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sewd-classifier-aug

Audio Classification
Transformers
TensorBoard
Safetensors
sew-d
Generated from Trainer
Inference Endpoints
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  1. README.md +58 -48
  2. runs/Jul13_14-51-02_LAPTOP-1GID9RGH/events.out.tfevents.1720857063.LAPTOP-1GID9RGH.18012.0 +2 -2
README.md CHANGED
@@ -20,12 +20,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [asapp/sew-d-tiny-100k-ft-ls100h](https://huggingface.co/asapp/sew-d-tiny-100k-ft-ls100h) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.3679
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- - Accuracy: 0.6092
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- - Precision: 0.6385
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- - Recall: 0.6092
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- - F1: 0.5780
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- - Binary: 0.7259
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  ## Model description
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@@ -59,48 +59,58 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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- | No log | 0.19 | 50 | 4.3617 | 0.0431 | 0.0054 | 0.0431 | 0.0091 | 0.2267 |
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- | No log | 0.38 | 100 | 4.1063 | 0.0593 | 0.0134 | 0.0593 | 0.0187 | 0.3046 |
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- | No log | 0.58 | 150 | 3.8795 | 0.1105 | 0.0541 | 0.1105 | 0.0502 | 0.3553 |
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- | No log | 0.77 | 200 | 3.6851 | 0.1294 | 0.0368 | 0.1294 | 0.0511 | 0.3768 |
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- | No log | 0.96 | 250 | 3.5025 | 0.2022 | 0.1342 | 0.2022 | 0.1289 | 0.4337 |
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- | 4.0829 | 1.15 | 300 | 3.3027 | 0.2049 | 0.1255 | 0.2049 | 0.1208 | 0.4394 |
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- | 4.0829 | 1.34 | 350 | 3.1674 | 0.2291 | 0.1067 | 0.2291 | 0.1305 | 0.4571 |
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- | 4.0829 | 1.53 | 400 | 3.0183 | 0.2453 | 0.1576 | 0.2453 | 0.1515 | 0.4677 |
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- | 4.0829 | 1.73 | 450 | 2.9047 | 0.2830 | 0.1807 | 0.2830 | 0.1898 | 0.4949 |
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- | 4.0829 | 1.92 | 500 | 2.7836 | 0.3181 | 0.2598 | 0.3181 | 0.2400 | 0.5194 |
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- | 3.3139 | 2.11 | 550 | 2.6784 | 0.3396 | 0.2432 | 0.3396 | 0.2484 | 0.5345 |
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- | 3.3139 | 2.3 | 600 | 2.5843 | 0.3261 | 0.2363 | 0.3261 | 0.2332 | 0.5259 |
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- | 3.3139 | 2.49 | 650 | 2.5050 | 0.3288 | 0.2625 | 0.3288 | 0.2465 | 0.5286 |
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- | 3.3139 | 2.68 | 700 | 2.3782 | 0.3531 | 0.3019 | 0.3531 | 0.2844 | 0.5456 |
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- | 3.3139 | 2.88 | 750 | 2.2826 | 0.4124 | 0.3926 | 0.4124 | 0.3473 | 0.5871 |
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- | 2.8692 | 3.07 | 800 | 2.2188 | 0.4151 | 0.3627 | 0.4151 | 0.3390 | 0.5889 |
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- | 2.8692 | 3.26 | 850 | 2.1541 | 0.4124 | 0.3370 | 0.4124 | 0.3348 | 0.5871 |
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- | 2.8692 | 3.45 | 900 | 2.0925 | 0.4016 | 0.3462 | 0.4016 | 0.3324 | 0.5787 |
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- | 2.8692 | 3.64 | 950 | 2.0181 | 0.4286 | 0.3550 | 0.4286 | 0.3612 | 0.5984 |
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- | 2.8692 | 3.84 | 1000 | 1.9575 | 0.4663 | 0.4514 | 0.4663 | 0.4074 | 0.6248 |
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- | 2.5712 | 4.03 | 1050 | 1.9088 | 0.4771 | 0.4544 | 0.4771 | 0.4229 | 0.6323 |
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- | 2.5712 | 4.22 | 1100 | 1.8500 | 0.4906 | 0.4284 | 0.4906 | 0.4235 | 0.6418 |
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- | 2.5712 | 4.41 | 1150 | 1.8270 | 0.4852 | 0.4312 | 0.4852 | 0.4222 | 0.6380 |
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- | 2.5712 | 4.6 | 1200 | 1.7758 | 0.4852 | 0.4556 | 0.4852 | 0.4306 | 0.6380 |
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- | 2.5712 | 4.79 | 1250 | 1.7430 | 0.4933 | 0.4552 | 0.4933 | 0.4388 | 0.6437 |
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- | 2.5712 | 4.99 | 1300 | 1.7188 | 0.5067 | 0.4941 | 0.5067 | 0.4624 | 0.6531 |
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- | 2.3754 | 5.18 | 1350 | 1.6813 | 0.5148 | 0.4820 | 0.5148 | 0.4668 | 0.6580 |
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- | 2.3754 | 5.37 | 1400 | 1.6463 | 0.5472 | 0.5302 | 0.5472 | 0.5029 | 0.6806 |
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- | 2.3754 | 5.56 | 1450 | 1.6446 | 0.5256 | 0.5247 | 0.5256 | 0.4801 | 0.6655 |
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- | 2.3754 | 5.75 | 1500 | 1.6005 | 0.5660 | 0.5579 | 0.5660 | 0.5235 | 0.6930 |
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- | 2.3754 | 5.94 | 1550 | 1.5667 | 0.5795 | 0.5561 | 0.5795 | 0.5363 | 0.7032 |
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- | 2.234 | 6.14 | 1600 | 1.5397 | 0.5741 | 0.5389 | 0.5741 | 0.5291 | 0.6995 |
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- | 2.234 | 6.33 | 1650 | 1.5235 | 0.5687 | 0.5444 | 0.5687 | 0.5219 | 0.6957 |
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- | 2.234 | 6.52 | 1700 | 1.5045 | 0.5849 | 0.5699 | 0.5849 | 0.5376 | 0.7070 |
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- | 2.234 | 6.71 | 1750 | 1.4936 | 0.5822 | 0.5635 | 0.5822 | 0.5383 | 0.7059 |
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- | 2.234 | 6.9 | 1800 | 1.4688 | 0.5822 | 0.5736 | 0.5822 | 0.5401 | 0.7059 |
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- | 2.1257 | 7.09 | 1850 | 1.4533 | 0.5957 | 0.6042 | 0.5957 | 0.5536 | 0.7154 |
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- | 2.1257 | 7.29 | 1900 | 1.4233 | 0.6038 | 0.6097 | 0.6038 | 0.5675 | 0.7202 |
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- | 2.1257 | 7.48 | 1950 | 1.4267 | 0.6038 | 0.6224 | 0.6038 | 0.5721 | 0.7210 |
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- | 2.1257 | 7.67 | 2000 | 1.4075 | 0.6146 | 0.6310 | 0.6146 | 0.5804 | 0.7278 |
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- | 2.1257 | 7.86 | 2050 | 1.3887 | 0.5984 | 0.6178 | 0.5984 | 0.5610 | 0.7173 |
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- | 2.0636 | 8.05 | 2100 | 1.3679 | 0.6092 | 0.6385 | 0.6092 | 0.5780 | 0.7259 |
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [asapp/sew-d-tiny-100k-ft-ls100h](https://huggingface.co/asapp/sew-d-tiny-100k-ft-ls100h) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.3895
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+ - Accuracy: 0.6280
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+ - Precision: 0.6286
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+ - Recall: 0.6280
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+ - F1: 0.5911
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+ - Binary: 0.7407
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 0.19 | 50 | 4.3686 | 0.0431 | 0.0158 | 0.0431 | 0.0125 | 0.2191 |
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+ | No log | 0.38 | 100 | 4.1099 | 0.0512 | 0.0096 | 0.0512 | 0.0109 | 0.2987 |
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+ | No log | 0.58 | 150 | 3.8483 | 0.0674 | 0.0273 | 0.0674 | 0.0229 | 0.3337 |
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+ | No log | 0.77 | 200 | 3.6126 | 0.0809 | 0.0229 | 0.0809 | 0.0292 | 0.3544 |
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+ | No log | 0.96 | 250 | 3.4229 | 0.1348 | 0.0686 | 0.1348 | 0.0691 | 0.3911 |
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+ | 4.077 | 1.15 | 300 | 3.2957 | 0.1698 | 0.0840 | 0.1698 | 0.0859 | 0.4183 |
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+ | 4.077 | 1.34 | 350 | 3.1928 | 0.2156 | 0.1085 | 0.2156 | 0.1245 | 0.4469 |
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+ | 4.077 | 1.53 | 400 | 3.0884 | 0.2075 | 0.0961 | 0.2075 | 0.1141 | 0.4420 |
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+ | 4.077 | 1.73 | 450 | 2.9780 | 0.2534 | 0.1994 | 0.2534 | 0.1676 | 0.4757 |
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+ | 4.077 | 1.92 | 500 | 2.8808 | 0.2884 | 0.2057 | 0.2884 | 0.1981 | 0.4987 |
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+ | 3.3556 | 2.11 | 550 | 2.7864 | 0.3100 | 0.2164 | 0.3100 | 0.2170 | 0.5156 |
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+ | 3.3556 | 2.3 | 600 | 2.7081 | 0.3369 | 0.2348 | 0.3369 | 0.2450 | 0.5361 |
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+ | 3.3556 | 2.49 | 650 | 2.6018 | 0.3423 | 0.2305 | 0.3423 | 0.2548 | 0.5391 |
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+ | 3.3556 | 2.68 | 700 | 2.5388 | 0.3531 | 0.2630 | 0.3531 | 0.2644 | 0.5458 |
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+ | 3.3556 | 2.88 | 750 | 2.4501 | 0.3558 | 0.2640 | 0.3558 | 0.2726 | 0.5493 |
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+ | 2.9854 | 3.07 | 800 | 2.3623 | 0.4232 | 0.3298 | 0.4232 | 0.3373 | 0.5965 |
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+ | 2.9854 | 3.26 | 850 | 2.2990 | 0.4232 | 0.3592 | 0.4232 | 0.3469 | 0.5951 |
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+ | 2.9854 | 3.45 | 900 | 2.2174 | 0.4259 | 0.3381 | 0.4259 | 0.3490 | 0.5992 |
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+ | 2.9854 | 3.64 | 950 | 2.1462 | 0.4555 | 0.3967 | 0.4555 | 0.3844 | 0.6199 |
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+ | 2.9854 | 3.84 | 1000 | 2.0908 | 0.4447 | 0.3910 | 0.4447 | 0.3737 | 0.6102 |
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+ | 2.6945 | 4.03 | 1050 | 2.0397 | 0.4528 | 0.3873 | 0.4528 | 0.3762 | 0.6191 |
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+ | 2.6945 | 4.22 | 1100 | 1.9789 | 0.4906 | 0.4262 | 0.4906 | 0.4216 | 0.6445 |
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+ | 2.6945 | 4.41 | 1150 | 1.9196 | 0.5229 | 0.4729 | 0.5229 | 0.4613 | 0.6671 |
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+ | 2.6945 | 4.6 | 1200 | 1.8807 | 0.4960 | 0.4391 | 0.4960 | 0.4328 | 0.6493 |
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+ | 2.6945 | 4.79 | 1250 | 1.8297 | 0.5175 | 0.4665 | 0.5175 | 0.4584 | 0.6633 |
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+ | 2.6945 | 4.99 | 1300 | 1.8099 | 0.5175 | 0.4805 | 0.5175 | 0.4550 | 0.6633 |
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+ | 2.4977 | 5.18 | 1350 | 1.7638 | 0.5283 | 0.4954 | 0.5283 | 0.4687 | 0.6709 |
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+ | 2.4977 | 5.37 | 1400 | 1.7227 | 0.5283 | 0.4549 | 0.5283 | 0.4608 | 0.6701 |
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+ | 2.4977 | 5.56 | 1450 | 1.6999 | 0.5472 | 0.5024 | 0.5472 | 0.4867 | 0.6833 |
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+ | 2.4977 | 5.75 | 1500 | 1.6623 | 0.5445 | 0.5207 | 0.5445 | 0.4919 | 0.6822 |
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+ | 2.4977 | 5.94 | 1550 | 1.6480 | 0.5499 | 0.5186 | 0.5499 | 0.4999 | 0.6860 |
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+ | 2.3471 | 6.14 | 1600 | 1.6190 | 0.5714 | 0.5378 | 0.5714 | 0.5109 | 0.7011 |
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+ | 2.3471 | 6.33 | 1650 | 1.6022 | 0.5687 | 0.5654 | 0.5687 | 0.5189 | 0.6992 |
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+ | 2.3471 | 6.52 | 1700 | 1.5881 | 0.5660 | 0.5306 | 0.5660 | 0.5074 | 0.6973 |
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+ | 2.3471 | 6.71 | 1750 | 1.5415 | 0.5795 | 0.5517 | 0.5795 | 0.5317 | 0.7067 |
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+ | 2.3471 | 6.9 | 1800 | 1.5210 | 0.5849 | 0.5541 | 0.5849 | 0.5374 | 0.7105 |
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+ | 2.2349 | 7.09 | 1850 | 1.4996 | 0.5984 | 0.5568 | 0.5984 | 0.5449 | 0.7199 |
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+ | 2.2349 | 7.29 | 1900 | 1.4846 | 0.6065 | 0.6233 | 0.6065 | 0.5622 | 0.7256 |
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+ | 2.2349 | 7.48 | 1950 | 1.4720 | 0.6065 | 0.6128 | 0.6065 | 0.5698 | 0.7256 |
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+ | 2.2349 | 7.67 | 2000 | 1.4549 | 0.6011 | 0.6045 | 0.6011 | 0.5640 | 0.7218 |
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+ | 2.2349 | 7.86 | 2050 | 1.4355 | 0.6307 | 0.6331 | 0.6307 | 0.5889 | 0.7426 |
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+ | 2.1754 | 8.05 | 2100 | 1.4426 | 0.6119 | 0.6166 | 0.6119 | 0.5702 | 0.7294 |
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+ | 2.1754 | 8.25 | 2150 | 1.4291 | 0.6226 | 0.6097 | 0.6226 | 0.5830 | 0.7369 |
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+ | 2.1754 | 8.44 | 2200 | 1.4291 | 0.6119 | 0.6037 | 0.6119 | 0.5696 | 0.7294 |
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+ | 2.1754 | 8.63 | 2250 | 1.4069 | 0.6307 | 0.6166 | 0.6307 | 0.5888 | 0.7426 |
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+ | 2.1754 | 8.82 | 2300 | 1.4038 | 0.6199 | 0.6132 | 0.6199 | 0.5793 | 0.7350 |
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+ | 2.138 | 9.01 | 2350 | 1.4045 | 0.6253 | 0.6265 | 0.6253 | 0.5848 | 0.7388 |
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+ | 2.138 | 9.2 | 2400 | 1.4043 | 0.6226 | 0.6060 | 0.6226 | 0.5833 | 0.7369 |
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+ | 2.138 | 9.4 | 2450 | 1.3902 | 0.6253 | 0.6109 | 0.6253 | 0.5846 | 0.7388 |
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+ | 2.138 | 9.59 | 2500 | 1.3906 | 0.6253 | 0.6125 | 0.6253 | 0.5849 | 0.7388 |
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+ | 2.138 | 9.78 | 2550 | 1.3915 | 0.6226 | 0.6075 | 0.6226 | 0.5838 | 0.7369 |
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+ | 2.138 | 9.97 | 2600 | 1.3895 | 0.6280 | 0.6286 | 0.6280 | 0.5911 | 0.7407 |
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  ### Framework versions
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