readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - xtreme_s
  metrics:
  - accuracy
  model-index:
  - name: xtreme_s_xlsr_300m_fleurs_langid_quicker_warmup
    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_300m_fleurs_langid_quicker_warmup
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the xtreme_s dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.9765
  - Accuracy: 0.6199
  
  ## 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: 4
  - eval_batch_size: 1
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 8
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 64
  - total_eval_batch_size: 8
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 10.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Accuracy | Validation Loss |
  |:-------------:|:-----:|:-----:|:--------:|:---------------:|
  | 0.6644        | 0.26  | 1000  | 0.3071   | 3.2482          |
  | 0.394         | 0.52  | 2000  | 0.5948   | 1.8833          |
  | 0.1034        | 0.78  | 3000  | 0.6297   | 1.5852          |
  | 0.1088        | 1.04  | 4000  | 0.5992   | 1.7903          |
  | 0.0032        | 1.3   | 5000  | 0.6356   | 1.6219          |
  | 0.1813        | 1.56  | 6000  | 0.5788   | 1.8168          |
  | 0.0654        | 1.82  | 7000  | 0.6234   | 1.6089          |
  | 0.0144        | 2.08  | 8000  | 0.6424   | 1.6071          |
  | 0.0019        | 2.34  | 9000  | 0.5822   | 1.7820          |
  | 0.0159        | 2.6   | 10000 | 0.6043   | 1.8407          |
  | 0.0029        | 2.86  | 11000 | 0.5845   | 1.8600          |
  | 0.0458        | 3.12  | 12000 | 0.6299   | 1.6591          |
  | 0.013         | 3.38  | 13000 | 0.5903   | 2.0788          |
  | 0.003         | 3.64  | 14000 | 0.6188   | 1.7645          |
  | 0.0015        | 3.9   | 15000 | 0.6328   | 1.7739          |
  | 0.0003        | 4.16  | 16000 | 0.6072   | 1.8742          |
  | 0.0005        | 4.42  | 17000 | 0.6231   | 1.7102          |
  | 0.006         | 4.68  | 18000 | 0.6122   | 1.6909          |
  | 0.2367        | 4.93  | 19000 | 0.6029   | 1.9891          |
  | 0.005         | 5.19  | 20000 | 0.6220   | 1.7245          |
  | 0.0813        | 5.45  | 21000 | 0.5739   | 2.0495          |
  | 0.1233        | 5.71  | 22000 | 0.6104   | 1.9601          |
  | 0.0003        | 5.97  | 23000 | 0.5924   | 1.8881          |
  | 0.0003        | 6.23  | 24000 | 0.6055   | 1.9568          |
  | 0.0001        | 6.49  | 25000 | 0.6086   | 1.8489          |
  | 0.2198        | 6.75  | 26000 | 0.6292   | 1.8048          |
  | 0.0261        | 7.01  | 27000 | 2.0284   | 0.5989          |
  | 0.0001        | 7.27  | 28000 | 1.7323   | 0.6431          |
  | 0.0001        | 7.53  | 29000 | 1.9329   | 0.6310          |
  | 0.0011        | 7.79  | 30000 | 1.9256   | 0.6107          |
  | 0.0933        | 8.05  | 31000 | 2.3915   | 0.5896          |
  | 0.0001        | 8.31  | 32000 | 1.9948   | 0.6021          |
  | 0.0003        | 8.57  | 33000 | 1.9518   | 0.6126          |
  | 0.0005        | 8.83  | 34000 | 1.8935   | 0.6243          |
  | 0.0           | 9.09  | 35000 | 2.0177   | 0.6144          |
  | 0.0002        | 9.35  | 36000 | 2.0234   | 0.6174          |
  | 0.0           | 9.61  | 37000 | 1.9568   | 0.6216          |
  | 0.0           | 9.87  | 38000 | 1.9765   | 0.6199          |
  
  
  ### Framework versions
  
  - Transformers 4.18.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 1.18.4.dev0
  - Tokenizers 0.11.6
  