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pseudolabeling-step2-F01-Pass-2

This model is a fine-tuned version of monideep2255/XLRS-torgo on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2704
  • Wer: 1.1942

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: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.9509 2.94 400 1.1949 1.2734
0.5686 5.88 800 1.0452 1.2266
0.4179 8.82 1200 1.1876 1.2032
0.3137 11.76 1600 1.2691 1.2572
0.2329 14.7 2000 1.2944 1.2104
0.1851 17.64 2400 1.4389 1.2626
0.1427 20.59 2800 1.3325 1.2608
0.1101 23.53 3200 1.4132 1.2176
0.0805 26.47 3600 1.3443 1.2482
0.0645 29.41 4000 1.2704 1.1942

Framework versions

  • Transformers 4.23.1
  • Pytorch 1.12.1+cu113
  • Datasets 1.18.3
  • Tokenizers 0.13.2
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