readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: 2-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. -->
  
  # 2-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: 11.0492
  
  ## 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: 1
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 8
  - 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   | 61    | 39.7928         |
  | No log        | 2.0   | 122   | 39.8195         |
  | No log        | 3.0   | 183   | 39.8228         |
  | No log        | 4.0   | 244   | 39.0793         |
  | No log        | 5.0   | 305   | 38.6628         |
  | No log        | 6.0   | 366   | 37.4014         |
  | No log        | 7.0   | 427   | 36.8203         |
  | No log        | 8.0   | 488   | 36.2607         |
  | 41.0436       | 9.0   | 549   | 35.9316         |
  | 41.0436       | 10.0  | 610   | 34.3282         |
  | 41.0436       | 11.0  | 671   | 33.3604         |
  | 41.0436       | 12.0  | 732   | 32.4470         |
  | 41.0436       | 13.0  | 793   | 31.5403         |
  | 41.0436       | 14.0  | 854   | 30.9665         |
  | 41.0436       | 15.0  | 915   | 29.4171         |
  | 41.0436       | 16.0  | 976   | 28.4589         |
  | 32.5506       | 17.0  | 1037  | 27.2699         |
  | 32.5506       | 18.0  | 1098  | 26.0614         |
  | 32.5506       | 19.0  | 1159  | 25.1529         |
  | 32.5506       | 20.0  | 1220  | 24.3080         |
  | 32.5506       | 21.0  | 1281  | 23.1511         |
  | 32.5506       | 22.0  | 1342  | 22.3332         |
  | 32.5506       | 23.0  | 1403  | 21.7154         |
  | 32.5506       | 24.0  | 1464  | 20.7367         |
  | 24.3212       | 25.0  | 1525  | 20.1804         |
  | 24.3212       | 26.0  | 1586  | 19.5591         |
  | 24.3212       | 27.0  | 1647  | 18.8760         |
  | 24.3212       | 28.0  | 1708  | 18.3291         |
  | 24.3212       | 29.0  | 1769  | 18.0296         |
  | 24.3212       | 30.0  | 1830  | 17.4091         |
  | 24.3212       | 31.0  | 1891  | 17.2150         |
  | 24.3212       | 32.0  | 1952  | 16.6483         |
  | 18.5662       | 33.0  | 2013  | 16.2697         |
  | 18.5662       | 34.0  | 2074  | 16.1714         |
  | 18.5662       | 35.0  | 2135  | 15.8008         |
  | 18.5662       | 36.0  | 2196  | 15.2647         |
  | 18.5662       | 37.0  | 2257  | 15.4195         |
  | 18.5662       | 38.0  | 2318  | 14.9898         |
  | 18.5662       | 39.0  | 2379  | 14.8599         |
  | 18.5662       | 40.0  | 2440  | 14.6119         |
  | 15.1141       | 41.0  | 2501  | 14.4644         |
  | 15.1141       | 42.0  | 2562  | 14.3398         |
  | 15.1141       | 43.0  | 2623  | 14.4107         |
  | 15.1141       | 44.0  | 2684  | 13.9039         |
  | 15.1141       | 45.0  | 2745  | 14.0043         |
  | 15.1141       | 46.0  | 2806  | 13.9534         |
  | 15.1141       | 47.0  | 2867  | 13.9267         |
  | 15.1141       | 48.0  | 2928  | 13.5888         |
  | 15.1141       | 49.0  | 2989  | 13.6313         |
  | 13.182        | 50.0  | 3050  | 13.5405         |
  | 13.182        | 51.0  | 3111  | 13.3378         |
  | 13.182        | 52.0  | 3172  | 13.3160         |
  | 13.182        | 53.0  | 3233  | 13.1356         |
  | 13.182        | 54.0  | 3294  | 13.2483         |
  | 13.182        | 55.0  | 3355  | 13.1259         |
  | 13.182        | 56.0  | 3416  | 13.1775         |
  | 13.182        | 57.0  | 3477  | 13.1118         |
  | 12.1712       | 58.0  | 3538  | 12.9363         |
  | 12.1712       | 59.0  | 3599  | 12.8765         |
  | 12.1712       | 60.0  | 3660  | 12.7923         |
  | 12.1712       | 61.0  | 3721  | 12.9732         |
  | 12.1712       | 62.0  | 3782  | 12.8606         |
  | 12.1712       | 63.0  | 3843  | 12.7897         |
  | 12.1712       | 64.0  | 3904  | 12.6516         |
  | 12.1712       | 65.0  | 3965  | 12.6242         |
  | 11.5853       | 66.0  | 4026  | 12.5954         |
  | 11.5853       | 67.0  | 4087  | 12.4505         |
  | 11.5853       | 68.0  | 4148  | 12.5595         |
  | 11.5853       | 69.0  | 4209  | 12.4287         |
  | 11.5853       | 70.0  | 4270  | 12.4768         |
  | 11.5853       | 71.0  | 4331  | 12.4003         |
  | 11.5853       | 72.0  | 4392  | 12.4323         |
  | 11.5853       | 73.0  | 4453  | 12