readme: ---
  license: cc-by-nc-4.0
  tags:
  - generated_from_trainer
  metrics:
  - bleu
  model-index:
  - name: nllb-200-distilled-600M-finetuned-pan_Guru-to-eng_Latn
    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. -->
  
  # nllb-200-distilled-600M-finetuned-pan_Guru-to-eng_Latn
  
  This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8728
  - Bleu: 42.5453
  - Gen Len: 32.376
  
  ## 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: 2e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Bleu    | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
  | 1.5153        | 0.72  | 500  | 1.0531          | 34.9696 | 32.548  |
  | 1.1282        | 1.45  | 1000 | 0.9580          | 38.3648 | 31.832  |
  | 1.0299        | 2.18  | 1500 | 0.9235          | 40.1212 | 31.964  |
  | 0.942         | 2.9   | 2000 | 0.8963          | 41.2737 | 31.884  |
  | 0.8869        | 3.63  | 2500 | 0.8847          | 41.4381 | 31.82   |
  | 0.8553        | 4.35  | 3000 | 0.8780          | 42.1548 | 32.136  |
  | 0.8306        | 5.08  | 3500 | 0.8733          | 42.3333 | 32.64   |
  | 0.8063        | 5.8   | 4000 | 0.8728          | 42.5453 | 32.376  |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  