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# 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
{"language": ["en", "ko"], "license": "apache-2.0", "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"], "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"}], "base_model": "KETI-AIR/long-ke-t5-base", "model-index": [{"name": "en2ko", "results": [{"task": {"type": "translation", "name": "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": [{"type": "bleu", "value": 42.463, "name": "Bleu"}]}]}]}
transformers pytorch safetensors longt5 text2text-generation generated_from_trainer translation en ko base_model:KETI-AIR/long-ke-t5-base license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #longt5 #text2text-generation #generated_from_trainer #translation #en #ko #base_model-KETI-AIR/long-ke-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

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



### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.0
* Datasets 2.8.0
* Tokenizers 0.13.2



### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.0
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #safetensors #longt5 #text2text-generation #generated_from_trainer #translation #en #ko #base_model-KETI-AIR/long-ke-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.0
* Datasets 2.8.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #safetensors #longt5 #text2text-generation #generated_from_trainer #translation #en #ko #base_model-KETI-AIR/long-ke-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


* Transformers 4.25.1
* Pytorch 1.12.0
* Datasets 2.8.0
* Tokenizers 0.13.2