readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: SCRATCH_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. -->
  
  # 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