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hubert-classifier-aug-fold-0-old

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hubert
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  1. README.md +68 -48
  2. model.safetensors +1 -1
  3. runs/Jun28_02-16-17_LAPTOP-1GID9RGH/events.out.tfevents.1719517301.LAPTOP-1GID9RGH.18912.1 +3 -0
README.md CHANGED
@@ -9,23 +9,23 @@ metrics:
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  - recall
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  - f1
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  model-index:
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- - name: hubert-classifier-aug-fold-9
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  results: []
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  should probably proofread and complete it, then remove this comment. -->
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- # hubert-classifier-aug-fold-9
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  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 4.4196
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- - Accuracy: 0.0162
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- - Precision: 0.0003
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- - Recall: 0.0162
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- - F1: 0.0005
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- - Binary: 0.1388
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  ## Model description
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@@ -59,46 +59,66 @@ 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 | 4.4284 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | No log | 0.38 | 100 | 4.4249 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1296 |
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- | No log | 0.58 | 150 | 4.4280 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1353 |
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- | No log | 0.77 | 200 | 4.4229 | 0.0108 | 0.0001 | 0.0108 | 0.0002 | 0.1315 |
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- | No log | 0.96 | 250 | 4.4254 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4398 | 1.15 | 300 | 4.4262 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4398 | 1.34 | 350 | 4.4237 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4398 | 1.53 | 400 | 4.4237 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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- | 4.4398 | 1.73 | 450 | 4.4233 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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- | 4.4398 | 1.92 | 500 | 4.4241 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4196 | 2.11 | 550 | 4.4240 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4196 | 2.3 | 600 | 4.4236 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1213 |
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- | 4.4196 | 2.49 | 650 | 4.4227 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4196 | 2.68 | 700 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1353 |
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- | 4.4196 | 2.88 | 750 | 4.4234 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4189 | 3.07 | 800 | 4.4234 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4189 | 3.26 | 850 | 4.4240 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4189 | 3.45 | 900 | 4.4230 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4189 | 3.64 | 950 | 4.4230 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4189 | 3.84 | 1000 | 4.4225 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1315 |
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- | 4.4185 | 4.03 | 1050 | 4.4233 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4185 | 4.22 | 1100 | 4.4229 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4185 | 4.41 | 1150 | 4.4226 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4185 | 4.6 | 1200 | 4.4223 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4185 | 4.79 | 1250 | 4.4234 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4185 | 4.99 | 1300 | 4.4225 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1302 |
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- | 4.4351 | 5.18 | 1350 | 4.4225 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4351 | 5.37 | 1400 | 4.4231 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4351 | 5.56 | 1450 | 4.4233 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4351 | 5.75 | 1500 | 4.4220 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4351 | 5.94 | 1550 | 4.4217 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4178 | 6.14 | 1600 | 4.4225 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4178 | 6.33 | 1650 | 4.4217 | 0.0162 | 0.0003 | 0.0162 | 0.0005 | 0.1321 |
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- | 4.4178 | 6.52 | 1700 | 4.4222 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4178 | 6.71 | 1750 | 4.4226 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4178 | 6.9 | 1800 | 4.4222 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4175 | 7.09 | 1850 | 4.4232 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4175 | 7.29 | 1900 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4175 | 7.48 | 1950 | 4.4231 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
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- | 4.4175 | 7.67 | 2000 | 4.4235 | 0.0135 | 0.0002 | 0.0135 | 0.0004 | 0.1334 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
9
  - recall
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  - f1
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  model-index:
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+ - name: hubert-classifier-aug-fold-0
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  results: []
14
  ---
15
 
16
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
  should probably proofread and complete it, then remove this comment. -->
18
 
19
+ # hubert-classifier-aug-fold-0
20
 
21
  This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
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+ - Loss: 0.6445
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+ - Accuracy: 0.7978
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+ - Precision: 0.8206
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+ - Recall: 0.7978
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+ - F1: 0.7858
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+ - Binary: 0.8580
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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 | 4.3930 | 0.0216 | 0.0009 | 0.0216 | 0.0017 | 0.1358 |
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+ | No log | 0.38 | 100 | 4.0474 | 0.0270 | 0.0008 | 0.0270 | 0.0016 | 0.2286 |
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+ | No log | 0.58 | 150 | 3.8906 | 0.0296 | 0.0013 | 0.0296 | 0.0025 | 0.2442 |
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+ | No log | 0.77 | 200 | 3.6015 | 0.0485 | 0.0029 | 0.0485 | 0.0053 | 0.3294 |
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+ | No log | 0.96 | 250 | 3.5656 | 0.0539 | 0.0138 | 0.0539 | 0.0140 | 0.3226 |
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+ | 4.0634 | 1.15 | 300 | 3.4188 | 0.0485 | 0.0066 | 0.0485 | 0.0105 | 0.3310 |
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+ | 4.0634 | 1.34 | 350 | 3.3200 | 0.0728 | 0.0113 | 0.0728 | 0.0190 | 0.3461 |
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+ | 4.0634 | 1.53 | 400 | 3.1522 | 0.0809 | 0.0132 | 0.0809 | 0.0210 | 0.3534 |
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+ | 4.0634 | 1.73 | 450 | 3.0267 | 0.1078 | 0.0260 | 0.1078 | 0.0363 | 0.3747 |
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+ | 4.0634 | 1.92 | 500 | 2.9975 | 0.1024 | 0.0334 | 0.1024 | 0.0409 | 0.3677 |
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+ | 3.3451 | 2.11 | 550 | 2.7835 | 0.1617 | 0.0980 | 0.1617 | 0.0936 | 0.4108 |
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+ | 3.3451 | 2.3 | 600 | 2.6777 | 0.2237 | 0.1048 | 0.2237 | 0.1246 | 0.4561 |
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+ | 3.3451 | 2.49 | 650 | 2.4946 | 0.2642 | 0.1847 | 0.2642 | 0.1754 | 0.4833 |
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+ | 3.3451 | 2.68 | 700 | 2.3266 | 0.2776 | 0.2121 | 0.2776 | 0.1948 | 0.4919 |
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+ | 3.3451 | 2.88 | 750 | 2.1236 | 0.3558 | 0.2876 | 0.3558 | 0.2704 | 0.5461 |
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+ | 2.8034 | 3.07 | 800 | 1.9687 | 0.3908 | 0.3357 | 0.3908 | 0.3092 | 0.5712 |
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+ | 2.8034 | 3.26 | 850 | 1.8541 | 0.4151 | 0.3439 | 0.4151 | 0.3410 | 0.5873 |
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+ | 2.8034 | 3.45 | 900 | 1.7298 | 0.4798 | 0.4299 | 0.4798 | 0.4120 | 0.6342 |
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+ | 2.8034 | 3.64 | 950 | 1.6094 | 0.5148 | 0.4804 | 0.5148 | 0.4505 | 0.6590 |
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+ | 2.8034 | 3.84 | 1000 | 1.5433 | 0.5526 | 0.5102 | 0.5526 | 0.4910 | 0.6863 |
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+ | 2.2398 | 4.03 | 1050 | 1.4371 | 0.5687 | 0.5274 | 0.5687 | 0.5078 | 0.6960 |
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+ | 2.2398 | 4.22 | 1100 | 1.2423 | 0.6334 | 0.6342 | 0.6334 | 0.6024 | 0.7445 |
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+ | 2.2398 | 4.41 | 1150 | 1.1731 | 0.6631 | 0.6428 | 0.6631 | 0.6210 | 0.7625 |
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+ | 2.2398 | 4.6 | 1200 | 1.1174 | 0.7008 | 0.7177 | 0.7008 | 0.6778 | 0.7900 |
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+ | 2.2398 | 4.79 | 1250 | 1.0677 | 0.6954 | 0.7081 | 0.6954 | 0.6674 | 0.7854 |
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+ | 2.2398 | 4.99 | 1300 | 1.0534 | 0.6739 | 0.6889 | 0.6739 | 0.6480 | 0.7712 |
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+ | 1.8823 | 5.18 | 1350 | 1.0200 | 0.7035 | 0.7035 | 0.7035 | 0.6711 | 0.7930 |
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+ | 1.8823 | 5.37 | 1400 | 0.9667 | 0.7035 | 0.7223 | 0.7035 | 0.6864 | 0.7919 |
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+ | 1.8823 | 5.56 | 1450 | 0.9057 | 0.7197 | 0.7433 | 0.7197 | 0.6972 | 0.8043 |
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+ | 1.8823 | 5.75 | 1500 | 0.8284 | 0.7547 | 0.7680 | 0.7547 | 0.7348 | 0.8296 |
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+ | 1.8823 | 5.94 | 1550 | 0.8156 | 0.7439 | 0.7708 | 0.7439 | 0.7310 | 0.8205 |
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+ | 1.6355 | 6.14 | 1600 | 0.8034 | 0.7412 | 0.7776 | 0.7412 | 0.7313 | 0.8194 |
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+ | 1.6355 | 6.33 | 1650 | 0.8032 | 0.7547 | 0.7768 | 0.7547 | 0.7430 | 0.8307 |
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+ | 1.6355 | 6.52 | 1700 | 0.8030 | 0.7412 | 0.7495 | 0.7412 | 0.7195 | 0.8213 |
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+ | 1.6355 | 6.71 | 1750 | 0.7365 | 0.7898 | 0.8217 | 0.7898 | 0.7786 | 0.8542 |
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+ | 1.6355 | 6.9 | 1800 | 0.7149 | 0.7817 | 0.8157 | 0.7817 | 0.7653 | 0.8493 |
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+ | 1.4501 | 7.09 | 1850 | 0.7493 | 0.7790 | 0.8200 | 0.7790 | 0.7698 | 0.8453 |
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+ | 1.4501 | 7.29 | 1900 | 0.7022 | 0.8086 | 0.8316 | 0.8086 | 0.8003 | 0.8663 |
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+ | 1.4501 | 7.48 | 1950 | 0.6849 | 0.7978 | 0.8226 | 0.7978 | 0.7874 | 0.8596 |
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+ | 1.4501 | 7.67 | 2000 | 0.6008 | 0.8464 | 0.8689 | 0.8464 | 0.8436 | 0.8927 |
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+ | 1.4501 | 7.86 | 2050 | 0.6288 | 0.8194 | 0.8449 | 0.8194 | 0.8112 | 0.8757 |
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+ | 1.3207 | 8.05 | 2100 | 0.6410 | 0.8167 | 0.8407 | 0.8167 | 0.8051 | 0.8722 |
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+ | 1.3207 | 8.25 | 2150 | 0.6128 | 0.8086 | 0.8318 | 0.8086 | 0.8031 | 0.8663 |
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+ | 1.3207 | 8.44 | 2200 | 0.6072 | 0.8113 | 0.8337 | 0.8113 | 0.8053 | 0.8682 |
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+ | 1.3207 | 8.63 | 2250 | 0.5702 | 0.8275 | 0.8519 | 0.8275 | 0.8210 | 0.8795 |
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+ | 1.3207 | 8.82 | 2300 | 0.6059 | 0.8140 | 0.8373 | 0.8140 | 0.8079 | 0.8714 |
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+ | 1.2239 | 9.01 | 2350 | 0.5053 | 0.8491 | 0.8685 | 0.8491 | 0.8453 | 0.8954 |
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+ | 1.2239 | 9.2 | 2400 | 0.5551 | 0.8383 | 0.8657 | 0.8383 | 0.8339 | 0.8871 |
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+ | 1.2239 | 9.4 | 2450 | 0.5767 | 0.8248 | 0.8571 | 0.8248 | 0.8201 | 0.8776 |
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+ | 1.2239 | 9.59 | 2500 | 0.5514 | 0.8383 | 0.8618 | 0.8383 | 0.8331 | 0.8852 |
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+ | 1.2239 | 9.78 | 2550 | 0.5911 | 0.8248 | 0.8542 | 0.8248 | 0.8195 | 0.8765 |
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+ | 1.2239 | 9.97 | 2600 | 0.5498 | 0.8302 | 0.8677 | 0.8302 | 0.8267 | 0.8814 |
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+ | 1.1305 | 10.16 | 2650 | 0.4722 | 0.8706 | 0.8928 | 0.8706 | 0.8672 | 0.9086 |
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+ | 1.1305 | 10.35 | 2700 | 0.5509 | 0.8302 | 0.8654 | 0.8302 | 0.8261 | 0.8814 |
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+ | 1.1305 | 10.55 | 2750 | 0.5590 | 0.8383 | 0.8713 | 0.8383 | 0.8336 | 0.8863 |
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+ | 1.1305 | 10.74 | 2800 | 0.5053 | 0.8464 | 0.8702 | 0.8464 | 0.8424 | 0.8919 |
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+ | 1.1305 | 10.93 | 2850 | 0.5135 | 0.8329 | 0.8548 | 0.8329 | 0.8272 | 0.8822 |
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+ | 1.0868 | 11.12 | 2900 | 0.5438 | 0.8221 | 0.8579 | 0.8221 | 0.8145 | 0.8757 |
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+ | 1.0868 | 11.31 | 2950 | 0.5633 | 0.8410 | 0.8720 | 0.8410 | 0.8348 | 0.8881 |
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+ | 1.0868 | 11.51 | 3000 | 0.5050 | 0.8410 | 0.8664 | 0.8410 | 0.8389 | 0.8889 |
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  ### Framework versions
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