readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: MIX2_en-ja_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_en-ja_helsinki
  
  This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-jap](https://huggingface.co/Helsinki-NLP/opus-mt-en-jap) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.6703
  
  ## 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 |
  |:-------------:|:-----:|:------:|:---------------:|
  | 3.5357        | 0.02  | 4000   | 2.9519          |
  | 2.8601        | 0.04  | 8000   | 2.6962          |
  | 2.6183        | 0.06  | 12000  | 2.5156          |
  | 2.4731        | 0.08  | 16000  | 2.4312          |
  | 2.3731        | 0.1   | 20000  | 2.3575          |
  | 2.2964        | 0.11  | 24000  | 2.3319          |
  | 2.238         | 0.13  | 28000  | 2.2802          |
  | 2.1919        | 0.15  | 32000  | 2.2552          |
  | 2.1479        | 0.17  | 36000  | 2.2354          |
  | 2.1104        | 0.19  | 40000  | 2.2210          |
  | 2.0788        | 0.21  | 44000  | 2.1835          |
  | 2.0552        | 0.23  | 48000  | 2.1391          |
  | 2.0228        | 0.25  | 52000  | 2.1338          |
  | 2.0062        | 0.27  | 56000  | 2.1115          |
  | 1.9868        | 0.29  | 60000  | 2.1025          |
  | 1.9628        | 0.31  | 64000  | 2.1334          |
  | 1.9474        | 0.32  | 68000  | 2.0935          |
  | 1.9318        | 0.34  | 72000  | 2.1030          |
  | 1.9187        | 0.36  | 76000  | 2.0605          |
  | 1.9019        | 0.38  | 80000  | 2.0388          |
  | 1.8916        | 0.4   | 84000  | 2.0360          |
  | 1.8775        | 0.42  | 88000  | 2.0356          |
  | 1.8689        | 0.44  | 92000  | 2.0315          |
  | 1.8558        | 0.46  | 96000  | 2.0169          |
  | 1.8431        | 0.48  | 100000 | 2.0213          |
  | 1.8373        | 0.5   | 104000 | 2.0071          |
  | 1.8224        | 0.52  | 108000 | 2.0093          |
  | 1.8181        | 0.53  | 112000 | 1.9952          |
  | 1.8087        | 0.55  | 116000 | 1.9927          |
  | 1.7998        | 0.57  | 120000 | 1.9726          |
  | 1.7947        | 0.59  | 124000 | 1.9817          |
  | 1.7874        | 0.61  | 128000 | 1.9650          |
  | 1.7781        | 0.63  | 132000 | 1.9688          |
  | 1.7712        | 0.65  | 136000 | 1.9655          |
  | 1.7631        | 0.67  | 140000 | 1.9561          |
  | 1.7577        | 0.69  | 144000 | 1.9529          |
  | 1.7528        | 0.71  | 148000 | 1.9447          |
  | 1.746         | 0.73  | 152000 | 1.9700          |
  | 1.7386        | 0.74  | 156000 | 1.9413          |
  | 1.7329        | 0.76  | 160000 | 1.9329          |
  | 1.7285        | 0.78  | 164000 | 1.9289          |
  | 1.7227        | 0.8   | 168000 | 1.9337          |
  | 1.7186        | 0.82  | 172000 | 1.9263          |
  | 1.7116        | 0.84  | 176000 | 1.9407          |
  | 1.7072        | 0.86  | 180000 | 1.9059          |
  | 1.7032        | 0.88  | 184000 | 1.9380          |
  | 1.6932        | 0.9   | 188000 | 1.9183          |
  | 1.6921        | 0.92  | 192000 | 1.9131          |
  | 1.6875        | 0.94  | 196000 | 1.9180          |
  | 1.6846        | 0.96  | 200000 | 1.9040          |
  | 1.6797        | 0.97  | 204000 | 1.9089          |
  | 1.6725        | 0.99  | 208000 | 1.9024          |
  | 1.6589        | 1.01  | 212000 | 1.8909          |
  | 1.6507        | 1.03  | 216000 | 1.8837          |
  | 1.6441        | 1.05  | 220000 | 1.8906          |
  | 1.6445        | 1.07  | 224000 | 1.8914          |
  | 1.6394        | 1.09  | 228000 | 1.8833          |
  | 1.6382        | 1.11  | 232000 | 1.8837          |
  | 1.6376        | 1.13  | 236000 | 1.8869          |
  | 1.6329        | 1.15  | 240000 | 1.8829          |
  | 1.6294        | 1.17  | 244000 | 1.8845          |
  | 1.6273        | 1.18  | 248000 | 1.8888          |
  | 1.6243        | 1.2   | 252000 | 1.8709          |
  | 1.6226        | 1.22  | 256000 | 1.8418          |
  | 1.6177        | 1.24  | 260000 | 1.8587          |
  | 1.6151        | 1.26  | 264000 | 1.8526          |
  | 1.6111        | 1.28  | 268000 | 1.8494          |
  | 1.6084        | 1.3   | 272000 | 1.8781          |
  | 1.6043        | 1.32  | 276000 | 1.8390          |
  | 1.6011        | 1.34  | 280000 | 1.8603          |
  | 1.5999        | 1.36  | 284000 | 1.8515          |
  | 1.5954        | 1.38  | 288000 | 1.8356          |
  | 1.5936        | 1