readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: MIX2_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. -->
  
  # MIX2_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: 1.4929
  - Otaku Benchmark VN BLEU: 20.21
  - Otaku Benchmark LN BLEU: 13.29
  - Otaku Benchmark MANGA BLEU: 19.07
  
  ## 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: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss |
  |:-------------:|:-----:|:------:|:---------------:|
  | 2.8467        | 0.01  | 2000   | 2.3237          |
  | 2.6439        | 0.02  | 4000   | 2.2542          |
  | 2.547         | 0.03  | 6000   | 2.1956          |
  | 2.4852        | 0.04  | 8000   | 2.1088          |
  | 2.4408        | 0.05  | 10000  | 2.0909          |
  | 2.404         | 0.06  | 12000  | 2.1029          |
  | 2.3634        | 0.07  | 14000  | 2.0636          |
  | 2.3491        | 0.08  | 16000  | 2.0312          |
  | 2.3203        | 0.09  | 18000  | 2.0187          |
  | 2.3002        | 0.1   | 20000  | 1.9999          |
  | 2.2791        | 0.11  | 22000  | 1.9823          |
  | 2.2607        | 0.11  | 24000  | 1.9588          |
  | 2.2475        | 0.12  | 26000  | 1.9728          |
  | 2.2308        | 0.13  | 28000  | 1.9330          |
  | 2.2237        | 0.14  | 30000  | 1.9657          |
  | 2.208         | 0.15  | 32000  | 1.9560          |
  | 2.2019        | 0.16  | 34000  | 1.9704          |
  | 2.1864        | 0.17  | 36000  | 1.9513          |
  | 2.1764        | 0.18  | 38000  | 1.9534          |
  | 2.163         | 0.19  | 40000  | 1.9140          |
  | 2.1534        | 0.2   | 42000  | 1.9241          |
  | 2.146         | 0.21  | 44000  | 1.9162          |
  | 2.1403        | 0.22  | 46000  | 1.9030          |
  | 2.1309        | 0.23  | 48000  | 1.8741          |
  | 2.1174        | 0.24  | 50000  | 1.8834          |
  | 2.1157        | 0.25  | 52000  | 1.8666          |
  | 2.1116        | 0.26  | 54000  | 1.8870          |
  | 2.1062        | 0.27  | 56000  | 1.8837          |
  | 2.0994        | 0.28  | 58000  | 1.8638          |
  | 2.0924        | 0.29  | 60000  | 1.8766          |
  | 2.0874        | 0.3   | 62000  | 1.8712          |
  | 2.0805        | 0.31  | 64000  | 1.8792          |
  | 2.0746        | 0.32  | 66000  | 1.8586          |
  | 2.0684        | 0.32  | 68000  | 1.8819          |
  | 2.0678        | 0.33  | 70000  | 1.8529          |
  | 2.061         | 0.34  | 72000  | 1.8219          |
  | 2.0532        | 0.35  | 74000  | 1.8383          |
  | 2.0536        | 0.36  | 76000  | 1.8273          |
  | 2.0432        | 0.37  | 78000  | 1.8304          |
  | 2.0386        | 0.38  | 80000  | 1.8208          |
  | 2.0361        | 0.39  | 82000  | 1.8103          |
  | 2.0353        | 0.4   | 84000  | 1.8193          |
  | 2.0266        | 0.41  | 86000  | 1.8369          |
  | 2.0277        | 0.42  | 88000  | 1.8266          |
  | 2.0221        | 0.43  | 90000  | 1.8372          |
  | 2.0181        | 0.44  | 92000  | 1.8436          |
  | 2.0182        | 0.45  | 94000  | 1.8505          |
  | 2.0088        | 0.46  | 96000  | 1.8127          |
  | 2.005         | 0.47  | 98000  | 1.8325          |
  | 2.0003        | 0.48  | 100000 | 1.8407          |
  | 2.0031        | 0.49  | 102000 | 1.8140          |
  | 1.9954        | 0.5   | 104000 | 1.8177          |
  | 1.9894        | 0.51  | 106000 | 1.8072          |
  | 1.9901        | 0.52  | 108000 | 1.7971          |
  | 1.9864        | 0.53  | 110000 | 1.8007          |
  | 1.9848        | 0.53  | 112000 | 1.7961          |
  | 1.9774        | 0.54  | 114000 | 1.7933          |
  | 1.9802        | 0.55  | 116000 | 1.8031          |
  | 1.9698        | 0.56  | 118000 | 1.8137          |
  | 1.973         | 0.57  | 120000 | 1.7930          |
  | 1.9696        | 0.58  | 122000 | 1.7838          |
  | 1.9641        | 0.59  | 124000 | 1.7730          |
  | 1.9609        | 0.6   | 126000 | 1.7800          |
  | 1.9605        | 0.61  | 128000 | 1.7680          |
  | 1.9516        | 0.62  | 130000 | 1.7895          |
  | 1.9529        | 0.63  | 132000 | 1.7825          |
  | 1.9503        | 0.64  | 134000 | 1.7792          |
  | 1.9528        | 0.65  | 136000 | 1.8031          |
  | 1.9439        | 0.66  | 138000 | 1.7652          |
  | 1.9453        | 0.67  | 140000 | 1.7713          |
  | 1.9404        | 0.68  |