readme: ---
  license: apache-2.0
  tags:
  - automatic-speech-recognition
  - google/xtreme_s
  - generated_from_trainer
  metrics:
  - f1
  - accuracy
  model-index:
  - name: xtreme_s_xlsr_minds14
    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. -->
  
  # xtreme_s_xlsr_minds14
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - MINDS14 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2890
  - F1: 0.9474
  - Accuracy: 0.9470
  
  ## 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: 0.0003
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 2
  - total_train_batch_size: 64
  - total_eval_batch_size: 16
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1500
  - num_epochs: 50.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
  | 2.551         | 2.7   | 200  | 2.5855          | 0.0407 | 0.1201   |
  | 1.6934        | 5.41  | 400  | 1.5072          | 0.5862 | 0.6085   |
  | 0.5914        | 8.11  | 600  | 0.7274          | 0.8270 | 0.8232   |
  | 0.3896        | 10.81 | 800  | 0.4402          | 0.8905 | 0.8890   |
  | 0.5052        | 13.51 | 1000 | 0.4483          | 0.8837 | 0.8829   |
  | 0.4806        | 16.22 | 1200 | 0.4981          | 0.8784 | 0.8787   |
  | 0.2103        | 18.92 | 1400 | 0.4957          | 0.8810 | 0.8817   |
  | 0.4198        | 21.62 | 1600 | 0.5161          | 0.8927 | 0.8921   |
  | 0.11          | 24.32 | 1800 | 0.4456          | 0.8923 | 0.8902   |
  | 0.1233        | 27.03 | 2000 | 0.3858          | 0.9016 | 0.9012   |
  | 0.1827        | 29.73 | 2200 | 0.3765          | 0.9162 | 0.9159   |
  | 0.1235        | 32.43 | 2400 | 0.3716          | 0.9134 | 0.9128   |
  | 0.1873        | 35.14 | 2600 | 0.3080          | 0.9314 | 0.9311   |
  | 0.017         | 37.84 | 2800 | 0.2629          | 0.9415 | 0.9409   |
  | 0.0436        | 40.54 | 3000 | 0.3159          | 0.9397 | 0.9390   |
  | 0.0455        | 43.24 | 3200 | 0.2963          | 0.9393 | 0.9390   |
  | 0.046         | 45.95 | 3400 | 0.2914          | 0.9457 | 0.9451   |
  | 0.0042        | 48.65 | 3600 | 0.2890          | 0.9474 | 0.9470   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0.dev0
  - Pytorch 1.10.2+cu113
  - Datasets 1.18.4.dev0
  - Tokenizers 0.11.6
  