readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: MIX1_ja-en_helsinki
    results: []
  ---
  
  <!-- 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. -->
  
  # MIX1_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 a combination of Visual Novel, Light Novel, and Subtitle data. A total of ~10MM lines of training data were used.
  It achieves the following results on the evaluation set:
  - Loss: 1.7947
  - Otaku Benchmark VN BLEU: 17.78
  - Otaku Benchmark LN BLEU: 11.80
  - Otaku Benchmark MANGA BLEU: 13.66
  
  ## 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: 64
  - 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 |
  |:-------------:|:-----:|:------:|:---------------:|
  | 2.7495        | 0.01  | 2000   | 2.5989          |
  | 2.5415        | 0.03  | 4000   | 2.4746          |
  | 2.4409        | 0.04  | 6000   | 2.4731          |
  | 2.3743        | 0.05  | 8000   | 2.4012          |
  | 2.3254        | 0.06  | 10000  | 2.3904          |
  | 2.2857        | 0.08  | 12000  | 2.3649          |
  | 2.2448        | 0.09  | 14000  | 2.3188          |
  | 2.2158        | 0.1   | 16000  | 2.2975          |
  | 2.193         | 0.11  | 18000  | 2.2756          |
  | 2.1669        | 0.13  | 20000  | 2.2852          |
  | 2.144         | 0.14  | 22000  | 2.2689          |
  | 2.1222        | 0.15  | 24000  | 2.2721          |
  | 2.1045        | 0.16  | 26000  | 2.2489          |
  | 2.0885        | 0.18  | 28000  | 2.2359          |
  | 2.0732        | 0.19  | 30000  | 2.2771          |
  | 2.0584        | 0.2   | 32000  | 2.2582          |
  | 2.0471        | 0.21  | 34000  | 2.2093          |
  | 2.0369        | 0.23  | 36000  | 2.1768          |
  | 2.0241        | 0.24  | 38000  | 2.1884          |
  | 2.0196        | 0.25  | 40000  | 2.2025          |
  | 2.004         | 0.27  | 42000  | 2.1507          |
  | 1.9936        | 0.28  | 44000  | 2.1668          |
  | 1.9869        | 0.29  | 46000  | 2.1432          |
  | 1.9735        | 0.3   | 48000  | 2.1662          |
  | 1.9651        | 0.32  | 50000  | 2.1824          |
  | 1.9551        | 0.33  | 52000  | 2.1608          |
  | 1.9485        | 0.34  | 54000  | 2.1322          |
  | 1.9421        | 0.35  | 56000  | 2.1476          |
  | 1.9303        | 0.37  | 58000  | 2.0994          |
  | 1.9236        | 0.38  | 60000  | 2.1182          |
  | 1.9183        | 0.39  | 62000  | 2.1305          |
  | 1.9108        | 0.4   | 64000  | 2.1469          |
  | 1.9051        | 0.42  | 66000  | 2.1414          |
  | 1.9018        | 0.43  | 68000  | 2.1089          |
  | 1.8959        | 0.44  | 70000  | 2.0908          |
  | 1.886         | 0.46  | 72000  | 2.0968          |
  | 1.8802        | 0.47  | 74000  | 2.0503          |
  | 1.8713        | 0.48  | 76000  | 2.0542          |
  | 1.8648        | 0.49  | 78000  | 2.0990          |
  | 1.8599        | 0.51  | 80000  | 2.1112          |
  | 1.8563        | 0.52  | 82000  | 2.1007          |
  | 1.8541        | 0.53  | 84000  | 2.0849          |
  | 1.845         | 0.54  | 86000  | 2.0831          |
  | 1.8448        | 0.56  | 88000  | 2.0560          |
  | 1.8342        | 0.57  | 90000  | 2.0349          |
  | 1.8344        | 0.58  | 92000  | 2.0301          |
  | 1.8291        | 0.59  | 94000  | 2.0300          |
  | 1.819         | 0.61  | 96000  | 2.0378          |
  | 1.8154        | 0.62  | 98000  | 2.0197          |
  | 1.82          | 0.63  | 100000 | 2.0463          |
  | 1.8081        | 0.64  | 102000 | 2.0077          |
  | 1.8046        | 0.66  | 104000 | 2.0101          |
  | 1.7978        | 0.67  | 106000 | 2.0150          |
  | 1.7934        | 0.68  | 108000 | 2.0215          |
  | 1.7904        | 0.7   | 110000 | 2.0278          |
  | 1.7871        | 0.71  | 112000 | 2.0588          |
  | 1.779         | 0.72  | 114000 | 2.0062          |
  | 1.7784        | 0.73  | 116000 | 2.0300          |
  | 1.7749        | 0.75  | 118000 | 1.9664          |
  | 1.7691        | 0.76  | 120000 | 2.0033          |
  | 1.7622        | 0.77  | 122000 | 1.9983          |
  | 1.7587        | 0.78  | 124000 | 2.0030          |
  | 1.755         | 0.8   | 126000 | 1.9955          |
  | 1.7531        | 0.81  | 128000 | 1.9764          |
  | 1.7439        | 0.82  | 130000 | 1.9942          |
  | 1.7406        | 0.83  | 132000 | 2.0221          |
  | 1.7385        | 0.85  | 134000 | 1.9835          |
  | 1.7332        | 0.86  | 136000 | 1.9967          |
  | 1.7332        | 0.87  | 138000 | 2.