Hugging Face's logo

fydhfzh
/
sewd-classifier-aug-ref

Audio Classification
Transformers
TensorBoard
Safetensors
sew-d
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
sewd-classifier-aug-ref / README.md
fydhfzh's picture
fydhfzh
End of training
bd190f1 verified
preview code
|
raw
history blame
8.11 kB
metadata
license: apache-2.0
base_model: asapp/sew-d-tiny-100k-ft-ls100h
tags:
  - generated_from_trainer
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: sewd-classifier-aug-ref
    results: []

sewd-classifier-aug-ref

This model is a fine-tuned version of asapp/sew-d-tiny-100k-ft-ls100h on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2210
  • Accuracy: 0.6402
  • Precision: 0.6291
  • Recall: 0.6402
  • F1: 0.6137
  • Binary: 0.7478

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
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy Precision Recall F1 Binary
No log 0.13 50 4.3839 0.0337 0.0029 0.0337 0.0052 0.2004
No log 0.27 100 4.1179 0.0687 0.0353 0.0687 0.0281 0.3295
No log 0.4 150 3.8682 0.0889 0.0335 0.0889 0.0338 0.3523
No log 0.54 200 3.6594 0.0997 0.0311 0.0997 0.0366 0.3635
No log 0.67 250 3.5084 0.1280 0.0534 0.1280 0.0494 0.3838
No log 0.81 300 3.3946 0.1469 0.0476 0.1469 0.0628 0.3964
No log 0.94 350 3.2570 0.1604 0.0699 0.1604 0.0830 0.4082
3.9151 1.08 400 3.1540 0.1806 0.0844 0.1806 0.0987 0.4224
3.9151 1.21 450 3.0449 0.1846 0.1000 0.1846 0.1005 0.4260
3.9151 1.35 500 2.9543 0.2237 0.1403 0.2237 0.1376 0.4534
3.9151 1.48 550 2.8691 0.2507 0.1621 0.2507 0.1606 0.4706
3.9151 1.62 600 2.7812 0.2493 0.1520 0.2493 0.1592 0.4718
3.9151 1.75 650 2.6598 0.2871 0.1855 0.2871 0.1856 0.4981
3.9151 1.89 700 2.6099 0.2951 0.2123 0.2951 0.2019 0.5047
3.1406 2.02 750 2.5039 0.3235 0.2106 0.3235 0.2230 0.5236
3.1406 2.16 800 2.4359 0.3383 0.2454 0.3383 0.2501 0.5358
3.1406 2.29 850 2.3869 0.3154 0.2329 0.3154 0.2324 0.5179
3.1406 2.43 900 2.3144 0.3612 0.2937 0.3612 0.2798 0.5513
3.1406 2.56 950 2.2470 0.3720 0.3122 0.3720 0.2908 0.5584
3.1406 2.7 1000 2.1944 0.3774 0.3099 0.3774 0.2992 0.5632
3.1406 2.83 1050 2.1421 0.4030 0.3250 0.4030 0.3226 0.5819
3.1406 2.97 1100 2.0630 0.4137 0.3442 0.4137 0.3336 0.5899
2.6974 3.1 1150 2.0115 0.4245 0.3679 0.4245 0.3510 0.5974
2.6974 3.24 1200 1.9716 0.4434 0.3964 0.4434 0.3729 0.6093
2.6974 3.37 1250 1.9255 0.4488 0.3972 0.4488 0.3883 0.6150
2.6974 3.51 1300 1.8715 0.4623 0.4112 0.4623 0.3969 0.6228
2.6974 3.64 1350 1.8223 0.4825 0.4534 0.4825 0.4222 0.6369
2.6974 3.78 1400 1.7951 0.5013 0.4728 0.5013 0.4500 0.6511
2.6974 3.91 1450 1.7427 0.5270 0.4855 0.5270 0.4804 0.6686
2.3963 4.05 1500 1.7319 0.5 0.4618 0.5 0.4452 0.6493
2.3963 4.18 1550 1.7098 0.4960 0.4588 0.4960 0.4454 0.6473
2.3963 4.32 1600 1.6518 0.5310 0.5051 0.5310 0.4855 0.6709
2.3963 4.45 1650 1.6535 0.5067 0.4838 0.5067 0.4552 0.6539
2.3963 4.59 1700 1.6011 0.5539 0.5106 0.5539 0.5061 0.6865
2.3963 4.72 1750 1.5894 0.5404 0.4940 0.5404 0.4923 0.6767
2.3963 4.86 1800 1.5580 0.5660 0.5371 0.5660 0.5285 0.6964
2.3963 4.99 1850 1.5375 0.5431 0.5032 0.5431 0.4968 0.6803
2.1926 5.12 1900 1.5166 0.5620 0.5237 0.5620 0.5193 0.6941
2.1926 5.26 1950 1.5168 0.5526 0.5198 0.5526 0.5085 0.6860
2.1926 5.39 2000 1.4773 0.5836 0.5615 0.5836 0.5455 0.7073
2.1926 5.53 2050 1.4488 0.5782 0.5564 0.5782 0.5396 0.7054
2.1926 5.66 2100 1.4335 0.5916 0.5691 0.5916 0.5560 0.7143
2.1926 5.8 2150 1.4078 0.5957 0.5782 0.5957 0.5641 0.7177
2.1926 5.93 2200 1.4092 0.5863 0.5691 0.5863 0.5506 0.7105
2.0446 6.07 2250 1.3942 0.5755 0.5405 0.5755 0.5334 0.7026
2.0446 6.2 2300 1.3828 0.5930 0.5776 0.5930 0.5613 0.7148
2.0446 6.34 2350 1.3625 0.6065 0.5886 0.6065 0.5688 0.7247
2.0446 6.47 2400 1.3444 0.6119 0.6008 0.6119 0.5754 0.7284
2.0446 6.61 2450 1.3088 0.6267 0.6134 0.6267 0.5914 0.7388
2.0446 6.74 2500 1.3183 0.6038 0.5869 0.6038 0.5729 0.7228
2.0446 6.88 2550 1.3000 0.6173 0.5886 0.6173 0.5810 0.7322
1.9441 7.01 2600 1.2930 0.6213 0.6048 0.6213 0.5900 0.7341
1.9441 7.15 2650 1.2757 0.6226 0.6097 0.6226 0.5959 0.7361
1.9441 7.28 2700 1.2787 0.6226 0.6091 0.6226 0.5963 0.7361
1.9441 7.42 2750 1.2566 0.6240 0.6204 0.6240 0.5983 0.7375
1.9441 7.55 2800 1.2549 0.6253 0.6055 0.6253 0.5970 0.7380
1.9441 7.69 2850 1.2396 0.6240 0.6255 0.6240 0.5954 0.7371
1.9441 7.82 2900 1.2400 0.6388 0.6361 0.6388 0.6128 0.7478
1.9441 7.96 2950 1.2369 0.6294 0.6188 0.6294 0.5996 0.7407
1.8636 8.09 3000 1.2235 0.6375 0.6363 0.6375 0.6151 0.7460
1.8636 8.23 3050 1.2178 0.6456 0.6415 0.6456 0.6196 0.7520
1.8636 8.36 3100 1.2093 0.6402 0.6346 0.6402 0.6149 0.7482
1.8636 8.5 3150 1.2210 0.6402 0.6291 0.6402 0.6137 0.7478

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.3.0
  • Datasets 2.19.1
  • Tokenizers 0.15.1