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w2v-bert-2.0-CV_Fleurs_AMMI_ALFFA-sw-5hrs-v1

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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_AMMI_ALFFA-sw-5hrs-v1
    results: []

w2v-bert-2.0-CV_Fleurs_AMMI_ALFFA-sw-5hrs-v1

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: 0.9564
  • Wer: 0.2347
  • Cer: 0.0832

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
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • 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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.3297 0.9972 175 0.7637 0.3590 0.1248
1.348 2.0 351 0.6631 0.3172 0.1132
1.0874 2.9972 526 0.6409 0.2720 0.0965
0.9039 4.0 702 0.5691 0.2759 0.1002
0.7405 4.9972 877 0.5492 0.2552 0.0905
0.6896 6.0 1053 0.6369 0.2470 0.0855
0.5831 6.9972 1228 0.5966 0.2508 0.0893
0.5089 8.0 1404 0.6115 0.2403 0.0857
0.4478 8.9972 1579 0.6523 0.2300 0.0810
0.4046 10.0 1755 0.6435 0.2459 0.0842
0.3745 10.9972 1930 0.6615 0.2336 0.0821
0.3461 12.0 2106 0.6885 0.2466 0.0850
0.3225 12.9972 2281 0.6068 0.2524 0.0871
0.278 14.0 2457 0.6808 0.2483 0.0850
0.2494 14.9972 2632 0.7234 0.2469 0.0846
0.2273 16.0 2808 0.7661 0.2414 0.0850
0.2022 16.9972 2983 0.8284 0.2451 0.0864
0.1811 18.0 3159 0.7355 0.2431 0.0855
0.1541 18.9972 3334 0.7872 0.2426 0.0860
0.1505 20.0 3510 0.7831 0.2523 0.0875
0.1373 20.9972 3685 0.8248 0.2366 0.0845
0.1213 22.0 3861 0.8190 0.2364 0.0826
0.1161 22.9972 4036 0.8505 0.2422 0.0849
0.1031 24.0 4212 0.9564 0.2347 0.0832

Framework versions

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