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End of training

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README.md CHANGED
@@ -4,10 +4,10 @@ base_model: klue/roberta-small
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  tags:
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  - generated_from_trainer
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  metrics:
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- - accuracy
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- - f1
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  - precision
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  - recall
 
 
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  model-index:
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  - name: roberta-small-hangul-2-hanja
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  results: []
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on the None dataset.
22
  It achieves the following results on the evaluation set:
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- - Accuracy: 0.9918
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- - F1: 0.9824
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- - Loss: 0.0868
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- - Precision: 0.9814
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- - Recall: 0.9835
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29
  ## Model description
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@@ -49,32 +49,162 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
  - lr_scheduler_type: linear
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- - num_epochs: 20
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54
  ### Training results
55
 
56
- | Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
57
- |:-------------:|:-----:|:----:|:--------:|:------:|:---------------:|:---------:|:------:|
58
- | No log | 1.0 | 482 | 0.8993 | 0.4607 | 0.8157 | 0.6392 | 0.3602 |
59
- | 1.7229 | 2.0 | 964 | 0.9606 | 0.8537 | 0.4586 | 0.8728 | 0.8354 |
60
- | 0.6279 | 3.0 | 1446 | 0.9701 | 0.9159 | 0.3291 | 0.9244 | 0.9075 |
61
- | 0.418 | 4.0 | 1928 | 0.9761 | 0.9426 | 0.2596 | 0.9439 | 0.9413 |
62
- | 0.316 | 5.0 | 2410 | 0.9797 | 0.9530 | 0.2159 | 0.9545 | 0.9515 |
63
- | 0.2553 | 6.0 | 2892 | 0.9821 | 0.9589 | 0.1848 | 0.9608 | 0.9571 |
64
- | 0.213 | 7.0 | 3374 | 0.9832 | 0.9613 | 0.1622 | 0.9616 | 0.9610 |
65
- | 0.1819 | 8.0 | 3856 | 0.9858 | 0.9707 | 0.1430 | 0.9691 | 0.9722 |
66
- | 0.16 | 9.0 | 4338 | 0.9871 | 0.9712 | 0.1307 | 0.9705 | 0.9719 |
67
- | 0.1409 | 10.0 | 4820 | 0.9885 | 0.9749 | 0.1197 | 0.9734 | 0.9764 |
68
- | 0.1295 | 11.0 | 5302 | 0.9893 | 0.9759 | 0.1120 | 0.9747 | 0.9771 |
69
- | 0.1174 | 12.0 | 5784 | 0.9893 | 0.9763 | 0.1065 | 0.9744 | 0.9782 |
70
- | 0.1085 | 13.0 | 6266 | 0.9896 | 0.9770 | 0.1005 | 0.9755 | 0.9785 |
71
- | 0.1011 | 14.0 | 6748 | 0.9905 | 0.9794 | 0.0968 | 0.9786 | 0.9803 |
72
- | 0.0954 | 15.0 | 7230 | 0.9910 | 0.9801 | 0.0941 | 0.9793 | 0.9810 |
73
- | 0.0899 | 16.0 | 7712 | 0.9912 | 0.9807 | 0.0916 | 0.9796 | 0.9817 |
74
- | 0.0866 | 17.0 | 8194 | 0.9917 | 0.9819 | 0.0893 | 0.9810 | 0.9828 |
75
- | 0.0847 | 18.0 | 8676 | 0.9918 | 0.9824 | 0.0880 | 0.9814 | 0.9835 |
76
- | 0.0814 | 19.0 | 9158 | 0.9918 | 0.9824 | 0.0870 | 0.9814 | 0.9835 |
77
- | 0.0822 | 20.0 | 9640 | 0.9918 | 0.9824 | 0.0868 | 0.9814 | 0.9835 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
4
  tags:
5
  - generated_from_trainer
6
  metrics:
 
 
7
  - precision
8
  - recall
9
+ - f1
10
+ - accuracy
11
  model-index:
12
  - name: roberta-small-hangul-2-hanja
13
  results: []
 
20
 
21
  This model is a fine-tuned version of [klue/roberta-small](https://huggingface.co/klue/roberta-small) on the None dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.0352
24
+ - Precision: 0.9894
25
+ - Recall: 0.9911
26
+ - F1: 0.9902
27
+ - Accuracy: 0.9956
28
 
29
  ## Model description
30
 
 
49
  - seed: 42
50
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
51
  - lr_scheduler_type: linear
52
+ - num_epochs: 150
53
 
54
  ### Training results
55
 
56
+ | Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall |
57
+ |:-------------:|:-----:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
58
+ | No log | 1.0 | 482 | 0.8993 | 0.4607 | 0.8157 | 0.6392 | 0.3602 |
59
+ | 1.7229 | 2.0 | 964 | 0.9606 | 0.8537 | 0.4586 | 0.8728 | 0.8354 |
60
+ | 0.6279 | 3.0 | 1446 | 0.9701 | 0.9159 | 0.3291 | 0.9244 | 0.9075 |
61
+ | 0.418 | 4.0 | 1928 | 0.9761 | 0.9426 | 0.2596 | 0.9439 | 0.9413 |
62
+ | 0.316 | 5.0 | 2410 | 0.9797 | 0.9530 | 0.2159 | 0.9545 | 0.9515 |
63
+ | 0.2553 | 6.0 | 2892 | 0.9821 | 0.9589 | 0.1848 | 0.9608 | 0.9571 |
64
+ | 0.213 | 7.0 | 3374 | 0.9832 | 0.9613 | 0.1622 | 0.9616 | 0.9610 |
65
+ | 0.1819 | 8.0 | 3856 | 0.9858 | 0.9707 | 0.1430 | 0.9691 | 0.9722 |
66
+ | 0.16 | 9.0 | 4338 | 0.9871 | 0.9712 | 0.1307 | 0.9705 | 0.9719 |
67
+ | 0.1409 | 10.0 | 4820 | 0.9885 | 0.9749 | 0.1197 | 0.9734 | 0.9764 |
68
+ | 0.1295 | 11.0 | 5302 | 0.9893 | 0.9759 | 0.1120 | 0.9747 | 0.9771 |
69
+ | 0.1174 | 12.0 | 5784 | 0.9893 | 0.9763 | 0.1065 | 0.9744 | 0.9782 |
70
+ | 0.1085 | 13.0 | 6266 | 0.9896 | 0.9770 | 0.1005 | 0.9755 | 0.9785 |
71
+ | 0.1011 | 14.0 | 6748 | 0.9905 | 0.9794 | 0.0968 | 0.9786 | 0.9803 |
72
+ | 0.0954 | 15.0 | 7230 | 0.9910 | 0.9801 | 0.0941 | 0.9793 | 0.9810 |
73
+ | 0.0899 | 16.0 | 7712 | 0.9912 | 0.9807 | 0.0916 | 0.9796 | 0.9817 |
74
+ | 0.0866 | 17.0 | 8194 | 0.9917 | 0.9819 | 0.0893 | 0.9810 | 0.9828 |
75
+ | 0.0847 | 18.0 | 8676 | 0.9918 | 0.9824 | 0.0880 | 0.9814 | 0.9835 |
76
+ | 0.0814 | 19.0 | 9158 | 0.9918 | 0.9824 | 0.0870 | 0.9814 | 0.9835 |
77
+ | 0.0822 | 20.0 | 9640 | 0.9918 | 0.9824 | 0.0868 | 0.9814 | 0.9835 |
78
+ | 0.0802 | 21.0 | 10122 | 0.9929 | 0.9922 | 0.0617 | 0.9924 | 0.9920 |
79
+ | 0.0734 | 22.0 | 10604 | 0.9932 | 0.9907 | 0.0562 | 0.9903 | 0.9911 |
80
+ | 0.0665 | 23.0 | 11086 | 0.9942 | 0.9933 | 0.0517 | 0.9937 | 0.9929 |
81
+ | 0.0582 | 24.0 | 11568 | 0.9939 | 0.9924 | 0.0485 | 0.9922 | 0.9926 |
82
+ | 0.0526 | 25.0 | 12050 | 0.9944 | 0.9926 | 0.0452 | 0.9926 | 0.9926 |
83
+ | 0.0468 | 26.0 | 12532 | 0.9947 | 0.9933 | 0.0423 | 0.9926 | 0.9940 |
84
+ | 0.0419 | 27.0 | 13014 | 0.9960 | 0.9945 | 0.0299 | 0.9943 | 0.9947 |
85
+ | 0.0419 | 28.0 | 13496 | 0.0352 | 0.9929 | 0.9940 | 0.9934 | 0.9959 |
86
+ | 0.0382 | 29.0 | 13978 | 0.0342 | 0.9954 | 0.9968 | 0.9961 | 0.9964 |
87
+ | 0.0346 | 30.0 | 14460 | 0.0335 | 0.9933 | 0.9947 | 0.9940 | 0.9955 |
88
+ | 0.0313 | 31.0 | 14942 | 0.0318 | 0.9929 | 0.9947 | 0.9938 | 0.9957 |
89
+ | 0.0286 | 32.0 | 15424 | 0.0310 | 0.9936 | 0.9950 | 0.9943 | 0.9958 |
90
+ | 0.026 | 33.0 | 15906 | 0.0304 | 0.9943 | 0.9957 | 0.9950 | 0.9961 |
91
+ | 0.0234 | 34.0 | 16388 | 0.0292 | 0.9933 | 0.9947 | 0.9940 | 0.9960 |
92
+ | 0.0217 | 35.0 | 16870 | 0.0279 | 0.9933 | 0.9950 | 0.9941 | 0.9957 |
93
+ | 0.0198 | 36.0 | 17352 | 0.0272 | 0.9922 | 0.9933 | 0.9927 | 0.9958 |
94
+ | 0.0178 | 37.0 | 17834 | 0.0264 | 0.9926 | 0.9940 | 0.9933 | 0.9959 |
95
+ | 0.0167 | 38.0 | 18316 | 0.0266 | 0.9922 | 0.9940 | 0.9931 | 0.9958 |
96
+ | 0.0149 | 39.0 | 18798 | 0.0262 | 0.9922 | 0.9940 | 0.9931 | 0.9960 |
97
+ | 0.0137 | 40.0 | 19280 | 0.0255 | 0.9918 | 0.9933 | 0.9925 | 0.9956 |
98
+ | 0.013 | 41.0 | 19762 | 0.0253 | 0.9918 | 0.9936 | 0.9927 | 0.9958 |
99
+ | 0.0115 | 42.0 | 20244 | 0.0250 | 0.9915 | 0.9922 | 0.9918 | 0.9959 |
100
+ | 0.0107 | 43.0 | 20726 | 0.0258 | 0.9911 | 0.9929 | 0.9920 | 0.9957 |
101
+ | 0.0098 | 44.0 | 21208 | 0.0248 | 0.9922 | 0.9940 | 0.9931 | 0.9959 |
102
+ | 0.009 | 45.0 | 21690 | 0.0254 | 0.9911 | 0.9929 | 0.9920 | 0.9960 |
103
+ | 0.0081 | 46.0 | 22172 | 0.0258 | 0.9918 | 0.9933 | 0.9925 | 0.9959 |
104
+ | 0.0075 | 47.0 | 22654 | 0.0251 | 0.9904 | 0.9925 | 0.9915 | 0.9956 |
105
+ | 0.0069 | 48.0 | 23136 | 0.0256 | 0.9904 | 0.9922 | 0.9913 | 0.9956 |
106
+ | 0.0063 | 49.0 | 23618 | 0.0262 | 0.9894 | 0.9918 | 0.9906 | 0.9955 |
107
+ | 0.0055 | 50.0 | 24100 | 0.0254 | 0.9904 | 0.9922 | 0.9913 | 0.9959 |
108
+ | 0.0052 | 51.0 | 24582 | 0.0255 | 0.9901 | 0.9922 | 0.9911 | 0.9956 |
109
+ | 0.0048 | 52.0 | 25064 | 0.0256 | 0.9901 | 0.9918 | 0.9910 | 0.9958 |
110
+ | 0.0044 | 53.0 | 25546 | 0.0276 | 0.9883 | 0.9901 | 0.9892 | 0.9954 |
111
+ | 0.0039 | 54.0 | 26028 | 0.0271 | 0.9890 | 0.9904 | 0.9897 | 0.9955 |
112
+ | 0.0037 | 55.0 | 26510 | 0.0275 | 0.9887 | 0.9908 | 0.9897 | 0.9957 |
113
+ | 0.0037 | 56.0 | 26992 | 0.0273 | 0.9901 | 0.9918 | 0.9910 | 0.9957 |
114
+ | 0.0032 | 57.0 | 27474 | 0.0270 | 0.9901 | 0.9918 | 0.9910 | 0.9960 |
115
+ | 0.003 | 58.0 | 27956 | 0.0284 | 0.9890 | 0.9918 | 0.9904 | 0.9955 |
116
+ | 0.0027 | 59.0 | 28438 | 0.0287 | 0.9890 | 0.9918 | 0.9904 | 0.9956 |
117
+ | 0.0024 | 60.0 | 28920 | 0.0290 | 0.9876 | 0.9908 | 0.9892 | 0.9955 |
118
+ | 0.0022 | 61.0 | 29402 | 0.0286 | 0.9901 | 0.9918 | 0.9910 | 0.9958 |
119
+ | 0.0021 | 62.0 | 29884 | 0.0286 | 0.9904 | 0.9922 | 0.9913 | 0.9959 |
120
+ | 0.0019 | 63.0 | 30366 | 0.0325 | 0.9883 | 0.9911 | 0.9897 | 0.9954 |
121
+ | 0.0017 | 64.0 | 30848 | 0.0295 | 0.9897 | 0.9915 | 0.9906 | 0.9957 |
122
+ | 0.0015 | 65.0 | 31330 | 0.0289 | 0.9908 | 0.9922 | 0.9915 | 0.9958 |
123
+ | 0.0014 | 66.0 | 31812 | 0.0303 | 0.9901 | 0.9922 | 0.9911 | 0.9957 |
124
+ | 0.0012 | 67.0 | 32294 | 0.0306 | 0.9890 | 0.9918 | 0.9904 | 0.9956 |
125
+ | 0.0012 | 68.0 | 32776 | 0.0312 | 0.9887 | 0.9911 | 0.9899 | 0.9955 |
126
+ | 0.0011 | 69.0 | 33258 | 0.0310 | 0.9883 | 0.9911 | 0.9897 | 0.9956 |
127
+ | 0.001 | 70.0 | 33740 | 0.0309 | 0.9883 | 0.9908 | 0.9895 | 0.9957 |
128
+ | 0.001 | 71.0 | 34222 | 0.0322 | 0.9897 | 0.9904 | 0.9901 | 0.9957 |
129
+ | 0.0008 | 72.0 | 34704 | 0.0323 | 0.9894 | 0.9908 | 0.9901 | 0.9958 |
130
+ | 0.0008 | 73.0 | 35186 | 0.0312 | 0.9890 | 0.9904 | 0.9897 | 0.9956 |
131
+ | 0.0007 | 74.0 | 35668 | 0.0327 | 0.9894 | 0.9908 | 0.9901 | 0.9957 |
132
+ | 0.0007 | 75.0 | 36150 | 0.0315 | 0.9904 | 0.9918 | 0.9911 | 0.9958 |
133
+ | 0.0007 | 76.0 | 36632 | 0.0318 | 0.9901 | 0.9922 | 0.9911 | 0.9957 |
134
+ | 0.0006 | 77.0 | 37114 | 0.0314 | 0.9901 | 0.9922 | 0.9911 | 0.9958 |
135
+ | 0.0006 | 78.0 | 37596 | 0.0325 | 0.9890 | 0.9918 | 0.9904 | 0.9956 |
136
+ | 0.0005 | 79.0 | 38078 | 0.0318 | 0.9880 | 0.9908 | 0.9894 | 0.9955 |
137
+ | 0.0005 | 80.0 | 38560 | 0.0315 | 0.9901 | 0.9908 | 0.9904 | 0.9957 |
138
+ | 0.0004 | 81.0 | 39042 | 0.0321 | 0.9897 | 0.9897 | 0.9897 | 0.9958 |
139
+ | 0.0004 | 82.0 | 39524 | 0.0340 | 0.9883 | 0.9908 | 0.9895 | 0.9952 |
140
+ | 0.0005 | 83.0 | 40006 | 0.0317 | 0.9908 | 0.9922 | 0.9915 | 0.9956 |
141
+ | 0.0005 | 84.0 | 40488 | 0.0324 | 0.9887 | 0.9915 | 0.9901 | 0.9955 |
142
+ | 0.0004 | 85.0 | 40970 | 0.0320 | 0.9901 | 0.9918 | 0.9910 | 0.9956 |
143
+ | 0.0003 | 86.0 | 41452 | 0.0324 | 0.9911 | 0.9925 | 0.9918 | 0.9957 |
144
+ | 0.0003 | 87.0 | 41934 | 0.0308 | 0.9908 | 0.9922 | 0.9915 | 0.9959 |
145
+ | 0.0003 | 88.0 | 42416 | 0.0337 | 0.9904 | 0.9922 | 0.9913 | 0.9956 |
146
+ | 0.0003 | 89.0 | 42898 | 0.0330 | 0.9904 | 0.9922 | 0.9913 | 0.9956 |
147
+ | 0.0003 | 90.0 | 43380 | 0.0330 | 0.9876 | 0.9904 | 0.9890 | 0.9956 |
148
+ | 0.0003 | 91.0 | 43862 | 0.0341 | 0.9901 | 0.9922 | 0.9911 | 0.9957 |
149
+ | 0.0003 | 92.0 | 44344 | 0.0337 | 0.9894 | 0.9918 | 0.9906 | 0.9956 |
150
+ | 0.0002 | 93.0 | 44826 | 0.0343 | 0.9897 | 0.9915 | 0.9906 | 0.9956 |
151
+ | 0.0003 | 94.0 | 45308 | 0.0336 | 0.9901 | 0.9918 | 0.9910 | 0.9957 |
152
+ | 0.0002 | 95.0 | 45790 | 0.0355 | 0.9887 | 0.9915 | 0.9901 | 0.9954 |
153
+ | 0.0002 | 96.0 | 46272 | 0.0326 | 0.9894 | 0.9915 | 0.9904 | 0.9958 |
154
+ | 0.0002 | 97.0 | 46754 | 0.0334 | 0.9894 | 0.9908 | 0.9901 | 0.9959 |
155
+ | 0.0002 | 98.0 | 47236 | 0.0337 | 0.9897 | 0.9918 | 0.9908 | 0.9959 |
156
+ | 0.0002 | 99.0 | 47718 | 0.0334 | 0.9897 | 0.9918 | 0.9908 | 0.9958 |
157
+ | 0.0002 | 100.0 | 48200 | 0.0347 | 0.9890 | 0.9915 | 0.9902 | 0.9957 |
158
+ | 0.0002 | 101.0 | 48682 | 0.0379 | 0.9873 | 0.9915 | 0.9894 | 0.9953 |
159
+ | 0.0002 | 102.0 | 49164 | 0.0340 | 0.9890 | 0.9915 | 0.9902 | 0.9957 |
160
+ | 0.0002 | 103.0 | 49646 | 0.0336 | 0.9890 | 0.9897 | 0.9894 | 0.9956 |
161
+ | 0.0001 | 104.0 | 50128 | 0.0362 | 0.9901 | 0.9922 | 0.9911 | 0.9954 |
162
+ | 0.0002 | 105.0 | 50610 | 0.0339 | 0.9890 | 0.9915 | 0.9902 | 0.9956 |
163
+ | 0.0002 | 106.0 | 51092 | 0.0339 | 0.9901 | 0.9908 | 0.9904 | 0.9957 |
164
+ | 0.0002 | 107.0 | 51574 | 0.0341 | 0.9900 | 0.9893 | 0.9897 | 0.9957 |
165
+ | 0.0002 | 108.0 | 52056 | 0.0350 | 0.9883 | 0.9911 | 0.9897 | 0.9955 |
166
+ | 0.0001 | 109.0 | 52538 | 0.0334 | 0.9901 | 0.9918 | 0.9910 | 0.9956 |
167
+ | 0.0001 | 110.0 | 53020 | 0.0364 | 0.9887 | 0.9908 | 0.9897 | 0.9954 |
168
+ | 0.0001 | 111.0 | 53502 | 0.0340 | 0.9879 | 0.9893 | 0.9886 | 0.9956 |
169
+ | 0.0001 | 112.0 | 53984 | 0.0346 | 0.9880 | 0.9911 | 0.9895 | 0.9955 |
170
+ | 0.0001 | 113.0 | 54466 | 0.0348 | 0.9887 | 0.9908 | 0.9897 | 0.9954 |
171
+ | 0.0001 | 114.0 | 54948 | 0.0347 | 0.9894 | 0.9918 | 0.9906 | 0.9956 |
172
+ | 0.0001 | 115.0 | 55430 | 0.0342 | 0.9890 | 0.9908 | 0.9899 | 0.9956 |
173
+ | 0.0001 | 116.0 | 55912 | 0.0344 | 0.9901 | 0.9915 | 0.9908 | 0.9957 |
174
+ | 0.0001 | 117.0 | 56394 | 0.0340 | 0.9887 | 0.9904 | 0.9895 | 0.9956 |
175
+ | 0.0001 | 118.0 | 56876 | 0.0347 | 0.9880 | 0.9911 | 0.9895 | 0.9955 |
176
+ | 0.0001 | 119.0 | 57358 | 0.0349 | 0.9887 | 0.9915 | 0.9901 | 0.9955 |
177
+ | 0.0001 | 120.0 | 57840 | 0.0351 | 0.9880 | 0.9904 | 0.9892 | 0.9955 |
178
+ | 0.0001 | 121.0 | 58322 | 0.0359 | 0.9883 | 0.9915 | 0.9899 | 0.9955 |
179
+ | 0.0001 | 122.0 | 58804 | 0.0365 | 0.9873 | 0.9908 | 0.9890 | 0.9955 |
180
+ | 0.0001 | 123.0 | 59286 | 0.0350 | 0.9869 | 0.9897 | 0.9883 | 0.9955 |
181
+ | 0.0001 | 124.0 | 59768 | 0.0344 | 0.9880 | 0.9901 | 0.9890 | 0.9955 |
182
+ | 0.0001 | 125.0 | 60250 | 0.0352 | 0.9897 | 0.9883 | 0.9890 | 0.9956 |
183
+ | 0.0001 | 126.0 | 60732 | 0.0359 | 0.9869 | 0.9904 | 0.9887 | 0.9953 |
184
+ | 0.0001 | 127.0 | 61214 | 0.0360 | 0.9862 | 0.9890 | 0.9876 | 0.9952 |
185
+ | 0.0001 | 128.0 | 61696 | 0.0357 | 0.9869 | 0.9908 | 0.9888 | 0.9953 |
186
+ | 0.0001 | 129.0 | 62178 | 0.0363 | 0.9869 | 0.9908 | 0.9888 | 0.9954 |
187
+ | 0.0001 | 130.0 | 62660 | 0.0360 | 0.9880 | 0.9908 | 0.9894 | 0.9954 |
188
+ | 0.0001 | 131.0 | 63142 | 0.0360 | 0.9883 | 0.9897 | 0.9890 | 0.9956 |
189
+ | 0.0001 | 132.0 | 63624 | 0.0362 | 0.9869 | 0.9901 | 0.9885 | 0.9954 |
190
+ | 0.0001 | 133.0 | 64106 | 0.0356 | 0.9876 | 0.9901 | 0.9888 | 0.9955 |
191
+ | 0.0001 | 134.0 | 64588 | 0.0367 | 0.9876 | 0.9911 | 0.9894 | 0.9954 |
192
+ | 0.0001 | 135.0 | 65070 | 0.0364 | 0.9876 | 0.9911 | 0.9894 | 0.9954 |
193
+ | 0.0001 | 136.0 | 65552 | 0.0363 | 0.9876 | 0.9904 | 0.9890 | 0.9954 |
194
+ | 0.0 | 137.0 | 66034 | 0.0369 | 0.9876 | 0.9911 | 0.9894 | 0.9954 |
195
+ | 0.0001 | 138.0 | 66516 | 0.0369 | 0.9876 | 0.9911 | 0.9894 | 0.9954 |
196
+ | 0.0001 | 139.0 | 66998 | 0.0358 | 0.9894 | 0.9911 | 0.9902 | 0.9956 |
197
+ | 0.0001 | 140.0 | 67480 | 0.0360 | 0.9887 | 0.9915 | 0.9901 | 0.9956 |
198
+ | 0.0001 | 141.0 | 67962 | 0.0357 | 0.9894 | 0.9911 | 0.9902 | 0.9957 |
199
+ | 0.0001 | 142.0 | 68444 | 0.0355 | 0.9894 | 0.9911 | 0.9902 | 0.9957 |
200
+ | 0.0001 | 143.0 | 68926 | 0.0349 | 0.9883 | 0.9893 | 0.9888 | 0.9957 |
201
+ | 0.0 | 144.0 | 69408 | 0.0357 | 0.9883 | 0.9908 | 0.9895 | 0.9956 |
202
+ | 0.0 | 145.0 | 69890 | 0.0361 | 0.9883 | 0.9915 | 0.9899 | 0.9955 |
203
+ | 0.0001 | 146.0 | 70372 | 0.0350 | 0.9894 | 0.9911 | 0.9902 | 0.9956 |
204
+ | 0.0001 | 147.0 | 70854 | 0.0354 | 0.9894 | 0.9918 | 0.9906 | 0.9956 |
205
+ | 0.0 | 148.0 | 71336 | 0.0351 | 0.9894 | 0.9911 | 0.9902 | 0.9956 |
206
+ | 0.0 | 149.0 | 71818 | 0.0352 | 0.9894 | 0.9911 | 0.9902 | 0.9956 |
207
+ | 0.0 | 150.0 | 72300 | 0.0352 | 0.9894 | 0.9911 | 0.9902 | 0.9956 |
208
 
209
 
210
  ### Framework versions
model.safetensors CHANGED
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  size 285162796
 
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