
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name: Translation |
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dataset: |
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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 |
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koen,none,none,none,none |
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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 |
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args: koen,none,none,none,none |
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metrics: |
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- type: bleu |
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value: 58.7008 |
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name: Bleu |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# ko2en |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5186 |
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- Bleu: 58.7008 |
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- Gen Len: 27.0073 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- total_train_batch_size: 128 |
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- total_eval_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len | |
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|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:| |
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| 0.6234 | 1.0 | 93762 | 0.5843 | 33.9843 | 17.5378 | |
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| 0.5334 | 2.0 | 187524 | 0.5369 | 35.3271 | 17.5388 | |
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| 0.4704 | 3.0 | 281286 | 0.5186 | 36.0533 | 17.5335 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
