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w2v-bert-cv-grain-lg_both_v2

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wav2vec2-bert
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metadata
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: w2v-bert-cv-grain-lg_both_v2
    results: []

w2v-bert-cv-grain-lg_both_v2

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0892
  • Wer: 0.0443
  • Cer: 0.0123

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 80
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
0.2889 1.0 10812 0.1708 0.1703 0.0386
0.1849 2.0 21624 0.1342 0.1274 0.0285
0.1512 3.0 32436 0.1144 0.1044 0.0244
0.1313 4.0 43248 0.1033 0.0918 0.0217
0.117 5.0 54060 0.1034 0.0738 0.0191
0.1056 6.0 64872 0.0906 0.0738 0.0181
0.0962 7.0 75684 0.0959 0.0655 0.0168
0.0885 8.0 86496 0.0860 0.0592 0.0155
0.0807 9.0 97308 0.0844 0.0603 0.0154
0.0742 10.0 108120 0.0814 0.0573 0.0144
0.0683 11.0 118932 0.0858 0.0588 0.0154
0.0629 12.0 129744 0.0944 0.0538 0.0146
0.0581 13.0 140556 0.0842 0.0558 0.0151
0.0528 14.0 151368 0.0873 0.0503 0.0141
0.0479 15.0 162180 0.0820 0.0503 0.0138
0.0429 16.0 172992 0.0815 0.0427 0.0125
0.0392 17.0 183804 0.0864 0.0466 0.0128
0.035 18.0 194616 0.0899 0.0479 0.0128
0.0316 19.0 205428 0.0872 0.0430 0.0120
0.0286 20.0 216240 0.0821 0.0425 0.0114
0.0254 21.0 227052 0.0898 0.0466 0.0122
0.0229 22.0 237864 0.0864 0.0417 0.0120
0.021 23.0 248676 0.0893 0.0408 0.0122
0.0192 24.0 259488 0.0878 0.0430 0.0118
0.0171 25.0 270300 0.0994 0.0473 0.0128
0.0156 26.0 281112 0.0892 0.0443 0.0123

Framework versions

  • Transformers 4.46.1
  • Pytorch 2.1.0+cu118
  • Datasets 3.1.0
  • Tokenizers 0.20.1