readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: MIX3_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. -->
  
  # MIX3_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: 1.4832
  
  ## 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: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step    | Validation Loss |
  |:-------------:|:-----:|:-------:|:---------------:|
  | 2.8699        | 0.01  | 5000    | 2.3465          |
  | 2.6168        | 0.02  | 10000   | 2.2205          |
  | 2.5083        | 0.03  | 15000   | 2.2382          |
  | 2.4359        | 0.04  | 20000   | 2.1670          |
  | 2.3821        | 0.06  | 25000   | 2.1122          |
  | 2.3358        | 0.07  | 30000   | 2.0902          |
  | 2.3045        | 0.08  | 35000   | 2.0461          |
  | 2.2782        | 0.09  | 40000   | 2.0290          |
  | 2.2481        | 0.1   | 45000   | 1.9910          |
  | 2.2267        | 0.11  | 50000   | 2.0059          |
  | 2.2056        | 0.12  | 55000   | 1.9858          |
  | 2.1903        | 0.13  | 60000   | 1.9725          |
  | 2.173         | 0.15  | 65000   | 1.9797          |
  | 2.154         | 0.16  | 70000   | 1.9654          |
  | 2.1429        | 0.17  | 75000   | 1.9567          |
  | 2.1304        | 0.18  | 80000   | 1.9348          |
  | 2.1232        | 0.19  | 85000   | 1.9361          |
  | 2.116         | 0.2   | 90000   | 1.9277          |
  | 2.1016        | 0.21  | 95000   | 1.9193          |
  | 2.0984        | 0.22  | 100000  | 1.9064          |
  | 2.0797        | 0.24  | 105000  | 1.9177          |
  | 2.0767        | 0.25  | 110000  | 1.8975          |
  | 2.0642        | 0.26  | 115000  | 1.8782          |
  | 2.0595        | 0.27  | 120000  | 1.9012          |
  | 2.0533        | 0.28  | 125000  | 1.8977          |
  | 2.044         | 0.29  | 130000  | 1.8984          |
  | 2.0374        | 0.3   | 135000  | 1.9221          |
  | 2.0305        | 0.31  | 140000  | 1.9243          |
  | 2.02          | 0.32  | 145000  | 1.8773          |
  | 2.0195        | 0.34  | 150000  | 1.8676          |
  | 2.0151        | 0.35  | 155000  | 1.8637          |
  | 2.0065        | 0.36  | 160000  | 1.8556          |
  | 2.0037        | 0.37  | 165000  | 1.8399          |
  | 1.9963        | 0.38  | 170000  | 1.8452          |
  | 1.9878        | 0.39  | 175000  | 1.8644          |
  | 1.9871        | 0.4   | 180000  | 1.8576          |
  | 1.9779        | 0.41  | 185000  | 1.8509          |
  | 1.9721        | 0.43  | 190000  | 1.8405          |
  | 1.9724        | 0.44  | 195000  | 1.8594          |
  | 1.9685        | 0.45  | 200000  | 1.8540          |
  | 1.9634        | 0.46  | 205000  | 1.8694          |
  | 1.9583        | 0.47  | 210000  | 1.8591          |
  | 1.9557        | 0.48  | 215000  | 1.8539          |
  | 1.9494        | 0.49  | 220000  | 1.8673          |
  | 1.9484        | 0.5   | 225000  | 1.8021          |
  | 1.9395        | 0.52  | 230000  | 1.8309          |
  | 1.9384        | 0.53  | 235000  | 1.7933          |
  | 1.937         | 0.54  | 240000  | 1.8199          |
  | 1.9315        | 0.55  | 245000  | 1.8065          |
  | 1.9276        | 0.56  | 250000  | 1.7857          |
  | 1.9248        | 0.57  | 255000  | 1.8207          |
  | 1.9195        | 0.58  | 260000  | 1.7898          |
  | 1.9187        | 0.59  | 265000  | 1.8097          |
  | 1.9138        | 0.6   | 270000  | 1.7909          |
  | 1.9094        | 0.62  | 275000  | 1.7995          |
  | 1.9098        | 0.63  | 280000  | 1.8165          |
  | 1.9038        | 0.64  | 285000  | 1.8132          |
  | 1.9034        | 0.65  | 290000  | 1.7951          |
  | 1.899         | 0.66  | 295000  | 1.7880          |
  | 1.8965        | 0.67  | 300000  | 1.7953          |
  | 1.8941        | 0.68  | 305000  | 1.7986          |
  | 1.8919        | 0.69  | 310000  | 1.7964          |
  | 1.8875        | 0.71  | 315000  | 1.8041          |
  | 1.884         | 0.72  | 320000  | 1.7764          |
  | 1.8798        | 0.73  | 325000  | 1.8019          |
  | 1.8801        | 0.74  | 330000  | 1.7790          |
  | 1.8809        | 0.75  | 335000  | 1.7849          |
  | 1.8736        | 0.76  | 340000  | 1.7800          |
  | 1.8727        | 0.77  | 345000  | 1.7900          |
  | 1.8722        | 0.78  | 350000  | 1.7727          |
  | 1.8699        | 0.8   | 355000  | 1.7597          |
  |