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--- |
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language: |
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- en |
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- ko |
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tags: |
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- generated_from_trainer |
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datasets: |
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- >- |
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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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model-index: |
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- name: en2ko |
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results: |
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- task: |
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name: Translation |
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type: translation |
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dataset: |
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name: >- |
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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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koen,none,none,none,none |
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type: >- |
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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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args: koen,none,none,none,none |
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metrics: |
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- name: Bleu |
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type: bleu |
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value: 42.463 |
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license: apache-2.0 |
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pipeline_tag: translation |
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widget: |
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- 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." |
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example_title: "Sample 1" |
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- 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." |
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example_title: "Sample 2" |
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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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# en2ko |
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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: 0.6000 |
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- Bleu: 42.463 |
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- Gen Len: 30.6512 |
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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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| 0.6989 | 1.0 | 93762 | 0.6666 | 20.3697 | 18.1258 | |
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| 0.6143 | 2.0 | 187524 | 0.6181 | 21.2903 | 18.1428 | |
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| 0.5544 | 3.0 | 281286 | 0.6000 | 21.9763 | 18.1424 | |
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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 |
