readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - bleu
  model-index:
  - name: opus-mt-zh-de-tuned-Tatoeba-small
    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. -->
  
  # opus-mt-zh-de-tuned-Tatoeba-small
  
  This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-de](https://huggingface.co/Helsinki-NLP/opus-mt-zh-de) on a refined dataset of Tatoeba German - Chinese corpus https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/master/data/README.md.
  It achieves the following results on the evaluation set:
  - Loss: 2.2703
  - Bleu: 16.504
  - Gen Len: 16.6531
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  Prefix used during fine-tuning: "将中文翻译成德语". This prefix is also recommended in prediction.
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - 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 | Bleu    | Gen Len |
  |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
  | 2.7229        | 0.24  | 16000  | 2.5605          | 14.1956 | 16.2206 |
  | 2.5988        | 0.49  | 32000  | 2.4447          | 14.8619 | 16.2726 |
  | 2.515         | 0.73  | 48000  | 2.3817          | 15.3212 | 16.2823 |
  | 2.4683        | 0.97  | 64000  | 2.3367          | 15.9043 | 16.7138 |
  | 2.3873        | 1.22  | 80000  | 2.3115          | 16.1037 | 16.6369 |
  | 2.3792        | 1.46  | 96000  | 2.2919          | 16.2957 | 16.6304 |
  | 2.3626        | 1.7   | 112000 | 2.2790          | 16.2995 | 16.6235 |
  | 2.3353        | 1.95  | 128000 | 2.2703          | 16.504  | 16.6531 |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  