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best_model-yelp_polarity-16-13

Text Classification
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PyTorch
albert
Generated from Trainer
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1
  ---
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- license: mit
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- base_model: roberta-base
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  tags:
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  - generated_from_trainer
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  metrics:
@@ -15,10 +15,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # best_model-yelp_polarity-16-13
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- This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3000
21
- - Accuracy: 0.9062
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  ## Model description
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@@ -50,156 +50,156 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
- | No log | 1.0 | 1 | 0.6990 | 0.5 |
54
- | No log | 2.0 | 2 | 0.6990 | 0.5 |
55
- | No log | 3.0 | 3 | 0.6990 | 0.5 |
56
- | No log | 4.0 | 4 | 0.6990 | 0.5 |
57
- | No log | 5.0 | 5 | 0.6990 | 0.5 |
58
- | No log | 6.0 | 6 | 0.6990 | 0.5 |
59
- | No log | 7.0 | 7 | 0.6989 | 0.5 |
60
- | No log | 8.0 | 8 | 0.6989 | 0.5 |
61
- | No log | 9.0 | 9 | 0.6989 | 0.5 |
62
- | 0.7032 | 10.0 | 10 | 0.6989 | 0.5 |
63
- | 0.7032 | 11.0 | 11 | 0.6989 | 0.5 |
64
- | 0.7032 | 12.0 | 12 | 0.6988 | 0.5 |
65
- | 0.7032 | 13.0 | 13 | 0.6988 | 0.5 |
66
- | 0.7032 | 14.0 | 14 | 0.6988 | 0.5 |
67
- | 0.7032 | 15.0 | 15 | 0.6987 | 0.5 |
68
- | 0.7032 | 16.0 | 16 | 0.6987 | 0.5 |
69
- | 0.7032 | 17.0 | 17 | 0.6986 | 0.5 |
70
- | 0.7032 | 18.0 | 18 | 0.6986 | 0.5 |
71
- | 0.7032 | 19.0 | 19 | 0.6985 | 0.5 |
72
- | 0.708 | 20.0 | 20 | 0.6985 | 0.5 |
73
- | 0.708 | 21.0 | 21 | 0.6984 | 0.5 |
74
- | 0.708 | 22.0 | 22 | 0.6983 | 0.5 |
75
- | 0.708 | 23.0 | 23 | 0.6983 | 0.5 |
76
- | 0.708 | 24.0 | 24 | 0.6982 | 0.5 |
77
- | 0.708 | 25.0 | 25 | 0.6981 | 0.5 |
78
- | 0.708 | 26.0 | 26 | 0.6981 | 0.5 |
79
- | 0.708 | 27.0 | 27 | 0.6980 | 0.5 |
80
- | 0.708 | 28.0 | 28 | 0.6979 | 0.5 |
81
- | 0.708 | 29.0 | 29 | 0.6978 | 0.5 |
82
- | 0.6974 | 30.0 | 30 | 0.6978 | 0.5 |
83
- | 0.6974 | 31.0 | 31 | 0.6977 | 0.5 |
84
- | 0.6974 | 32.0 | 32 | 0.6976 | 0.5 |
85
- | 0.6974 | 33.0 | 33 | 0.6975 | 0.5 |
86
- | 0.6974 | 34.0 | 34 | 0.6974 | 0.5 |
87
- | 0.6974 | 35.0 | 35 | 0.6973 | 0.5 |
88
- | 0.6974 | 36.0 | 36 | 0.6972 | 0.5 |
89
- | 0.6974 | 37.0 | 37 | 0.6971 | 0.5 |
90
- | 0.6974 | 38.0 | 38 | 0.6970 | 0.5 |
91
- | 0.6974 | 39.0 | 39 | 0.6969 | 0.5 |
92
- | 0.6965 | 40.0 | 40 | 0.6968 | 0.5 |
93
- | 0.6965 | 41.0 | 41 | 0.6967 | 0.5 |
94
- | 0.6965 | 42.0 | 42 | 0.6966 | 0.5 |
95
- | 0.6965 | 43.0 | 43 | 0.6965 | 0.5 |
96
- | 0.6965 | 44.0 | 44 | 0.6964 | 0.5 |
97
- | 0.6965 | 45.0 | 45 | 0.6963 | 0.5 |
98
- | 0.6965 | 46.0 | 46 | 0.6962 | 0.5 |
99
- | 0.6965 | 47.0 | 47 | 0.6961 | 0.5 |
100
- | 0.6965 | 48.0 | 48 | 0.6960 | 0.5 |
101
- | 0.6965 | 49.0 | 49 | 0.6958 | 0.5 |
102
- | 0.6971 | 50.0 | 50 | 0.6957 | 0.5 |
103
- | 0.6971 | 51.0 | 51 | 0.6956 | 0.5 |
104
- | 0.6971 | 52.0 | 52 | 0.6955 | 0.5 |
105
- | 0.6971 | 53.0 | 53 | 0.6953 | 0.5 |
106
- | 0.6971 | 54.0 | 54 | 0.6952 | 0.5 |
107
- | 0.6971 | 55.0 | 55 | 0.6950 | 0.5 |
108
- | 0.6971 | 56.0 | 56 | 0.6949 | 0.5 |
109
- | 0.6971 | 57.0 | 57 | 0.6948 | 0.5 |
110
- | 0.6971 | 58.0 | 58 | 0.6946 | 0.5 |
111
- | 0.6971 | 59.0 | 59 | 0.6945 | 0.5 |
112
- | 0.6932 | 60.0 | 60 | 0.6943 | 0.5 |
113
- | 0.6932 | 61.0 | 61 | 0.6942 | 0.5 |
114
- | 0.6932 | 62.0 | 62 | 0.6940 | 0.5 |
115
- | 0.6932 | 63.0 | 63 | 0.6939 | 0.5 |
116
- | 0.6932 | 64.0 | 64 | 0.6937 | 0.5 |
117
- | 0.6932 | 65.0 | 65 | 0.6936 | 0.5 |
118
- | 0.6932 | 66.0 | 66 | 0.6934 | 0.5 |
119
- | 0.6932 | 67.0 | 67 | 0.6933 | 0.5 |
120
- | 0.6932 | 68.0 | 68 | 0.6931 | 0.5 |
121
- | 0.6932 | 69.0 | 69 | 0.6929 | 0.5 |
122
- | 0.6964 | 70.0 | 70 | 0.6928 | 0.5 |
123
- | 0.6964 | 71.0 | 71 | 0.6926 | 0.5 |
124
- | 0.6964 | 72.0 | 72 | 0.6924 | 0.5 |
125
- | 0.6964 | 73.0 | 73 | 0.6923 | 0.5 |
126
- | 0.6964 | 74.0 | 74 | 0.6921 | 0.5 |
127
- | 0.6964 | 75.0 | 75 | 0.6919 | 0.5 |
128
- | 0.6964 | 76.0 | 76 | 0.6917 | 0.5 |
129
- | 0.6964 | 77.0 | 77 | 0.6915 | 0.5 |
130
- | 0.6964 | 78.0 | 78 | 0.6913 | 0.5 |
131
- | 0.6964 | 79.0 | 79 | 0.6911 | 0.5 |
132
- | 0.6875 | 80.0 | 80 | 0.6909 | 0.5 |
133
- | 0.6875 | 81.0 | 81 | 0.6907 | 0.5 |
134
- | 0.6875 | 82.0 | 82 | 0.6905 | 0.5 |
135
- | 0.6875 | 83.0 | 83 | 0.6902 | 0.5 |
136
- | 0.6875 | 84.0 | 84 | 0.6900 | 0.5 |
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- | 0.6875 | 85.0 | 85 | 0.6898 | 0.5 |
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- | 0.6875 | 86.0 | 86 | 0.6895 | 0.5 |
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- | 0.6875 | 87.0 | 87 | 0.6892 | 0.5 |
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- | 0.6875 | 88.0 | 88 | 0.6889 | 0.5 |
141
- | 0.6875 | 89.0 | 89 | 0.6886 | 0.5 |
142
- | 0.6885 | 90.0 | 90 | 0.6883 | 0.5 |
143
- | 0.6885 | 91.0 | 91 | 0.6880 | 0.5 |
144
- | 0.6885 | 92.0 | 92 | 0.6876 | 0.5 |
145
- | 0.6885 | 93.0 | 93 | 0.6873 | 0.5 |
146
- | 0.6885 | 94.0 | 94 | 0.6868 | 0.5 |
147
- | 0.6885 | 95.0 | 95 | 0.6864 | 0.5 |
148
- | 0.6885 | 96.0 | 96 | 0.6859 | 0.5 |
149
- | 0.6885 | 97.0 | 97 | 0.6854 | 0.5 |
150
- | 0.6885 | 98.0 | 98 | 0.6848 | 0.5 |
151
- | 0.6885 | 99.0 | 99 | 0.6842 | 0.5 |
152
- | 0.6748 | 100.0 | 100 | 0.6836 | 0.5 |
153
- | 0.6748 | 101.0 | 101 | 0.6829 | 0.5 |
154
- | 0.6748 | 102.0 | 102 | 0.6822 | 0.5 |
155
- | 0.6748 | 103.0 | 103 | 0.6815 | 0.5312 |
156
- | 0.6748 | 104.0 | 104 | 0.6807 | 0.5312 |
157
- | 0.6748 | 105.0 | 105 | 0.6798 | 0.5312 |
158
- | 0.6748 | 106.0 | 106 | 0.6788 | 0.5312 |
159
- | 0.6748 | 107.0 | 107 | 0.6777 | 0.5312 |
160
- | 0.6748 | 108.0 | 108 | 0.6765 | 0.5312 |
161
- | 0.6748 | 109.0 | 109 | 0.6752 | 0.5312 |
162
- | 0.664 | 110.0 | 110 | 0.6738 | 0.5312 |
163
- | 0.664 | 111.0 | 111 | 0.6722 | 0.5312 |
164
- | 0.664 | 112.0 | 112 | 0.6705 | 0.5312 |
165
- | 0.664 | 113.0 | 113 | 0.6686 | 0.5625 |
166
- | 0.664 | 114.0 | 114 | 0.6666 | 0.5625 |
167
- | 0.664 | 115.0 | 115 | 0.6645 | 0.5938 |
168
- | 0.664 | 116.0 | 116 | 0.6622 | 0.5938 |
169
- | 0.664 | 117.0 | 117 | 0.6595 | 0.5938 |
170
- | 0.664 | 118.0 | 118 | 0.6566 | 0.5938 |
171
- | 0.664 | 119.0 | 119 | 0.6533 | 0.5938 |
172
- | 0.6328 | 120.0 | 120 | 0.6497 | 0.5938 |
173
- | 0.6328 | 121.0 | 121 | 0.6456 | 0.5938 |
174
- | 0.6328 | 122.0 | 122 | 0.6410 | 0.6562 |
175
- | 0.6328 | 123.0 | 123 | 0.6359 | 0.6875 |
176
- | 0.6328 | 124.0 | 124 | 0.6302 | 0.6875 |
177
- | 0.6328 | 125.0 | 125 | 0.6238 | 0.7188 |
178
- | 0.6328 | 126.0 | 126 | 0.6164 | 0.7188 |
179
- | 0.6328 | 127.0 | 127 | 0.6080 | 0.7812 |
180
- | 0.6328 | 128.0 | 128 | 0.5986 | 0.7812 |
181
- | 0.6328 | 129.0 | 129 | 0.5881 | 0.7812 |
182
- | 0.5516 | 130.0 | 130 | 0.5762 | 0.8125 |
183
- | 0.5516 | 131.0 | 131 | 0.5633 | 0.875 |
184
- | 0.5516 | 132.0 | 132 | 0.5493 | 0.875 |
185
- | 0.5516 | 133.0 | 133 | 0.5349 | 0.875 |
186
- | 0.5516 | 134.0 | 134 | 0.5201 | 0.875 |
187
- | 0.5516 | 135.0 | 135 | 0.5055 | 0.875 |
188
- | 0.5516 | 136.0 | 136 | 0.4909 | 0.875 |
189
- | 0.5516 | 137.0 | 137 | 0.4762 | 0.875 |
190
- | 0.5516 | 138.0 | 138 | 0.4608 | 0.875 |
191
- | 0.5516 | 139.0 | 139 | 0.4448 | 0.875 |
192
- | 0.3883 | 140.0 | 140 | 0.4275 | 0.9375 |
193
- | 0.3883 | 141.0 | 141 | 0.4112 | 0.9062 |
194
- | 0.3883 | 142.0 | 142 | 0.3955 | 0.9062 |
195
- | 0.3883 | 143.0 | 143 | 0.3799 | 0.9062 |
196
- | 0.3883 | 144.0 | 144 | 0.3660 | 0.9062 |
197
- | 0.3883 | 145.0 | 145 | 0.3524 | 0.9062 |
198
- | 0.3883 | 146.0 | 146 | 0.3394 | 0.9062 |
199
- | 0.3883 | 147.0 | 147 | 0.3270 | 0.9062 |
200
- | 0.3883 | 148.0 | 148 | 0.3159 | 0.9062 |
201
- | 0.3883 | 149.0 | 149 | 0.3072 | 0.9062 |
202
- | 0.2135 | 150.0 | 150 | 0.3000 | 0.9062 |
203
 
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205
  ### Framework versions
 
1
  ---
2
+ license: apache-2.0
3
+ base_model: albert-base-v2
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  tags:
5
  - generated_from_trainer
6
  metrics:
 
15
 
16
  # best_model-yelp_polarity-16-13
17
 
18
+ This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 0.3928
21
+ - Accuracy: 0.875
22
 
23
  ## Model description
24
 
 
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
+ | No log | 1.0 | 1 | 0.7228 | 0.5 |
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+ | No log | 2.0 | 2 | 0.7227 | 0.5 |
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+ | No log | 3.0 | 3 | 0.7227 | 0.5 |
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+ | No log | 4.0 | 4 | 0.7225 | 0.5 |
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+ | No log | 5.0 | 5 | 0.7224 | 0.5 |
58
+ | No log | 6.0 | 6 | 0.7221 | 0.5 |
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+ | No log | 7.0 | 7 | 0.7219 | 0.5 |
60
+ | No log | 8.0 | 8 | 0.7216 | 0.5 |
61
+ | No log | 9.0 | 9 | 0.7213 | 0.5 |
62
+ | 0.7034 | 10.0 | 10 | 0.7209 | 0.5 |
63
+ | 0.7034 | 11.0 | 11 | 0.7205 | 0.5 |
64
+ | 0.7034 | 12.0 | 12 | 0.7200 | 0.5 |
65
+ | 0.7034 | 13.0 | 13 | 0.7195 | 0.5 |
66
+ | 0.7034 | 14.0 | 14 | 0.7189 | 0.5 |
67
+ | 0.7034 | 15.0 | 15 | 0.7183 | 0.5 |
68
+ | 0.7034 | 16.0 | 16 | 0.7177 | 0.5 |
69
+ | 0.7034 | 17.0 | 17 | 0.7170 | 0.5 |
70
+ | 0.7034 | 18.0 | 18 | 0.7163 | 0.5 |
71
+ | 0.7034 | 19.0 | 19 | 0.7156 | 0.5 |
72
+ | 0.6925 | 20.0 | 20 | 0.7148 | 0.5 |
73
+ | 0.6925 | 21.0 | 21 | 0.7140 | 0.5 |
74
+ | 0.6925 | 22.0 | 22 | 0.7132 | 0.5 |
75
+ | 0.6925 | 23.0 | 23 | 0.7123 | 0.5 |
76
+ | 0.6925 | 24.0 | 24 | 0.7113 | 0.5 |
77
+ | 0.6925 | 25.0 | 25 | 0.7104 | 0.5 |
78
+ | 0.6925 | 26.0 | 26 | 0.7093 | 0.5 |
79
+ | 0.6925 | 27.0 | 27 | 0.7082 | 0.5 |
80
+ | 0.6925 | 28.0 | 28 | 0.7071 | 0.5 |
81
+ | 0.6925 | 29.0 | 29 | 0.7059 | 0.5 |
82
+ | 0.6581 | 30.0 | 30 | 0.7047 | 0.5 |
83
+ | 0.6581 | 31.0 | 31 | 0.7034 | 0.5 |
84
+ | 0.6581 | 32.0 | 32 | 0.7021 | 0.5 |
85
+ | 0.6581 | 33.0 | 33 | 0.7007 | 0.5 |
86
+ | 0.6581 | 34.0 | 34 | 0.6991 | 0.5 |
87
+ | 0.6581 | 35.0 | 35 | 0.6975 | 0.5 |
88
+ | 0.6581 | 36.0 | 36 | 0.6958 | 0.5 |
89
+ | 0.6581 | 37.0 | 37 | 0.6941 | 0.5 |
90
+ | 0.6581 | 38.0 | 38 | 0.6923 | 0.5 |
91
+ | 0.6581 | 39.0 | 39 | 0.6904 | 0.5 |
92
+ | 0.6325 | 40.0 | 40 | 0.6883 | 0.5 |
93
+ | 0.6325 | 41.0 | 41 | 0.6862 | 0.5 |
94
+ | 0.6325 | 42.0 | 42 | 0.6841 | 0.5 |
95
+ | 0.6325 | 43.0 | 43 | 0.6818 | 0.5 |
96
+ | 0.6325 | 44.0 | 44 | 0.6794 | 0.5 |
97
+ | 0.6325 | 45.0 | 45 | 0.6770 | 0.5 |
98
+ | 0.6325 | 46.0 | 46 | 0.6745 | 0.5312 |
99
+ | 0.6325 | 47.0 | 47 | 0.6718 | 0.5312 |
100
+ | 0.6325 | 48.0 | 48 | 0.6690 | 0.5312 |
101
+ | 0.6325 | 49.0 | 49 | 0.6662 | 0.5625 |
102
+ | 0.573 | 50.0 | 50 | 0.6633 | 0.5625 |
103
+ | 0.573 | 51.0 | 51 | 0.6602 | 0.5625 |
104
+ | 0.573 | 52.0 | 52 | 0.6571 | 0.5625 |
105
+ | 0.573 | 53.0 | 53 | 0.6538 | 0.5625 |
106
+ | 0.573 | 54.0 | 54 | 0.6504 | 0.5625 |
107
+ | 0.573 | 55.0 | 55 | 0.6469 | 0.5625 |
108
+ | 0.573 | 56.0 | 56 | 0.6435 | 0.5625 |
109
+ | 0.573 | 57.0 | 57 | 0.6401 | 0.625 |
110
+ | 0.573 | 58.0 | 58 | 0.6368 | 0.625 |
111
+ | 0.573 | 59.0 | 59 | 0.6336 | 0.6562 |
112
+ | 0.5136 | 60.0 | 60 | 0.6305 | 0.6875 |
113
+ | 0.5136 | 61.0 | 61 | 0.6273 | 0.6562 |
114
+ | 0.5136 | 62.0 | 62 | 0.6240 | 0.6562 |
115
+ | 0.5136 | 63.0 | 63 | 0.6206 | 0.6562 |
116
+ | 0.5136 | 64.0 | 64 | 0.6172 | 0.6875 |
117
+ | 0.5136 | 65.0 | 65 | 0.6138 | 0.6875 |
118
+ | 0.5136 | 66.0 | 66 | 0.6105 | 0.6875 |
119
+ | 0.5136 | 67.0 | 67 | 0.6072 | 0.6875 |
120
+ | 0.5136 | 68.0 | 68 | 0.6038 | 0.6875 |
121
+ | 0.5136 | 69.0 | 69 | 0.6004 | 0.6875 |
122
+ | 0.4388 | 70.0 | 70 | 0.5968 | 0.6875 |
123
+ | 0.4388 | 71.0 | 71 | 0.5931 | 0.7188 |
124
+ | 0.4388 | 72.0 | 72 | 0.5893 | 0.75 |
125
+ | 0.4388 | 73.0 | 73 | 0.5854 | 0.75 |
126
+ | 0.4388 | 74.0 | 74 | 0.5814 | 0.75 |
127
+ | 0.4388 | 75.0 | 75 | 0.5773 | 0.75 |
128
+ | 0.4388 | 76.0 | 76 | 0.5732 | 0.75 |
129
+ | 0.4388 | 77.0 | 77 | 0.5695 | 0.7812 |
130
+ | 0.4388 | 78.0 | 78 | 0.5660 | 0.7812 |
131
+ | 0.4388 | 79.0 | 79 | 0.5626 | 0.7812 |
132
+ | 0.3545 | 80.0 | 80 | 0.5590 | 0.7812 |
133
+ | 0.3545 | 81.0 | 81 | 0.5553 | 0.7812 |
134
+ | 0.3545 | 82.0 | 82 | 0.5514 | 0.8125 |
135
+ | 0.3545 | 83.0 | 83 | 0.5476 | 0.7812 |
136
+ | 0.3545 | 84.0 | 84 | 0.5437 | 0.7812 |
137
+ | 0.3545 | 85.0 | 85 | 0.5396 | 0.7812 |
138
+ | 0.3545 | 86.0 | 86 | 0.5358 | 0.7812 |
139
+ | 0.3545 | 87.0 | 87 | 0.5316 | 0.7812 |
140
+ | 0.3545 | 88.0 | 88 | 0.5277 | 0.7812 |
141
+ | 0.3545 | 89.0 | 89 | 0.5238 | 0.7812 |
142
+ | 0.2725 | 90.0 | 90 | 0.5197 | 0.7812 |
143
+ | 0.2725 | 91.0 | 91 | 0.5159 | 0.7812 |
144
+ | 0.2725 | 92.0 | 92 | 0.5120 | 0.7812 |
145
+ | 0.2725 | 93.0 | 93 | 0.5079 | 0.7812 |
146
+ | 0.2725 | 94.0 | 94 | 0.5034 | 0.7812 |
147
+ | 0.2725 | 95.0 | 95 | 0.4983 | 0.7812 |
148
+ | 0.2725 | 96.0 | 96 | 0.4934 | 0.7812 |
149
+ | 0.2725 | 97.0 | 97 | 0.4885 | 0.7812 |
150
+ | 0.2725 | 98.0 | 98 | 0.4835 | 0.7812 |
151
+ | 0.2725 | 99.0 | 99 | 0.4790 | 0.8125 |
152
+ | 0.199 | 100.0 | 100 | 0.4751 | 0.8125 |
153
+ | 0.199 | 101.0 | 101 | 0.4714 | 0.8125 |
154
+ | 0.199 | 102.0 | 102 | 0.4677 | 0.8125 |
155
+ | 0.199 | 103.0 | 103 | 0.4634 | 0.8438 |
156
+ | 0.199 | 104.0 | 104 | 0.4585 | 0.8438 |
157
+ | 0.199 | 105.0 | 105 | 0.4532 | 0.875 |
158
+ | 0.199 | 106.0 | 106 | 0.4484 | 0.875 |
159
+ | 0.199 | 107.0 | 107 | 0.4439 | 0.875 |
160
+ | 0.199 | 108.0 | 108 | 0.4400 | 0.875 |
161
+ | 0.199 | 109.0 | 109 | 0.4363 | 0.875 |
162
+ | 0.1406 | 110.0 | 110 | 0.4329 | 0.875 |
163
+ | 0.1406 | 111.0 | 111 | 0.4296 | 0.875 |
164
+ | 0.1406 | 112.0 | 112 | 0.4259 | 0.875 |
165
+ | 0.1406 | 113.0 | 113 | 0.4219 | 0.8438 |
166
+ | 0.1406 | 114.0 | 114 | 0.4176 | 0.8438 |
167
+ | 0.1406 | 115.0 | 115 | 0.4138 | 0.8438 |
168
+ | 0.1406 | 116.0 | 116 | 0.4108 | 0.8438 |
169
+ | 0.1406 | 117.0 | 117 | 0.4077 | 0.8438 |
170
+ | 0.1406 | 118.0 | 118 | 0.4042 | 0.8438 |
171
+ | 0.1406 | 119.0 | 119 | 0.4003 | 0.8438 |
172
+ | 0.0921 | 120.0 | 120 | 0.3968 | 0.8438 |
173
+ | 0.0921 | 121.0 | 121 | 0.3936 | 0.8438 |
174
+ | 0.0921 | 122.0 | 122 | 0.3905 | 0.8438 |
175
+ | 0.0921 | 123.0 | 123 | 0.3878 | 0.8438 |
176
+ | 0.0921 | 124.0 | 124 | 0.3851 | 0.8438 |
177
+ | 0.0921 | 125.0 | 125 | 0.3823 | 0.8438 |
178
+ | 0.0921 | 126.0 | 126 | 0.3802 | 0.8438 |
179
+ | 0.0921 | 127.0 | 127 | 0.3786 | 0.8438 |
180
+ | 0.0921 | 128.0 | 128 | 0.3769 | 0.8125 |
181
+ | 0.0921 | 129.0 | 129 | 0.3748 | 0.8125 |
182
+ | 0.0543 | 130.0 | 130 | 0.3721 | 0.8125 |
183
+ | 0.0543 | 131.0 | 131 | 0.3700 | 0.8125 |
184
+ | 0.0543 | 132.0 | 132 | 0.3685 | 0.8125 |
185
+ | 0.0543 | 133.0 | 133 | 0.3687 | 0.8125 |
186
+ | 0.0543 | 134.0 | 134 | 0.3699 | 0.8125 |
187
+ | 0.0543 | 135.0 | 135 | 0.3711 | 0.8125 |
188
+ | 0.0543 | 136.0 | 136 | 0.3719 | 0.8125 |
189
+ | 0.0543 | 137.0 | 137 | 0.3716 | 0.8125 |
190
+ | 0.0543 | 138.0 | 138 | 0.3706 | 0.8438 |
191
+ | 0.0543 | 139.0 | 139 | 0.3699 | 0.8438 |
192
+ | 0.0313 | 140.0 | 140 | 0.3692 | 0.875 |
193
+ | 0.0313 | 141.0 | 141 | 0.3690 | 0.875 |
194
+ | 0.0313 | 142.0 | 142 | 0.3690 | 0.875 |
195
+ | 0.0313 | 143.0 | 143 | 0.3698 | 0.875 |
196
+ | 0.0313 | 144.0 | 144 | 0.3715 | 0.875 |
197
+ | 0.0313 | 145.0 | 145 | 0.3737 | 0.875 |
198
+ | 0.0313 | 146.0 | 146 | 0.3766 | 0.875 |
199
+ | 0.0313 | 147.0 | 147 | 0.3798 | 0.875 |
200
+ | 0.0313 | 148.0 | 148 | 0.3838 | 0.875 |
201
+ | 0.0313 | 149.0 | 149 | 0.3884 | 0.875 |
202
+ | 0.0183 | 150.0 | 150 | 0.3928 | 0.875 |
203
 
204
 
205
  ### Framework versions