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vakyansh-wav2vec2-malayalam-mlm-8-audio-abuse-feature

This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-malayalam-mlm-8 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5558
  • Accuracy: 0.8118
  • Macro F1-score: 0.7576

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • 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 Macro F1-score
6.7625 0.77 10 6.7667 0.0 0.0
6.6935 1.54 20 6.5968 0.2527 0.0191
6.5445 2.31 30 6.3912 0.6909 0.4086
6.338 3.08 40 6.0995 0.6909 0.4086
6.1044 3.85 50 5.7554 0.6909 0.4086
5.755 4.62 60 5.4099 0.6909 0.4086
5.4695 5.38 70 5.1276 0.6909 0.4086
5.1723 6.15 80 4.8795 0.6909 0.4086
4.9648 6.92 90 4.6629 0.6909 0.4086
4.7414 7.69 100 4.4481 0.6909 0.4086
4.5793 8.46 110 4.2398 0.6909 0.4086
4.4455 9.23 120 4.0339 0.6909 0.4086
4.2287 10.0 130 3.8247 0.6909 0.4086
3.9367 10.77 140 3.6164 0.6909 0.4086
3.7916 11.54 150 3.4090 0.6909 0.4086
3.6112 12.31 160 3.2043 0.6909 0.4086
3.408 13.08 170 3.0023 0.6909 0.4086
3.1359 13.85 180 2.8029 0.6909 0.4086
2.9607 14.62 190 2.6125 0.6909 0.4086
2.83 15.38 200 2.4336 0.6909 0.4086
2.4853 16.15 210 2.2649 0.6909 0.4086
2.3841 16.92 220 2.1059 0.6909 0.4086
2.2296 17.69 230 1.9583 0.6909 0.4086
1.9631 18.46 240 1.8302 0.6909 0.4086
2.0456 19.23 250 1.7146 0.6909 0.4086
1.8406 20.0 260 1.6100 0.6909 0.4086
1.7127 20.77 270 1.5130 0.6909 0.4086
1.5241 21.54 280 1.4264 0.6909 0.4086
1.4366 22.31 290 1.3458 0.6909 0.4086
1.4368 23.08 300 1.2710 0.6909 0.4086
1.2664 23.85 310 1.2024 0.6909 0.4086
1.2681 24.62 320 1.1391 0.6909 0.4086
1.1518 25.38 330 1.0791 0.6909 0.4086
1.0681 26.15 340 1.0221 0.6909 0.4086
1.014 26.92 350 0.9679 0.6909 0.4086
0.9918 27.69 360 0.9197 0.6909 0.4086
1.0046 28.46 370 0.8839 0.6909 0.4086
0.9582 29.23 380 0.8422 0.6909 0.4086
0.927 30.0 390 0.8017 0.6909 0.4086
0.8853 30.77 400 0.7666 0.6909 0.4086
0.7872 31.54 410 0.7353 0.6909 0.4086
0.7773 32.31 420 0.7032 0.6909 0.4086
0.7163 33.08 430 0.6929 0.6909 0.4086
0.7686 33.85 440 0.6617 0.6909 0.4086
0.7504 34.62 450 0.6623 0.6909 0.4086
0.7491 35.38 460 0.6333 0.6909 0.4086
0.6688 36.15 470 0.6115 0.6962 0.4348
0.6785 36.92 480 0.5968 0.6909 0.4086
0.6511 37.69 490 0.5879 0.6909 0.4086
0.5906 38.46 500 0.5855 0.8253 0.7679
0.6 39.23 510 0.5837 0.8065 0.7299
0.604 40.0 520 0.5683 0.8226 0.7699
0.6269 40.77 530 0.5697 0.8065 0.7362
0.5643 41.54 540 0.5628 0.8199 0.7687
0.6269 42.31 550 0.5650 0.8145 0.7570
0.5965 43.08 560 0.5666 0.8065 0.7473
0.5578 43.85 570 0.5683 0.8065 0.7401
0.5571 44.62 580 0.5607 0.8172 0.7690
0.5511 45.38 590 0.5566 0.8145 0.7618
0.5404 46.15 600 0.5587 0.8091 0.7482
0.5708 46.92 610 0.5541 0.8172 0.7660
0.62 47.69 620 0.5524 0.8145 0.7618
0.6095 48.46 630 0.5573 0.8065 0.7438
0.5282 49.23 640 0.5559 0.8145 0.7586
0.5307 50.0 650 0.5558 0.8118 0.7576

Framework versions

  • Transformers 4.33.0
  • Pytorch 2.0.0
  • Datasets 2.1.0
  • Tokenizers 0.13.3
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