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w2v-bert-2.0-CV_Fleurs-lg-50hrs-v4

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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-2.0-CV_Fleurs-lg-50hrs-v4
    results: []

w2v-bert-2.0-CV_Fleurs-lg-50hrs-v4

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: 0.3482
  • Wer: 0.2832
  • Cer: 0.0557

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: 3e-05
  • 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
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
1.9126 1.0 3160 0.3415 0.4010 0.0853
0.2463 2.0 6320 0.2633 0.3447 0.0670
0.1946 3.0 9480 0.2369 0.3201 0.0633
0.168 4.0 12640 0.2246 0.3098 0.0607
0.15 5.0 15800 0.2179 0.3205 0.0595
0.1394 6.0 18960 0.2245 0.3060 0.0594
0.1283 7.0 22120 0.2173 0.3029 0.0600
0.1219 8.0 25280 0.2203 0.3183 0.0583
0.1155 9.0 28440 0.2148 0.2923 0.0573
0.1117 10.0 31600 0.2334 0.3037 0.0586
0.1031 11.0 34760 0.2162 0.2876 0.0578
0.0908 12.0 37920 0.2210 0.2883 0.0560
0.0804 13.0 41080 0.2271 0.3001 0.0581
0.0706 14.0 44240 0.2403 0.2753 0.0540
0.0602 15.0 47400 0.2528 0.2955 0.0578
0.0512 16.0 50560 0.2695 0.2883 0.0555
0.0432 17.0 53720 0.2597 0.2903 0.0554
0.0367 18.0 56880 0.2764 0.2850 0.0556
0.0317 19.0 60040 0.2954 0.2908 0.0570
0.0267 20.0 63200 0.3053 0.2878 0.0556
0.0236 21.0 66360 0.3087 0.2868 0.0565
0.0208 22.0 69520 0.2907 0.2970 0.0584
0.0175 23.0 72680 0.3274 0.2838 0.0550
0.0169 24.0 75840 0.3482 0.2832 0.0557

Framework versions

  • Transformers 4.46.0
  • Pytorch 2.1.0+cu118
  • Datasets 3.0.2
  • Tokenizers 0.20.1