
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# ko2en

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: 0.5186
- Bleu: 58.7008
- Gen Len: 27.0073

## 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 |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 0.6234        | 1.0   | 93762  | 0.5843          | 33.9843 | 17.5378 |
| 0.5334        | 2.0   | 187524 | 0.5369          | 35.3271 | 17.5388 |
| 0.4704        | 3.0   | 281286 | 0.5186          | 36.0533 | 17.5335 |


### 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": "Sample 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": "Sample 2"}], "base_model": "KETI-AIR/long-ke-t5-base", "model-index": [{"name": "ko2en", "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": 58.7008, "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 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 #region-us 

ko2en
=====


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: 0.5186
* Bleu: 58.7008
* Gen Len: 27.0073


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 #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 #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