
<!-- 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. -->

# SCRATCH_ja-en_helsinki

This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5583
- Otaku Benchmark VN BLEU: 19.12
- Otaku Benchmark LN BLEU: 11.55
- Otaku Benchmark MANGA BLEU: 12.98

## 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: 0.0003
- train_batch_size: 96
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 3.0252        | 0.02  | 2000   | 2.4140          |
| 2.8406        | 0.03  | 4000   | 2.2819          |
| 2.7505        | 0.05  | 6000   | 2.3018          |
| 2.6948        | 0.06  | 8000   | 2.1931          |
| 2.6408        | 0.08  | 10000  | 2.1724          |
| 2.6004        | 0.09  | 12000  | 2.1583          |
| 2.5685        | 0.11  | 14000  | 2.1203          |
| 2.5432        | 0.12  | 16000  | 2.1593          |
| 2.5153        | 0.14  | 18000  | 2.1009          |
| 2.4906        | 0.15  | 20000  | 2.0899          |
| 2.4709        | 0.17  | 22000  | 2.0512          |
| 2.4471        | 0.18  | 24000  | 2.0208          |
| 2.4295        | 0.2   | 26000  | 2.0773          |
| 2.4154        | 0.21  | 28000  | 2.0441          |
| 2.4008        | 0.23  | 30000  | 2.0235          |
| 2.3834        | 0.24  | 32000  | 2.0190          |
| 2.3709        | 0.26  | 34000  | 1.9831          |
| 2.3537        | 0.27  | 36000  | 1.9870          |
| 2.3486        | 0.29  | 38000  | 1.9692          |
| 2.3346        | 0.3   | 40000  | 1.9517          |
| 2.3195        | 0.32  | 42000  | 1.9800          |
| 2.3104        | 0.33  | 44000  | 1.9676          |
| 2.298         | 0.35  | 46000  | 1.9563          |
| 2.2905        | 0.36  | 48000  | 1.9217          |
| 2.2792        | 0.38  | 50000  | 1.9195          |
| 2.2714        | 0.39  | 52000  | 1.9109          |
| 2.2593        | 0.41  | 54000  | 1.9044          |
| 2.2582        | 0.42  | 56000  | 1.8876          |
| 2.2482        | 0.44  | 58000  | 1.8860          |
| 2.2394        | 0.45  | 60000  | 1.8887          |
| 2.2273        | 0.47  | 62000  | 1.8862          |
| 2.2255        | 0.48  | 64000  | 1.8705          |
| 2.2166        | 0.5   | 66000  | 1.8696          |
| 2.2075        | 0.51  | 68000  | 1.8657          |
| 2.1992        | 0.53  | 70000  | 1.8585          |
| 2.1969        | 0.54  | 72000  | 1.8526          |
| 2.1894        | 0.56  | 74000  | 1.8493          |
| 2.1817        | 0.57  | 76000  | 1.8480          |
| 2.1771        | 0.59  | 78000  | 1.8333          |
| 2.1683        | 0.6   | 80000  | 1.8342          |
| 2.1667        | 0.62  | 82000  | 1.8537          |
| 2.1546        | 0.63  | 84000  | 1.8261          |
| 2.1467        | 0.65  | 86000  | 1.8092          |
| 2.1421        | 0.66  | 88000  | 1.8137          |
| 2.1395        | 0.68  | 90000  | 1.8286          |
| 2.1313        | 0.69  | 92000  | 1.8042          |
| 2.1241        | 0.71  | 94000  | 1.7934          |
| 2.1214        | 0.72  | 96000  | 1.7940          |
| 2.12          | 0.74  | 98000  | 1.8064          |
| 2.1096        | 0.75  | 100000 | 1.7983          |
| 2.1035        | 0.77  | 102000 | 1.8089          |
| 2.0937        | 0.78  | 104000 | 1.7941          |
| 2.0893        | 0.8   | 106000 | 1.7791          |
| 2.0869        | 0.81  | 108000 | 1.7807          |
| 2.0845        | 0.83  | 110000 | 1.7852          |
| 2.0782        | 0.84  | 112000 | 1.7675          |
| 2.0755        | 0.86  | 114000 | 1.7756          |
| 2.0657        | 0.87  | 116000 | 1.7604          |
| 2.0614        | 0.89  | 118000 | 1.7447          |
| 2.0591        | 0.9   | 120000 | 1.7489          |
| 2.0586        | 0.92  | 122000 | 1.7550          |
| 2.0498        | 0.93  | 124000 | 1.7543          |
| 2.0455        | 0.95  | 126000 | 1.7510          |
| 2.04          | 0.96  | 128000 | 1.7439          |
| 2.0385        | 0.98  | 130000 | 1.7407          |
| 2.0267        | 0.99  | 132000 | 1.7467          |
| 2.0088        | 1.01  | 134000 | 1.7455          |
| 1.9826        | 1.02  | 136000 | 1.7210          |
| 1.9785        | 1.04  | 138000 | 1.7524          |
| 1.9777        | 1.05  | 140000 | 1.7272          |
| 1.9763        | 1.07  | 142000 | 1.7283          |
| 1.9736        | 1.08  | 144000 | 1.7210          |
| 1.9704        | 1.1   | 146000 | 1.7001          |
| 1.9625        | 1.11  | 148000 | 1.7112          |
| 1.9665        | 1.13  | 150000 | 1.7236          |
| 1.9592        | 1.14  | 152000 | 1.7169          |
| 1.9606        | 1.16  | 154000 | 1.6962          |
| 1.9571        | 1.17  | 156000 | 1.7064          |
| 1.9532        | 1.19  | 158000 | 1.6898          |
| 1.9465        | 1.2   | 160000 | 1.7004          |
| 1.9438        | 1.22  | 162000 | 1.7092          |
| 1.9435        | 1.23  | 164000 | 1.6927          |
| 1.9361        | 1.25  | 166000 | 1.6838          |
| 1.9369        | 1.26  | 168000 | 1.6784          |
| 1.9287        | 1.28  | 170000 | 1.6709          |
| 1.928         | 1.29  | 172000 | 1.6735          |
| 1.9227        | 1.31  | 174000 | 1.6689          |
| 1.9213        | 1.32  | 176000 | 1.6685          |
| 1.9152        | 1.34  | 178000 | 1.6635          |
| 1.9092        | 1.35  | 180000 | 1.6561          |
| 1.9059        | 1.37  | 182000 | 1.6673          |
| 1.9094        | 1.38  | 184000 | 1.6717          |
| 1.9006        | 1.4   | 186000 | 1.6593          |
| 1.8956        | 1.41  | 188000 | 1.6483          |
| 1.8972        | 1.43  | 190000 | 1.6635          |
| 1.8907        | 1.44  | 192000 | 1.6604          |
| 1.8885        | 1.46  | 194000 | 1.6465          |
| 1.8844        | 1.47  | 196000 | 1.6444          |
| 1.8799        | 1.49  | 198000 | 1.6307          |
| 1.8813        | 1.5   | 200000 | 1.6240          |
| 1.8693        | 1.52  | 202000 | 1.6102          |
| 1.8768        | 1.53  | 204000 | 1.6197          |
| 1.8678        | 1.55  | 206000 | 1.6275          |
| 1.8588        | 1.56  | 208000 | 1.6183          |
| 1.8585        | 1.58  | 210000 | 1.6197          |
| 1.8564        | 1.59  | 212000 | 1.6004          |
| 1.8493        | 1.61  | 214000 | 1.6078          |
| 1.85          | 1.62  | 216000 | 1.6001          |
| 1.8428        | 1.64  | 218000 | 1.6106          |
| 1.8428        | 1.65  | 220000 | 1.5866          |
| 1.8423        | 1.67  | 222000 | 1.5993          |
| 1.8352        | 1.68  | 224000 | 1.6052          |
| 1.8385        | 1.7   | 226000 | 1.5959          |
| 1.8307        | 1.71  | 228000 | 1.6024          |
| 1.8248        | 1.73  | 230000 | 1.5969          |
| 1.82          | 1.74  | 232000 | 1.5878          |
| 1.8254        | 1.76  | 234000 | 1.5934          |
| 1.8188        | 1.77  | 236000 | 1.5827          |
| 1.813         | 1.79  | 238000 | 1.5797          |
| 1.8128        | 1.8   | 240000 | 1.5758          |
| 1.8044        | 1.82  | 242000 | 1.5752          |
| 1.808         | 1.83  | 244000 | 1.5818          |
| 1.8025        | 1.85  | 246000 | 1.5772          |
| 1.7992        | 1.86  | 248000 | 1.5738          |
| 1.8021        | 1.88  | 250000 | 1.5752          |
| 1.7988        | 1.89  | 252000 | 1.5717          |
| 1.7967        | 1.91  | 254000 | 1.5690          |
| 1.7909        | 1.92  | 256000 | 1.5607          |
| 1.7942        | 1.94  | 258000 | 1.5618          |
| 1.7897        | 1.95  | 260000 | 1.5585          |
| 1.7871        | 1.97  | 262000 | 1.5576          |
| 1.7843        | 1.98  | 264000 | 1.5577          |
| 1.7888        | 2.0   | 266000 | 1.5583          |


### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "SCRATCH_ja-en_helsinki", "results": []}]}
transformers pytorch marian text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

SCRATCH\_ja-en\_helsinki
========================


This model is a fine-tuned version of Helsinki-NLP/opus-mt-ja-en on an unknown dataset.
It achieves the following results on the evaluation set:


* Loss: 1.5583
* Otaku Benchmark VN BLEU: 19.12
* Otaku Benchmark LN BLEU: 11.55
* Otaku Benchmark MANGA BLEU: 12.98


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: 0.0003
* train\_batch\_size: 96
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 96
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 96
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #marian #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0003
* train\_batch\_size: 96
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 2
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.19.2
* Pytorch 1.11.0+cu113
* Datasets 2.2.2
* Tokenizers 0.12.1