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Splend1dchan
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xtreme_s_w2v2_minds14.en-US

Transformers
PyTorch
TensorBoard
wav2vec2
minds14
google/xtreme_s
Generated from Trainer
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metadata
language:
  - en-US
license: apache-2.0
tags:
  - minds14
  - google/xtreme_s
  - generated_from_trainer
datasets:
  - xtreme_s
metrics:
  - f1
  - accuracy
model-index:
  - name: xtreme_s_w2v2_minds14.en-US
    results: []

xtreme_s_w2v2_minds14.en-US

This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the GOOGLE/XTREME_S - MINDS14.EN-US dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5337
  • F1: 0.9144
  • Accuracy: 0.9113

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: 2
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 150.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1 Accuracy
2.6482 3.95 20 2.6421 0.0242 0.0745
2.6292 7.95 40 2.6359 0.0108 0.0816
2.5993 11.95 60 2.6301 0.0167 0.0674
2.4023 15.95 80 2.5514 0.1105 0.1454
1.4015 19.95 100 1.6843 0.5599 0.5851
0.4379 23.95 120 0.8126 0.7921 0.7908
0.0642 27.95 140 0.7178 0.8158 0.8156
0.0376 31.95 160 0.7286 0.8473 0.8475
0.0185 35.95 180 0.6779 0.8719 0.8723
0.0752 39.95 200 0.7096 0.8578 0.8511
0.0266 43.95 220 0.7655 0.8596 0.8546
0.0078 47.95 240 0.7623 0.8563 0.8511
0.007 51.95 260 0.6620 0.8794 0.8759
0.0047 55.95 280 0.5936 0.9045 0.9007
0.0067 59.95 300 0.8279 0.8546 0.8617
0.0394 63.95 320 0.8766 0.8359 0.8227
0.0051 67.95 340 0.8097 0.8483 0.8475
0.0095 71.95 360 0.6095 0.9083 0.9078
0.0026 75.95 380 0.5286 0.8889 0.8865
0.0023 79.95 400 0.7218 0.8926 0.8936
0.0023 83.95 420 0.6551 0.8997 0.8972
0.0027 87.95 440 0.6664 0.8848 0.8794
0.0019 91.95 460 0.5344 0.9032 0.9043
0.002 95.95 480 0.5863 0.8983 0.9007
0.0015 99.95 500 0.5715 0.9047 0.9043
0.0016 103.95 520 0.5615 0.8956 0.8936
0.0014 107.95 540 0.6353 0.8965 0.8936
0.0014 111.95 560 0.5593 0.9041 0.9007
0.0013 115.95 580 0.6041 0.8977 0.8936
0.0013 119.95 600 0.5794 0.9026 0.9007
0.0012 123.95 620 0.6858 0.9003 0.8972
0.0013 127.95 640 0.6730 0.9002 0.8972
0.0013 131.95 660 0.5707 0.9146 0.9113
0.0012 135.95 680 0.5604 0.9153 0.9113
0.0019 139.95 700 0.5468 0.9114 0.9078
0.0015 143.95 720 0.5361 0.9144 0.9113
0.0012 147.95 740 0.5337 0.9144 0.9113

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.1.0
  • Tokenizers 0.12.1