readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: finetuned-xlm-r-masakhaner-swa-whole-word-phonetic
    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. -->
  
  # finetuned-xlm-r-masakhaner-swa-whole-word-phonetic
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 11.0418
  
  ## 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: 5e-08
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 500
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | No log        | 1.0   | 62    | 39.5330         |
  | No log        | 2.0   | 124   | 39.2832         |
  | No log        | 3.0   | 186   | 39.7275         |
  | No log        | 4.0   | 248   | 38.7389         |
  | No log        | 5.0   | 310   | 38.1088         |
  | No log        | 6.0   | 372   | 37.6541         |
  | No log        | 7.0   | 434   | 36.2860         |
  | No log        | 8.0   | 496   | 36.0252         |
  | 40.88         | 9.0   | 558   | 35.2045         |
  | 40.88         | 10.0  | 620   | 34.1314         |
  | 40.88         | 11.0  | 682   | 32.9068         |
  | 40.88         | 12.0  | 744   | 32.0917         |
  | 40.88         | 13.0  | 806   | 31.3414         |
  | 40.88         | 14.0  | 868   | 30.1522         |
  | 40.88         | 15.0  | 930   | 29.0864         |
  | 40.88         | 16.0  | 992   | 27.7657         |
  | 32.3449       | 17.0  | 1054  | 26.6795         |
  | 32.3449       | 18.0  | 1116  | 25.4307         |
  | 32.3449       | 19.0  | 1178  | 24.4571         |
  | 32.3449       | 20.0  | 1240  | 24.0340         |
  | 32.3449       | 21.0  | 1302  | 22.6889         |
  | 32.3449       | 22.0  | 1364  | 21.9149         |
  | 32.3449       | 23.0  | 1426  | 20.9471         |
  | 32.3449       | 24.0  | 1488  | 20.7152         |
  | 24.114        | 25.0  | 1550  | 19.9579         |
  | 24.114        | 26.0  | 1612  | 19.4215         |
  | 24.114        | 27.0  | 1674  | 18.7025         |
  | 24.114        | 28.0  | 1736  | 18.0530         |
  | 24.114        | 29.0  | 1798  | 17.7290         |
  | 24.114        | 30.0  | 1860  | 17.2821         |
  | 24.114        | 31.0  | 1922  | 16.8666         |
  | 24.114        | 32.0  | 1984  | 16.4210         |
  | 18.4015       | 33.0  | 2046  | 16.2339         |
  | 18.4015       | 34.0  | 2108  | 15.7990         |
  | 18.4015       | 35.0  | 2170  | 15.7727         |
  | 18.4015       | 36.0  | 2232  | 15.2343         |
  | 18.4015       | 37.0  | 2294  | 15.1427         |
  | 18.4015       | 38.0  | 2356  | 14.8736         |
  | 18.4015       | 39.0  | 2418  | 14.7042         |
  | 18.4015       | 40.0  | 2480  | 14.5392         |
  | 14.946        | 41.0  | 2542  | 14.5356         |
  | 14.946        | 42.0  | 2604  | 14.1603         |
  | 14.946        | 43.0  | 2666  | 14.1452         |
  | 14.946        | 44.0  | 2728  | 14.1982         |
  | 14.946        | 45.0  | 2790  | 13.9763         |
  | 14.946        | 46.0  | 2852  | 13.9216         |
  | 14.946        | 47.0  | 2914  | 13.7671         |
  | 14.946        | 48.0  | 2976  | 13.6343         |
  | 13.1518       | 49.0  | 3038  | 13.6241         |
  | 13.1518       | 50.0  | 3100  | 13.4163         |
  | 13.1518       | 51.0  | 3162  | 13.4401         |
  | 13.1518       | 52.0  | 3224  | 13.5609         |
  | 13.1518       | 53.0  | 3286  | 13.2858         |
  | 13.1518       | 54.0  | 3348  | 13.1582         |
  | 13.1518       | 55.0  | 3410  | 13.2456         |
  | 13.1518       | 56.0  | 3472  | 13.2341         |
  | 12.1359       | 57.0  | 3534  | 13.0155         |
  | 12.1359       | 58.0  | 3596  | 13.0293         |
  | 12.1359       | 59.0  | 3658  | 12.9712         |
  | 12.1359       | 60.0  | 3720  | 12.9446         |
  | 12.1359       | 61.0  | 3782  | 12.8301         |
  | 12.1359       | 62.0  | 3844  | 12.7574         |
  | 12.1359       | 63.0  | 3906  | 12.6967         |
  | 12.1359       | 64.0  | 3968  | 12.6533         |
  | 11.5584       | 65.0  | 4030  | 12.5547         |
  | 11.5584       | 66.0  | 4092  | 12.5866         |
  | 11.5584       | 67.0  | 4154  | 12.7563         |
  | 11.5584       | 68.0  | 4216  | 12.6487         |
  | 11.5584       | 69.0  | 4278  | 12.6015         |
  | 11.5584       | 70.0  | 4340  | 12.5097         |
  | 11.5584       | 71.0  | 4402  | 12.5312         |
  | 11.5584       | 72.0  | 4464  | 12.4679         |
  | 11.224        | 73.0  | 4526  | 12.4805         |
  | 11.224        | 74.0  | 4588  | 12.4431        