readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: LN_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. -->
  
  # LN_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: 2.5382
  
  ## 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: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step   | Validation Loss |
  |:-------------:|:-----:|:------:|:---------------:|
  | 2.5108        | 0.02  | 2000   | 2.8405          |
  | 2.2729        | 0.04  | 4000   | 2.7758          |
  | 2.1673        | 0.06  | 6000   | 2.7098          |
  | 2.0981        | 0.08  | 8000   | 2.6487          |
  | 2.048         | 0.1   | 10000  | 2.7008          |
  | 2.0077        | 0.12  | 12000  | 2.6614          |
  | 1.9677        | 0.13  | 14000  | 2.6227          |
  | 1.9445        | 0.15  | 16000  | 2.5895          |
  | 1.9137        | 0.17  | 18000  | 2.5897          |
  | 1.8911        | 0.19  | 20000  | 2.6771          |
  | 1.8695        | 0.21  | 22000  | 2.6332          |
  | 1.8479        | 0.23  | 24000  | 2.6130          |
  | 1.8378        | 0.25  | 26000  | 2.6518          |
  | 1.8191        | 0.27  | 28000  | 2.6401          |
  | 1.8024        | 0.29  | 30000  | 2.6617          |
  | 1.7933        | 0.31  | 32000  | 2.6705          |
  | 1.7787        | 0.33  | 34000  | 2.6280          |
  | 1.7661        | 0.35  | 36000  | 2.6911          |
  | 1.7514        | 0.36  | 38000  | 2.6766          |
  | 1.7444        | 0.38  | 40000  | 2.6996          |
  | 1.7363        | 0.4   | 42000  | 2.6276          |
  | 1.722         | 0.42  | 44000  | 2.6466          |
  | 1.7177        | 0.44  | 46000  | 2.5937          |
  | 1.7055        | 0.46  | 48000  | 2.6386          |
  | 1.6956        | 0.48  | 50000  | 2.6794          |
  | 1.6885        | 0.5   | 52000  | 2.7336          |
  | 1.679         | 0.52  | 54000  | 2.7266          |
  | 1.6715        | 0.54  | 56000  | 2.6945          |
  | 1.6666        | 0.56  | 58000  | 2.7111          |
  | 1.6599        | 0.58  | 60000  | 2.7205          |
  | 1.6566        | 0.59  | 62000  | 2.7194          |
  | 1.6481        | 0.61  | 64000  | 2.6582          |
  | 1.6434        | 0.63  | 66000  | 2.6859          |
  | 1.6315        | 0.65  | 68000  | 2.7058          |
  | 1.6258        | 0.67  | 70000  | 2.7428          |
  | 1.6189        | 0.69  | 72000  | 2.7411          |
  | 1.6169        | 0.71  | 74000  | 2.7039          |
  | 1.6087        | 0.73  | 76000  | 2.6844          |
  | 1.6021        | 0.75  | 78000  | 2.6454          |
  | 1.6034        | 0.77  | 80000  | 2.6596          |
  | 1.5941        | 0.79  | 82000  | 2.6903          |
  | 1.5862        | 0.81  | 84000  | 2.7099          |
  | 1.5836        | 0.83  | 86000  | 2.6929          |
  | 1.5827        | 0.84  | 88000  | 2.7181          |
  | 1.5747        | 0.86  | 90000  | 2.6888          |
  | 1.5678        | 0.88  | 92000  | 2.6662          |
  | 1.5643        | 0.9   | 94000  | 2.6663          |
  | 1.561         | 0.92  | 96000  | 2.6699          |
  | 1.5565        | 0.94  | 98000  | 2.6667          |
  | 1.5501        | 0.96  | 100000 | 2.6828          |
  | 1.5476        | 0.98  | 102000 | 2.6531          |
  | 1.5444        | 1.0   | 104000 | 2.6799          |
  | 1.5057        | 1.02  | 106000 | 2.6525          |
  | 1.5003        | 1.04  | 108000 | 2.6996          |
  | 1.4996        | 1.06  | 110000 | 2.6649          |
  | 1.4996        | 1.07  | 112000 | 2.6974          |
  | 1.4966        | 1.09  | 114000 | 2.7594          |
  | 1.4967        | 1.11  | 116000 | 2.6966          |
  | 1.492         | 1.13  | 118000 | 2.6929          |
  | 1.4923        | 1.15  | 120000 | 2.6522          |
  | 1.4838        | 1.17  | 122000 | 2.6363          |
  | 1.4839        | 1.19  | 124000 | 2.6849          |
  | 1.4807        | 1.21  | 126000 | 2.6667          |
  | 1.4778        | 1.23  | 128000 | 2.6684          |
  | 1.4731        | 1.25  | 130000 | 2.6338          |
  | 1.4727        | 1.27  | 132000 | 2.6093          |
  | 1.4695        | 1.29  | 134000 | 2.6020          |
  | 1.4656        | 1.3   | 136000 | 2.6341          |
  | 1.4648        | 1.32  | 138000 | 2.6509          |
  | 1.4578        | 1.34  | 140000 | 2.6807          |
  | 1.4606        | 1.36  | 142000 | 2.6357          |
  | 1.4529        | 1.38  | 144000 | 2.6404          |
  | 1.4488        | 1.4   |