readme: ---
  language:
  - all
  license: apache-2.0
  tags:
  - minds14
  - google/xtreme_s
  - generated_from_trainer
  datasets:
  - google/xtreme_s
  metrics:
  - f1
  - accuracy
  model-index:
  - name: xtreme_s_xlsr_300m_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_300m_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.ALL dataset.
  It achieves the following results on the evaluation set:
  - Accuracy: 0.9033
  - Accuracy Cs-cz: 0.9164
  - Accuracy De-de: 0.9477
  - Accuracy En-au: 0.9235
  - Accuracy En-gb: 0.9324
  - Accuracy En-us: 0.9326
  - Accuracy Es-es: 0.9177
  - Accuracy Fr-fr: 0.9444
  - Accuracy It-it: 0.9167
  - Accuracy Ko-kr: 0.8649
  - Accuracy Nl-nl: 0.9450
  - Accuracy Pl-pl: 0.9146
  - Accuracy Pt-pt: 0.8940
  - Accuracy Ru-ru: 0.8667
  - Accuracy Zh-cn: 0.7291
  - F1: 0.9015
  - F1 Cs-cz: 0.9154
  - F1 De-de: 0.9467
  - F1 En-au: 0.9199
  - F1 En-gb: 0.9334
  - F1 En-us: 0.9308
  - F1 Es-es: 0.9158
  - F1 Fr-fr: 0.9436
  - F1 It-it: 0.9135
  - F1 Ko-kr: 0.8642
  - F1 Nl-nl: 0.9440
  - F1 Pl-pl: 0.9159
  - F1 Pt-pt: 0.8883
  - F1 Ru-ru: 0.8646
  - F1 Zh-cn: 0.7249
  - Loss: 0.4119
  - Loss Cs-cz: 0.3790
  - Loss De-de: 0.2649
  - Loss En-au: 0.3459
  - Loss En-gb: 0.2853
  - Loss En-us: 0.2203
  - Loss Es-es: 0.2731
  - Loss Fr-fr: 0.1909
  - Loss It-it: 0.3520
  - Loss Ko-kr: 0.5431
  - Loss Nl-nl: 0.2515
  - Loss Pl-pl: 0.4113
  - Loss Pt-pt: 0.4798
  - Loss Ru-ru: 0.6470
  - Loss Zh-cn: 1.1216
  - Predict Samples: 4086
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
  | 2.6739        | 5.41  | 200  | 2.5687          | 0.0430 | 0.1190   |
  | 1.4953        | 10.81 | 400  | 1.6052          | 0.5550 | 0.5692   |
  | 0.6177        | 16.22 | 600  | 0.7927          | 0.8052 | 0.8011   |
  | 0.3609        | 21.62 | 800  | 0.5679          | 0.8609 | 0.8609   |
  | 0.4972        | 27.03 | 1000 | 0.5944          | 0.8509 | 0.8523   |
  | 0.1799        | 32.43 | 1200 | 0.6194          | 0.8623 | 0.8621   |
  | 0.1308        | 37.84 | 1400 | 0.5956          | 0.8569 | 0.8548   |
  | 0.2298        | 43.24 | 1600 | 0.5201          | 0.8732 | 0.8743   |
  | 0.0052        | 48.65 | 1800 | 0.3826          | 0.9106 | 0.9103   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0.dev0
  - Pytorch 1.10.2+cu113
  - Datasets 2.0.1.dev0
  - Tokenizers 0.11.6
  