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metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - f1
model-index:
  - name: wav2vec2-base-finetuned-ks
    results: []

wav2vec2-base-finetuned-ks

This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1376
  • Accuracy: 0.8210
  • F1: 0.8209

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: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
1.3731 0.99 35 1.3532 0.3767 0.2859
1.3039 2.0 71 1.2740 0.4237 0.3434
1.2185 2.99 106 1.1573 0.5020 0.4423
1.0887 4.0 142 1.1107 0.5013 0.4389
1.0183 4.99 177 1.0801 0.5610 0.5348
0.8625 6.0 213 0.9364 0.6373 0.6285
0.7487 6.99 248 0.9735 0.6048 0.5867
0.6151 8.0 284 0.8946 0.6698 0.6735
0.5081 8.99 319 0.8748 0.6797 0.6855
0.4559 10.0 355 0.8701 0.6850 0.6832
0.4347 10.99 390 0.8887 0.7003 0.7040
0.2845 12.0 426 0.8715 0.7129 0.7145
0.275 12.99 461 0.8846 0.7268 0.7263
0.2301 14.0 497 0.8651 0.7261 0.7324
0.1657 14.99 532 0.8573 0.7473 0.7473
0.1593 16.0 568 0.8472 0.7420 0.7443
0.1398 16.99 603 0.7433 0.7825 0.7829
0.1318 18.0 639 0.7989 0.7739 0.7768
0.1425 18.99 674 0.7967 0.7759 0.7788
0.1116 20.0 710 0.8969 0.7659 0.7650
0.0716 20.99 745 0.9783 0.7434 0.7480
0.0909 22.0 781 0.9413 0.7593 0.7626
0.0691 22.99 816 0.9298 0.7832 0.7832
0.068 24.0 852 0.9522 0.7725 0.7744
0.0416 24.99 887 0.9624 0.7686 0.7746
0.0569 26.0 923 0.9376 0.7832 0.7832
0.0369 26.99 958 1.0163 0.7845 0.7843
0.0482 28.0 994 1.0013 0.7931 0.7895
0.0497 28.99 1029 1.1005 0.7725 0.7713
0.0427 30.0 1065 1.0346 0.7891 0.7901
0.0252 30.99 1100 1.0611 0.7871 0.7883
0.0268 32.0 1136 1.0436 0.7944 0.7962
0.022 32.99 1171 1.0217 0.8031 0.8012
0.0127 34.0 1207 1.0936 0.7971 0.7969
0.0153 34.99 1242 1.0777 0.8097 0.8055
0.0062 36.0 1278 1.2379 0.7699 0.7751
0.0081 36.99 1313 1.0697 0.7977 0.7987
0.0072 38.0 1349 1.1284 0.7997 0.8001
0.0105 38.99 1384 1.0593 0.8137 0.8136
0.0102 40.0 1420 1.0805 0.8130 0.8126
0.0088 40.99 1455 1.1237 0.8110 0.8115
0.0073 42.0 1491 1.0980 0.8170 0.8167
0.0046 42.99 1526 1.1584 0.8044 0.8049
0.0061 44.0 1562 1.1517 0.8110 0.8114
0.0021 44.99 1597 1.1564 0.8064 0.8074
0.0073 46.0 1633 1.1214 0.8183 0.8183
0.002 46.99 1668 1.1376 0.8210 0.8209
0.0064 48.0 1704 1.1283 0.8210 0.8208
0.0072 48.99 1739 1.1271 0.8203 0.8201
0.0019 49.3 1750 1.1273 0.8203 0.8201

Framework versions

  • Transformers 4.36.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0