Hugging Face's logo

fydhfzh
/
hubert-classifier-aug-fold-2-old

Audio Classification
Transformers
TensorBoard
Safetensors
hubert
Generated from Trainer
Inference Endpoints
Model card Files Metrics Community
fydhfzh commited on
Commit
ffcafef
•
1 Parent(s): a08808c

End of training

Browse files
Files changed (3) hide show
  1. README.md +104 -83
  2. runs/Jun28_12-52-19_LAPTOP-1GID9RGH/events.out.tfevents.1719553941.LAPTOP-1GID9RGH.1516.4 +2 -2
  3. runs/Jun28_12-52-19_LAPTOP-1GID9RGH/events.out.tfevents.1719556263.LAPTOP-1GID9RGH.1516.5 +3 -0
README.md CHANGED
@@ -20,12 +20,12 @@ should probably proofread and complete it, then remove this comment. -->
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:
23
- - Loss: 0.5430
24
- - Accuracy: 0.8491
25
- - Precision: 0.8708
26
- - Recall: 0.8491
27
- - F1: 0.8458
28
- - Binary: 0.8927
29
 
30
  ## Model description
31
 
@@ -59,83 +59,104 @@ The following hyperparameters were used during training:
59
 
60
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
61
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
62
- | No log | 0.19 | 50 | 3.9323 | 0.0485 | 0.0031 | 0.0485 | 0.0057 | 0.3261 |
63
- | No log | 0.38 | 100 | 3.4684 | 0.0539 | 0.0179 | 0.0539 | 0.0123 | 0.3313 |
64
- | No log | 0.58 | 150 | 3.2846 | 0.0809 | 0.0187 | 0.0809 | 0.0276 | 0.3544 |
65
- | No log | 0.77 | 200 | 3.1760 | 0.1051 | 0.0548 | 0.1051 | 0.0442 | 0.3720 |
66
- | No log | 0.96 | 250 | 2.9745 | 0.1725 | 0.0938 | 0.1725 | 0.0937 | 0.4199 |
67
- | 3.6721 | 1.15 | 300 | 2.7015 | 0.2722 | 0.1857 | 0.2722 | 0.1809 | 0.4898 |
68
- | 3.6721 | 1.34 | 350 | 2.4436 | 0.2615 | 0.1492 | 0.2615 | 0.1576 | 0.4798 |
69
- | 3.6721 | 1.53 | 400 | 2.2415 | 0.3288 | 0.2190 | 0.3288 | 0.2345 | 0.5240 |
70
- | 3.6721 | 1.73 | 450 | 2.0686 | 0.4070 | 0.3119 | 0.4070 | 0.3201 | 0.5841 |
71
- | 3.6721 | 1.92 | 500 | 1.9477 | 0.3989 | 0.3383 | 0.3989 | 0.3187 | 0.5776 |
72
- | 2.7219 | 2.11 | 550 | 1.8375 | 0.4070 | 0.3327 | 0.4070 | 0.3307 | 0.5846 |
73
- | 2.7219 | 2.3 | 600 | 1.6781 | 0.4825 | 0.4173 | 0.4825 | 0.4048 | 0.6380 |
74
- | 2.7219 | 2.49 | 650 | 1.5253 | 0.5526 | 0.5324 | 0.5526 | 0.4927 | 0.6863 |
75
- | 2.7219 | 2.68 | 700 | 1.4445 | 0.5606 | 0.5282 | 0.5606 | 0.5004 | 0.6919 |
76
- | 2.7219 | 2.88 | 750 | 1.3314 | 0.6146 | 0.6177 | 0.6146 | 0.5699 | 0.7323 |
77
- | 2.1118 | 3.07 | 800 | 1.2297 | 0.6604 | 0.6628 | 0.6604 | 0.6247 | 0.7636 |
78
- | 2.1118 | 3.26 | 850 | 1.1280 | 0.6792 | 0.6951 | 0.6792 | 0.6484 | 0.7749 |
79
- | 2.1118 | 3.45 | 900 | 1.0082 | 0.7224 | 0.7139 | 0.7224 | 0.6899 | 0.8049 |
80
- | 2.1118 | 3.64 | 950 | 1.0459 | 0.7008 | 0.6809 | 0.7008 | 0.6622 | 0.7919 |
81
- | 2.1118 | 3.84 | 1000 | 1.0364 | 0.7197 | 0.7428 | 0.7197 | 0.6972 | 0.8040 |
82
- | 1.76 | 4.03 | 1050 | 0.8991 | 0.7466 | 0.7552 | 0.7466 | 0.7243 | 0.8218 |
83
- | 1.76 | 4.22 | 1100 | 0.8680 | 0.7493 | 0.7761 | 0.7493 | 0.7310 | 0.8245 |
84
- | 1.76 | 4.41 | 1150 | 0.7998 | 0.7925 | 0.8125 | 0.7925 | 0.7793 | 0.8539 |
85
- | 1.76 | 4.6 | 1200 | 0.7608 | 0.8086 | 0.8378 | 0.8086 | 0.7985 | 0.8674 |
86
- | 1.76 | 4.79 | 1250 | 0.7963 | 0.7871 | 0.7982 | 0.7871 | 0.7696 | 0.8512 |
87
- | 1.76 | 4.99 | 1300 | 0.7944 | 0.7763 | 0.8052 | 0.7763 | 0.7638 | 0.8437 |
88
- | 1.536 | 5.18 | 1350 | 0.8100 | 0.7844 | 0.8113 | 0.7844 | 0.7707 | 0.8482 |
89
- | 1.536 | 5.37 | 1400 | 0.7404 | 0.8059 | 0.8128 | 0.8059 | 0.7907 | 0.8633 |
90
- | 1.536 | 5.56 | 1450 | 0.6682 | 0.8167 | 0.8413 | 0.8167 | 0.8074 | 0.8709 |
91
- | 1.536 | 5.75 | 1500 | 0.5892 | 0.8302 | 0.8440 | 0.8302 | 0.8216 | 0.8803 |
92
- | 1.536 | 5.94 | 1550 | 0.6009 | 0.8329 | 0.8543 | 0.8329 | 0.8250 | 0.8822 |
93
- | 1.3881 | 6.14 | 1600 | 0.6010 | 0.8598 | 0.8767 | 0.8598 | 0.8534 | 0.9011 |
94
- | 1.3881 | 6.33 | 1650 | 0.5969 | 0.8329 | 0.8601 | 0.8329 | 0.8252 | 0.8822 |
95
- | 1.3881 | 6.52 | 1700 | 0.6715 | 0.8194 | 0.8546 | 0.8194 | 0.8104 | 0.8728 |
96
- | 1.3881 | 6.71 | 1750 | 0.5895 | 0.8329 | 0.8589 | 0.8329 | 0.8246 | 0.8833 |
97
- | 1.3881 | 6.9 | 1800 | 0.6164 | 0.8437 | 0.8736 | 0.8437 | 0.8388 | 0.8906 |
98
- | 1.2734 | 7.09 | 1850 | 0.6407 | 0.8383 | 0.8597 | 0.8383 | 0.8263 | 0.8860 |
99
- | 1.2734 | 7.29 | 1900 | 0.6194 | 0.8383 | 0.8685 | 0.8383 | 0.8325 | 0.8860 |
100
- | 1.2734 | 7.48 | 1950 | 0.6108 | 0.8221 | 0.8545 | 0.8221 | 0.8107 | 0.8747 |
101
- | 1.2734 | 7.67 | 2000 | 0.5130 | 0.8652 | 0.8835 | 0.8652 | 0.8605 | 0.9049 |
102
- | 1.2734 | 7.86 | 2050 | 0.4984 | 0.8571 | 0.8704 | 0.8571 | 0.8466 | 0.9000 |
103
- | 1.1711 | 8.05 | 2100 | 0.5369 | 0.8491 | 0.8616 | 0.8491 | 0.8401 | 0.8943 |
104
- | 1.1711 | 8.25 | 2150 | 0.5673 | 0.8437 | 0.8739 | 0.8437 | 0.8370 | 0.8898 |
105
- | 1.1711 | 8.44 | 2200 | 0.5658 | 0.8437 | 0.8725 | 0.8437 | 0.8389 | 0.8898 |
106
- | 1.1711 | 8.63 | 2250 | 0.5673 | 0.8544 | 0.8768 | 0.8544 | 0.8511 | 0.8984 |
107
- | 1.1711 | 8.82 | 2300 | 0.5246 | 0.8679 | 0.8884 | 0.8679 | 0.8637 | 0.9067 |
108
- | 1.1145 | 9.01 | 2350 | 0.4924 | 0.8814 | 0.9071 | 0.8814 | 0.8784 | 0.9162 |
109
- | 1.1145 | 9.2 | 2400 | 0.5114 | 0.8625 | 0.8839 | 0.8625 | 0.8560 | 0.9038 |
110
- | 1.1145 | 9.4 | 2450 | 0.5047 | 0.8571 | 0.8832 | 0.8571 | 0.8515 | 0.8992 |
111
- | 1.1145 | 9.59 | 2500 | 0.4966 | 0.8868 | 0.9030 | 0.8868 | 0.8816 | 0.9208 |
112
- | 1.1145 | 9.78 | 2550 | 0.4980 | 0.8733 | 0.8981 | 0.8733 | 0.8689 | 0.9113 |
113
- | 1.1145 | 9.97 | 2600 | 0.5202 | 0.8787 | 0.9019 | 0.8787 | 0.8720 | 0.9151 |
114
- | 1.0456 | 10.16 | 2650 | 0.4958 | 0.8760 | 0.8982 | 0.8760 | 0.8691 | 0.9132 |
115
- | 1.0456 | 10.35 | 2700 | 0.4336 | 0.8733 | 0.8924 | 0.8733 | 0.8667 | 0.9113 |
116
- | 1.0456 | 10.55 | 2750 | 0.4368 | 0.8922 | 0.9103 | 0.8922 | 0.8888 | 0.9245 |
117
- | 1.0456 | 10.74 | 2800 | 0.4992 | 0.8733 | 0.8985 | 0.8733 | 0.8713 | 0.9105 |
118
- | 1.0456 | 10.93 | 2850 | 0.4706 | 0.8868 | 0.9034 | 0.8868 | 0.8843 | 0.9208 |
119
- | 1.007 | 11.12 | 2900 | 0.4948 | 0.8625 | 0.8804 | 0.8625 | 0.8546 | 0.9038 |
120
- | 1.007 | 11.31 | 2950 | 0.5275 | 0.8706 | 0.8890 | 0.8706 | 0.8654 | 0.9094 |
121
- | 1.007 | 11.51 | 3000 | 0.4775 | 0.8706 | 0.8900 | 0.8706 | 0.8637 | 0.9094 |
122
- | 1.007 | 11.7 | 3050 | 0.5229 | 0.8598 | 0.8866 | 0.8598 | 0.8548 | 0.9011 |
123
- | 1.007 | 11.89 | 3100 | 0.4870 | 0.8814 | 0.9075 | 0.8814 | 0.8787 | 0.9170 |
124
- | 0.9465 | 12.08 | 3150 | 0.4628 | 0.8733 | 0.8955 | 0.8733 | 0.8718 | 0.9113 |
125
- | 0.9465 | 12.27 | 3200 | 0.4396 | 0.8895 | 0.9075 | 0.8895 | 0.8868 | 0.9226 |
126
- | 0.9465 | 12.46 | 3250 | 0.5112 | 0.8733 | 0.8942 | 0.8733 | 0.8694 | 0.9113 |
127
- | 0.9465 | 12.66 | 3300 | 0.3970 | 0.8949 | 0.9101 | 0.8949 | 0.8919 | 0.9264 |
128
- | 0.9465 | 12.85 | 3350 | 0.4164 | 0.8922 | 0.9076 | 0.8922 | 0.8896 | 0.9245 |
129
- | 0.9125 | 13.04 | 3400 | 0.4644 | 0.8760 | 0.8987 | 0.8760 | 0.8749 | 0.9132 |
130
- | 0.9125 | 13.23 | 3450 | 0.4351 | 0.8895 | 0.9109 | 0.8895 | 0.8862 | 0.9226 |
131
- | 0.9125 | 13.42 | 3500 | 0.4282 | 0.9003 | 0.9171 | 0.9003 | 0.8979 | 0.9302 |
132
- | 0.9125 | 13.61 | 3550 | 0.4369 | 0.8814 | 0.9012 | 0.8814 | 0.8773 | 0.9170 |
133
- | 0.9125 | 13.81 | 3600 | 0.4421 | 0.8841 | 0.9054 | 0.8841 | 0.8817 | 0.9189 |
134
- | 0.9125 | 14.0 | 3650 | 0.4481 | 0.8787 | 0.8962 | 0.8787 | 0.8704 | 0.9162 |
135
- | 0.8821 | 14.19 | 3700 | 0.4565 | 0.8652 | 0.8899 | 0.8652 | 0.8617 | 0.9049 |
136
- | 0.8821 | 14.38 | 3750 | 0.4575 | 0.8760 | 0.8905 | 0.8760 | 0.8725 | 0.9132 |
137
- | 0.8821 | 14.57 | 3800 | 0.4521 | 0.8814 | 0.8991 | 0.8814 | 0.8786 | 0.9170 |
138
- | 0.8821 | 14.77 | 3850 | 0.5047 | 0.8652 | 0.8907 | 0.8652 | 0.8616 | 0.9057 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
139
 
140
 
141
  ### Framework versions
 
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:
23
+ - Loss: 0.5511
24
+ - Accuracy: 0.8720
25
+ - Precision: 0.8843
26
+ - Recall: 0.8720
27
+ - F1: 0.8715
28
+ - Binary: 0.9111
29
 
30
  ## Model description
31
 
 
59
 
60
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
61
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
62
+ | No log | 0.22 | 50 | 4.3908 | 0.0364 | 0.0036 | 0.0364 | 0.0055 | 0.2323 |
63
+ | No log | 0.43 | 100 | 3.7968 | 0.0418 | 0.0037 | 0.0418 | 0.0062 | 0.3105 |
64
+ | No log | 0.65 | 150 | 3.4223 | 0.0499 | 0.0034 | 0.0499 | 0.0062 | 0.3297 |
65
+ | No log | 0.86 | 200 | 3.2649 | 0.0985 | 0.0263 | 0.0985 | 0.0345 | 0.3641 |
66
+ | 3.8781 | 1.08 | 250 | 3.1085 | 0.1323 | 0.0462 | 0.1323 | 0.0530 | 0.3862 |
67
+ | 3.8781 | 1.29 | 300 | 3.0185 | 0.1228 | 0.0541 | 0.1228 | 0.0555 | 0.3795 |
68
+ | 3.8781 | 1.51 | 350 | 2.8481 | 0.1660 | 0.1305 | 0.1660 | 0.1055 | 0.4117 |
69
+ | 3.8781 | 1.72 | 400 | 2.6281 | 0.2254 | 0.1349 | 0.2254 | 0.1446 | 0.4561 |
70
+ | 3.8781 | 1.94 | 450 | 2.4175 | 0.2605 | 0.1934 | 0.2605 | 0.1835 | 0.4804 |
71
+ | 3.0628 | 2.16 | 500 | 2.2785 | 0.2915 | 0.1990 | 0.2915 | 0.1961 | 0.5027 |
72
+ | 3.0628 | 2.37 | 550 | 2.0868 | 0.3738 | 0.3460 | 0.3738 | 0.3058 | 0.5586 |
73
+ | 3.0628 | 2.59 | 600 | 1.9295 | 0.4305 | 0.3815 | 0.4305 | 0.3534 | 0.6031 |
74
+ | 3.0628 | 2.8 | 650 | 1.7881 | 0.4872 | 0.4541 | 0.4872 | 0.4192 | 0.6405 |
75
+ | 2.478 | 3.02 | 700 | 1.6403 | 0.5304 | 0.4907 | 0.5304 | 0.4651 | 0.6715 |
76
+ | 2.478 | 3.23 | 750 | 1.5843 | 0.5735 | 0.5809 | 0.5735 | 0.5262 | 0.7026 |
77
+ | 2.478 | 3.45 | 800 | 1.4142 | 0.5789 | 0.5783 | 0.5789 | 0.5349 | 0.7040 |
78
+ | 2.478 | 3.66 | 850 | 1.2960 | 0.6289 | 0.6269 | 0.6289 | 0.5828 | 0.7399 |
79
+ | 2.478 | 3.88 | 900 | 1.2609 | 0.6680 | 0.6867 | 0.6680 | 0.6347 | 0.7686 |
80
+ | 2.0301 | 4.09 | 950 | 1.1055 | 0.6991 | 0.7211 | 0.6991 | 0.6727 | 0.7906 |
81
+ | 2.0301 | 4.31 | 1000 | 1.1323 | 0.7018 | 0.7338 | 0.7018 | 0.6907 | 0.7918 |
82
+ | 2.0301 | 4.53 | 1050 | 0.9934 | 0.7314 | 0.7459 | 0.7314 | 0.7152 | 0.8112 |
83
+ | 2.0301 | 4.74 | 1100 | 0.9489 | 0.7395 | 0.7599 | 0.7395 | 0.7213 | 0.8174 |
84
+ | 2.0301 | 4.96 | 1150 | 0.9370 | 0.7355 | 0.7633 | 0.7355 | 0.7260 | 0.8151 |
85
+ | 1.7314 | 5.17 | 1200 | 0.8762 | 0.7679 | 0.7919 | 0.7679 | 0.7548 | 0.8359 |
86
+ | 1.7314 | 5.39 | 1250 | 0.7944 | 0.7868 | 0.8047 | 0.7868 | 0.7783 | 0.8520 |
87
+ | 1.7314 | 5.6 | 1300 | 0.7927 | 0.7881 | 0.8091 | 0.7881 | 0.7804 | 0.8518 |
88
+ | 1.7314 | 5.82 | 1350 | 0.7881 | 0.7895 | 0.8087 | 0.7895 | 0.7830 | 0.8533 |
89
+ | 1.5038 | 6.03 | 1400 | 0.7181 | 0.8057 | 0.8260 | 0.8057 | 0.8012 | 0.8661 |
90
+ | 1.5038 | 6.25 | 1450 | 0.7339 | 0.8084 | 0.8205 | 0.8084 | 0.8006 | 0.8665 |
91
+ | 1.5038 | 6.47 | 1500 | 0.6845 | 0.8192 | 0.8371 | 0.8192 | 0.8160 | 0.8737 |
92
+ | 1.5038 | 6.68 | 1550 | 0.6878 | 0.8246 | 0.8463 | 0.8246 | 0.8239 | 0.8776 |
93
+ | 1.5038 | 6.9 | 1600 | 0.6396 | 0.8300 | 0.8439 | 0.8300 | 0.8277 | 0.8803 |
94
+ | 1.3718 | 7.11 | 1650 | 0.6743 | 0.8232 | 0.8458 | 0.8232 | 0.8225 | 0.8772 |
95
+ | 1.3718 | 7.33 | 1700 | 0.6018 | 0.8435 | 0.8644 | 0.8435 | 0.8419 | 0.8912 |
96
+ | 1.3718 | 7.54 | 1750 | 0.6461 | 0.8300 | 0.8494 | 0.8300 | 0.8293 | 0.8811 |
97
+ | 1.3718 | 7.76 | 1800 | 0.6181 | 0.8178 | 0.8385 | 0.8178 | 0.8157 | 0.8731 |
98
+ | 1.3718 | 7.97 | 1850 | 0.5696 | 0.8421 | 0.8608 | 0.8421 | 0.8425 | 0.8904 |
99
+ | 1.2723 | 8.19 | 1900 | 0.5924 | 0.8354 | 0.8550 | 0.8354 | 0.8335 | 0.8858 |
100
+ | 1.2723 | 8.41 | 1950 | 0.6176 | 0.8313 | 0.8464 | 0.8313 | 0.8281 | 0.8821 |
101
+ | 1.2723 | 8.62 | 2000 | 0.6529 | 0.8246 | 0.8496 | 0.8246 | 0.8239 | 0.8773 |
102
+ | 1.2723 | 8.84 | 2050 | 0.5532 | 0.8489 | 0.8680 | 0.8489 | 0.8485 | 0.8953 |
103
+ | 1.1706 | 9.05 | 2100 | 0.5791 | 0.8502 | 0.8673 | 0.8502 | 0.8502 | 0.8957 |
104
+ | 1.1706 | 9.27 | 2150 | 0.5871 | 0.8448 | 0.8624 | 0.8448 | 0.8434 | 0.8916 |
105
+ | 1.1706 | 9.48 | 2200 | 0.5853 | 0.8394 | 0.8605 | 0.8394 | 0.8394 | 0.8887 |
106
+ | 1.1706 | 9.7 | 2250 | 0.5420 | 0.8502 | 0.8675 | 0.8502 | 0.8494 | 0.8962 |
107
+ | 1.1706 | 9.91 | 2300 | 0.5913 | 0.8286 | 0.8515 | 0.8286 | 0.8275 | 0.8822 |
108
+ | 1.102 | 10.13 | 2350 | 0.5674 | 0.8448 | 0.8614 | 0.8448 | 0.8426 | 0.8927 |
109
+ | 1.102 | 10.34 | 2400 | 0.5948 | 0.8502 | 0.8675 | 0.8502 | 0.8491 | 0.8966 |
110
+ | 1.102 | 10.56 | 2450 | 0.5462 | 0.8543 | 0.8725 | 0.8543 | 0.8544 | 0.8969 |
111
+ | 1.102 | 10.78 | 2500 | 0.5774 | 0.8462 | 0.8662 | 0.8462 | 0.8469 | 0.8928 |
112
+ | 1.102 | 10.99 | 2550 | 0.5601 | 0.8448 | 0.8671 | 0.8448 | 0.8460 | 0.8938 |
113
+ | 1.0635 | 11.21 | 2600 | 0.5655 | 0.8570 | 0.8744 | 0.8570 | 0.8572 | 0.8988 |
114
+ | 1.0635 | 11.42 | 2650 | 0.5635 | 0.8516 | 0.8688 | 0.8516 | 0.8506 | 0.8950 |
115
+ | 1.0635 | 11.64 | 2700 | 0.6053 | 0.8327 | 0.8547 | 0.8327 | 0.8325 | 0.8822 |
116
+ | 1.0635 | 11.85 | 2750 | 0.6079 | 0.8408 | 0.8621 | 0.8408 | 0.8390 | 0.8896 |
117
+ | 1.0079 | 12.07 | 2800 | 0.5631 | 0.8489 | 0.8657 | 0.8489 | 0.8489 | 0.8962 |
118
+ | 1.0079 | 12.28 | 2850 | 0.5383 | 0.8691 | 0.8828 | 0.8691 | 0.8685 | 0.9093 |
119
+ | 1.0079 | 12.5 | 2900 | 0.5289 | 0.8623 | 0.8760 | 0.8623 | 0.8620 | 0.9040 |
120
+ | 1.0079 | 12.72 | 2950 | 0.5366 | 0.8650 | 0.8841 | 0.8650 | 0.8661 | 0.9065 |
121
+ | 1.0079 | 12.93 | 3000 | 0.5507 | 0.8516 | 0.8715 | 0.8516 | 0.8507 | 0.8965 |
122
+ | 0.9768 | 13.15 | 3050 | 0.5615 | 0.8596 | 0.8734 | 0.8596 | 0.8586 | 0.9036 |
123
+ | 0.9768 | 13.36 | 3100 | 0.5332 | 0.8583 | 0.8769 | 0.8583 | 0.8591 | 0.9016 |
124
+ | 0.9768 | 13.58 | 3150 | 0.5017 | 0.8691 | 0.8825 | 0.8691 | 0.8687 | 0.9086 |
125
+ | 0.9768 | 13.79 | 3200 | 0.5430 | 0.8623 | 0.8753 | 0.8623 | 0.8619 | 0.9034 |
126
+ | 0.9196 | 14.01 | 3250 | 0.5612 | 0.8556 | 0.8706 | 0.8556 | 0.8536 | 0.8992 |
127
+ | 0.9196 | 14.22 | 3300 | 0.5152 | 0.8664 | 0.8817 | 0.8664 | 0.8652 | 0.9062 |
128
+ | 0.9196 | 14.44 | 3350 | 0.5082 | 0.8745 | 0.8852 | 0.8745 | 0.8744 | 0.9126 |
129
+ | 0.9196 | 14.66 | 3400 | 0.5131 | 0.8745 | 0.8824 | 0.8745 | 0.8744 | 0.9128 |
130
+ | 0.9196 | 14.87 | 3450 | 0.5532 | 0.8543 | 0.8691 | 0.8543 | 0.8529 | 0.8992 |
131
+ | 0.8897 | 15.09 | 3500 | 0.5611 | 0.8556 | 0.8682 | 0.8556 | 0.8544 | 0.8987 |
132
+ | 0.8897 | 15.3 | 3550 | 0.5185 | 0.8664 | 0.8742 | 0.8664 | 0.8646 | 0.9062 |
133
+ | 0.8897 | 15.52 | 3600 | 0.5576 | 0.8556 | 0.8713 | 0.8556 | 0.8547 | 0.8988 |
134
+ | 0.8897 | 15.73 | 3650 | 0.5190 | 0.8650 | 0.8758 | 0.8650 | 0.8631 | 0.9053 |
135
+ | 0.8897 | 15.95 | 3700 | 0.5215 | 0.8623 | 0.8753 | 0.8623 | 0.8613 | 0.9031 |
136
+ | 0.8483 | 16.16 | 3750 | 0.5424 | 0.8543 | 0.8708 | 0.8543 | 0.8524 | 0.8978 |
137
+ | 0.8483 | 16.38 | 3800 | 0.5499 | 0.8691 | 0.8814 | 0.8691 | 0.8698 | 0.9090 |
138
+ | 0.8483 | 16.59 | 3850 | 0.5676 | 0.8556 | 0.8711 | 0.8556 | 0.8530 | 0.8997 |
139
+ | 0.8483 | 16.81 | 3900 | 0.5817 | 0.8570 | 0.8750 | 0.8570 | 0.8565 | 0.8996 |
140
+ | 0.8359 | 17.03 | 3950 | 0.5602 | 0.8583 | 0.8759 | 0.8583 | 0.8576 | 0.9005 |
141
+ | 0.8359 | 17.24 | 4000 | 0.5563 | 0.8596 | 0.8760 | 0.8596 | 0.8589 | 0.9016 |
142
+ | 0.8359 | 17.46 | 4050 | 0.5181 | 0.8637 | 0.8754 | 0.8637 | 0.8620 | 0.9039 |
143
+ | 0.8359 | 17.67 | 4100 | 0.5184 | 0.8731 | 0.8886 | 0.8731 | 0.8710 | 0.9119 |
144
+ | 0.8359 | 17.89 | 4150 | 0.5222 | 0.8664 | 0.8755 | 0.8664 | 0.8636 | 0.9076 |
145
+ | 0.7893 | 18.1 | 4200 | 0.5228 | 0.8691 | 0.8800 | 0.8691 | 0.8683 | 0.9086 |
146
+ | 0.7893 | 18.32 | 4250 | 0.5159 | 0.8745 | 0.8879 | 0.8745 | 0.8743 | 0.9128 |
147
+ | 0.7893 | 18.53 | 4300 | 0.5181 | 0.8745 | 0.8843 | 0.8745 | 0.8724 | 0.9117 |
148
+ | 0.7893 | 18.75 | 4350 | 0.5315 | 0.8772 | 0.8884 | 0.8772 | 0.8759 | 0.9134 |
149
+ | 0.7893 | 18.97 | 4400 | 0.5018 | 0.8772 | 0.8880 | 0.8772 | 0.8756 | 0.9132 |
150
+ | 0.768 | 19.18 | 4450 | 0.5367 | 0.8677 | 0.8787 | 0.8677 | 0.8682 | 0.9067 |
151
+ | 0.768 | 19.4 | 4500 | 0.5166 | 0.8745 | 0.8855 | 0.8745 | 0.8733 | 0.9120 |
152
+ | 0.768 | 19.61 | 4550 | 0.5010 | 0.8758 | 0.8850 | 0.8758 | 0.8748 | 0.9128 |
153
+ | 0.768 | 19.83 | 4600 | 0.5553 | 0.8704 | 0.8813 | 0.8704 | 0.8694 | 0.9096 |
154
+ | 0.7463 | 20.04 | 4650 | 0.5474 | 0.8637 | 0.8755 | 0.8637 | 0.8625 | 0.9053 |
155
+ | 0.7463 | 20.26 | 4700 | 0.5482 | 0.8664 | 0.8795 | 0.8664 | 0.8661 | 0.9062 |
156
+ | 0.7463 | 20.47 | 4750 | 0.5528 | 0.8650 | 0.8782 | 0.8650 | 0.8649 | 0.9058 |
157
+ | 0.7463 | 20.69 | 4800 | 0.5490 | 0.8650 | 0.8782 | 0.8650 | 0.8640 | 0.9053 |
158
+ | 0.7463 | 20.91 | 4850 | 0.5362 | 0.8637 | 0.8746 | 0.8637 | 0.8620 | 0.9043 |
159
+ | 0.7261 | 21.12 | 4900 | 0.5296 | 0.8691 | 0.8830 | 0.8691 | 0.8681 | 0.9077 |
160
 
161
 
162
  ### Framework versions
runs/Jun28_12-52-19_LAPTOP-1GID9RGH/events.out.tfevents.1719553941.LAPTOP-1GID9RGH.1516.4 CHANGED
@@ -1,3 +1,3 @@
1
  version https://git-lfs.github.com/spec/v1
2
- oid sha256:339f237ec76ebd030d167e8112efa29a4b09f2ed30315b9d53e7621f02ee9273
3
- size 60048
 
1
  version https://git-lfs.github.com/spec/v1
2
+ oid sha256:80df97bba97f2614467241166eff98c1bda7867f6fb51e63d6c3adccd6649376
3
+ size 65211
runs/Jun28_12-52-19_LAPTOP-1GID9RGH/events.out.tfevents.1719556263.LAPTOP-1GID9RGH.1516.5 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2027e23c544ec34d8ad4f10af696afc6d5ee4b557b2e45c681e852b637287300
3
+ size 610