đ en2ko
This model is a fine - tuned translation model that can effectively convert English to Korean, trained on multiple high - quality datasets to provide accurate translation results.
đ Quick Start
You can use the following examples to quickly test the translation capabilities of this model:
Sample 1
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.
Sample 2
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.
⨠Features
- Fine - tuned Model: It is a fine - tuned version of [KETI - AIR/long - ke - t5 - base](https://huggingface.co/KETI - AIR/long - ke - t5 - base), which can better adapt to the English - to - Korean translation task.
- Multiple Datasets: Trained on multiple high - quality translation datasets, including KETI - AIR/aihub_koenzh_food_translation, KETI - AIR/aihub_scitech_translation, etc., to ensure the diversity and accuracy of translation.
- Evaluation Metrics: Evaluated by BLEU metric, achieving a BLEU score of 42.463, indicating high - quality translation results.
đĻ Installation
No specific installation steps are provided in the original document.
đ Documentation
Model Information
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 following 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
Evaluation Results
It achieves the following results on the evaluation set:
- Loss: 0.6000
- Bleu: 42.463
- Gen Len: 30.6512
Training Details
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
đ License
This model is licensed under the Apache - 2.0 license.
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