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# ko2en_bidirection2

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: 0.5716
- Bleu: 51.5949
- Gen Len: 28.8348

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 0.7004        | 1.0   | 187524 | 0.6461          | 28.0622 | 17.8368 |
| 0.6176        | 2.0   | 375048 | 0.5967          | 29.3033 | 17.8281 |
| 0.5642        | 3.0   | 562572 | 0.5716          | 30.0045 | 17.8366 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.12.0
- Datasets 2.8.0
- Tokenizers 0.13.2
{"language": ["ko", "en"], "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_ko2en: IBM \uc653\uc2a8X\ub294 AI \ubc0f \ub370\uc774\ud130 \ud50c\ub7ab\ud3fc\uc774\ub2e4. \uc2e0\ub8b0\ud560 \uc218 \uc788\ub294 \ub370\uc774\ud130, \uc18d\ub3c4, \uac70\ubc84\ub10c\uc2a4\ub97c \uac16\uace0 \ud30c\uc6b4\ub370\uc774\uc158 \ubaa8\ub378 \ubc0f \uba38\uc2e0 \ub7ec\ub2dd \uae30\ub2a5\uc744 \ud3ec\ud568\ud55c AI \ubaa8\ub378\uc744 \ud559\uc2b5\uc2dc\ud0a4\uace0, \uc870\uc815\ud574, \uc870\uc9c1 \uc804\uccb4\uc5d0\uc11c \ud65c\uc6a9\ud558\uae30 \uc704\ud55c \uc804 \uacfc\uc815\uc744 \uc544\uc6b0\ub974\ub294 \uae30\uc220\uacfc \uc11c\ube44\uc2a4\ub97c \uc81c\uacf5\ud55c\ub2e4.", "example_title": "KO2EN 1"}, {"text": "translate_ko2en: \uc774\uc6a9\uc790\ub294 \uc2e0\ub8b0\ud560 \uc218 \uc788\uace0 \uac1c\ubc29\ub41c \ud658\uacbd\uc5d0\uc11c \uc790\uc2e0\uc758 \ub370\uc774\ud130\uc5d0 \ub300\ud574 \uc790\uccb4\uc801\uc778 AI\ub97c \uad6c\ucd95\ud558\uac70\ub098, \uc2dc\uc7a5\uc5d0 \ucd9c\uc2dc\ub41c AI \ubaa8\ub378\uc744 \uc815\uad50\ud558\uac8c \uc870\uc815\ud560 \uc218 \uc788\ub2e4. \ub300\uaddc\ubaa8\ub85c \ud65c\uc6a9\ud558\uae30 \uc704\ud55c \ub3c4\uad6c \uc138\ud2b8, \uae30\uc220, \uc778\ud504\ub77c \ubc0f \uc804\ubb38 \ucee8\uc124\ud305 \uc11c\ube44\uc2a4\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\ub2e4.", "example_title": "KO2EN 2"}, {"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": "EN2KO 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": "EN2KO 2"}], "base_model": "KETI-AIR/long-ke-t5-base", "model-index": [{"name": "ko2en_bidirection2", "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": 51.5949, "name": "Bleu"}]}]}]}
transformers pytorch safetensors longt5 text2text-generation generated_from_trainer translation ko en base_model:KETI-AIR/long-ke-t5-base license:apache-2.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #longt5 #text2text-generation #generated_from_trainer #translation #ko #en #base_model-KETI-AIR/long-ke-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

ko2en\_bidirection2
===================


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: 0.5716
* Bleu: 51.5949
* Gen Len: 28.8348


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 #ko #en #base_model-KETI-AIR/long-ke-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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 #ko #en #base_model-KETI-AIR/long-ke-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #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