readme: ---
  tags:
  - translation
  - generated_from_trainer
  datasets:
  - cmu_hinglish_dog
  metrics:
  - bleu
  model-index:
  - name: t5-small_6_3-hi_en-to-en
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: cmu_hinglish_dog
        type: cmu_hinglish_dog
        args: hi_en-en
      metrics:
      - name: Bleu
        type: bleu
        value: 18.0863
  ---
  
  <!-- 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. -->
  
  # t5-small_6_3-hi_en-to-en
  
  This model was trained from scratch on the cmu_hinglish_dog dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.3662
  - Bleu: 18.0863
  - Gen Len: 15.2708
  
  ## Model description
  
  Model generated using:<br />
  ```python make_student.py t5-small t5_small_6_3 6 3```<br />
  Check this [link](https://discuss.huggingface.co/t/questions-on-distilling-from-t5/1193/9) for more information.
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  Used cmu_hinglish_dog dataset. Please check this [link](https://huggingface.co/datasets/cmu_hinglish_dog) for dataset description
   
  ## Translation:
  
  * Source: hi_en: The text in Hinglish
  * Target: en: The text in English
  
  
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 100
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
  | No log        | 1.0   | 126   | 3.0601          | 4.7146  | 11.9904 |
  | No log        | 2.0   | 252   | 2.8885          | 5.9584  | 12.3418 |
  | No log        | 3.0   | 378   | 2.7914          | 6.649   | 12.3758 |
  | 3.4671        | 4.0   | 504   | 2.7347          | 7.3305  | 12.3854 |
  | 3.4671        | 5.0   | 630   | 2.6832          | 8.3132  | 12.4268 |
  | 3.4671        | 6.0   | 756   | 2.6485          | 8.339   | 12.3641 |
  | 3.4671        | 7.0   | 882   | 2.6096          | 8.7269  | 12.414  |
  | 3.0208        | 8.0   | 1008  | 2.5814          | 9.2163  | 12.2675 |
  | 3.0208        | 9.0   | 1134  | 2.5542          | 9.448   | 12.3875 |
  | 3.0208        | 10.0  | 1260  | 2.5339          | 9.9011  | 12.4321 |
  | 3.0208        | 11.0  | 1386  | 2.5043          | 9.7529  | 12.5149 |
  | 2.834         | 12.0  | 1512  | 2.4848          | 9.9606  | 12.4193 |
  | 2.834         | 13.0  | 1638  | 2.4737          | 9.9368  | 12.3673 |
  | 2.834         | 14.0  | 1764  | 2.4458          | 10.3182 | 12.4352 |
  | 2.834         | 15.0  | 1890  | 2.4332          | 10.486  | 12.4671 |
  | 2.7065        | 16.0  | 2016  | 2.4239          | 10.6921 | 12.414  |
  | 2.7065        | 17.0  | 2142  | 2.4064          | 10.7426 | 12.4607 |
  | 2.7065        | 18.0  | 2268  | 2.3941          | 11.0509 | 12.4087 |
  | 2.7065        | 19.0  | 2394  | 2.3826          | 11.2407 | 12.3386 |
  | 2.603         | 20.0  | 2520  | 2.3658          | 11.3711 | 12.3992 |
  | 2.603         | 21.0  | 2646  | 2.3537          | 11.42   | 12.5032 |
  | 2.603         | 22.0  | 2772  | 2.3475          | 12.0665 | 12.5074 |
  | 2.603         | 23.0  | 2898  | 2.3398          | 12.0343 | 12.4342 |
  | 2.5192        | 24.0  | 3024  | 2.3298          | 12.1011 | 12.5096 |
  | 2.5192        | 25.0  | 3150  | 2.3216          | 12.2562 | 12.4809 |
  | 2.5192        | 26.0  | 3276  | 2.3131          | 12.4585 | 12.4427 |
  | 2.5192        | 27.0  | 3402  | 2.3052          | 12.7094 | 12.534  |
  | 2.4445        | 28.0  | 3528  | 2.2984          | 12.7432 | 12.5053 |
  | 2.4445        | 29.0  | 3654  | 2.2920          | 12.8409 | 12.4501 |
  | 2.4445        | 30.0  | 3780  | 2.2869          | 12.6365 | 12.4936 |
  | 2.4445        | 31.0  | 3906  | 2.2777          | 12.8523 | 12.5234 |
  | 2.3844        | 32.0  | 4032  | 2.2788          | 12.9216 | 12.4204 |
  | 2.3844        | 33.0  | 4158  | 2.2710          | 12.9568 | 12.5064 |
  | 2.3844        | 34.0  | 4284  | 2.2643          | 12.9641 | 12.4299 |
  | 2.3844        | 35.0  | 4410  | 2.2621          | 12.9787 | 12.448  |
  | 2.3282        | 36.0  | 4536  | 2.2554          | 13.1264 | 12.4374 |
  | 2.3282        | 37.0  | 4662  | 2.2481          | 13.1853 | 12.4416 |
  | 2.3282        | 38.0  | 4788  | 2.2477          | 13.3259 | 12.4119 |
  | 2.3282        | 39.0  | 4914  | 2.2448          | 13.2017 | 12.4278 |
  | 2.2842        | 40.0  | 5040  | 2.2402          | 13.3772 | 12.4437 |
  | 2.2842        | 41.0  | 5166  | 2.2373          | 13.2184 | 12.414  |
  | 2.2842        | 42.0  | 5292  | 2.2357          | 13.5267 | 12.4342 |
  | 2.2842        | 43.0  | 5418  | 2.2310          | 13.5754 | 12.4087 |