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xtreme_s_w2v2_t5lephone-small_minds14.en-US

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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_t5lephone-small_minds14.en-US
    results: []

xtreme_s_w2v2_t5lephone-small_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: 1.5203
  • F1: 0.7526
  • Accuracy: 0.7518

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
2.589 3.95 20 2.6401 0.0108 0.0816
2.5223 7.95 40 2.6493 0.0339 0.0816
2.5085 11.95 60 2.6236 0.0539 0.1028
2.1252 15.95 80 2.5006 0.1458 0.1667
1.3711 19.95 100 2.2712 0.2344 0.2837
1.5092 23.95 120 2.0599 0.3631 0.3936
0.4962 27.95 140 1.8475 0.4881 0.4894
0.4169 31.95 160 1.8262 0.5358 0.5142
0.1579 35.95 180 1.6481 0.5967 0.6028
0.0927 39.95 200 1.4470 0.6748 0.6560
0.1363 43.95 220 1.2725 0.6836 0.6879
0.1324 47.95 240 1.4330 0.6653 0.6702
0.0294 51.95 260 1.2978 0.7079 0.7163
0.0326 55.95 280 1.3869 0.6823 0.6879
0.0444 59.95 300 1.5764 0.7051 0.6986
0.0527 63.95 320 2.2013 0.5899 0.5851
0.1542 67.95 340 1.5203 0.7053 0.6986
0.0127 71.95 360 1.7149 0.7105 0.7128
0.0105 75.95 380 1.2471 0.7853 0.7837
0.009 79.95 400 1.5720 0.7065 0.7057
0.0081 83.95 420 1.9395 0.6656 0.6702
0.2345 87.95 440 1.5704 0.7408 0.7411
0.0076 91.95 460 1.4706 0.7554 0.7589
0.0064 95.95 480 1.5746 0.7491 0.7518
0.3105 99.95 500 1.6824 0.7273 0.7376
0.0058 103.95 520 1.3799 0.7474 0.7624
0.0055 107.95 540 1.4086 0.7350 0.7518
0.0051 111.95 560 1.2832 0.7874 0.7979
0.0052 115.95 580 1.3474 0.7752 0.7801
0.0046 119.95 600 1.6125 0.7451 0.7482
0.0044 123.95 620 1.5927 0.7486 0.7518
0.0044 127.95 640 1.5551 0.7487 0.7518
0.0041 131.95 660 1.5117 0.7631 0.7660
0.0041 135.95 680 1.5210 0.7577 0.7624
0.0041 139.95 700 1.5145 0.7655 0.7660
0.004 143.95 720 1.5053 0.7665 0.7660
0.004 147.95 740 1.5203 0.7526 0.7518

Framework versions

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