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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:
@@ -15,9 +15,9 @@ should probably proofread and complete it, then remove this comment. -->
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  # best_model-yelp_polarity-32-13
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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:
20
- - Loss: 0.7343
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  - Accuracy: 0.9219
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  ## Model description
@@ -50,156 +50,156 @@ The following hyperparameters were used during training:
50
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
- | No log | 1.0 | 2 | 0.5150 | 0.9375 |
54
- | No log | 2.0 | 4 | 0.5183 | 0.9375 |
55
- | No log | 3.0 | 6 | 0.5239 | 0.9375 |
56
- | No log | 4.0 | 8 | 0.5297 | 0.9375 |
57
- | 0.121 | 5.0 | 10 | 0.5354 | 0.9375 |
58
- | 0.121 | 6.0 | 12 | 0.5416 | 0.9375 |
59
- | 0.121 | 7.0 | 14 | 0.5505 | 0.9375 |
60
- | 0.121 | 8.0 | 16 | 0.5631 | 0.9219 |
61
- | 0.121 | 9.0 | 18 | 0.5919 | 0.9219 |
62
- | 0.0647 | 10.0 | 20 | 0.6157 | 0.9219 |
63
- | 0.0647 | 11.0 | 22 | 0.6462 | 0.9062 |
64
- | 0.0647 | 12.0 | 24 | 0.6650 | 0.9062 |
65
- | 0.0647 | 13.0 | 26 | 0.6774 | 0.9062 |
66
- | 0.0647 | 14.0 | 28 | 0.6785 | 0.9062 |
67
- | 0.0493 | 15.0 | 30 | 0.6712 | 0.9062 |
68
- | 0.0493 | 16.0 | 32 | 0.6561 | 0.9062 |
69
- | 0.0493 | 17.0 | 34 | 0.6397 | 0.9219 |
70
- | 0.0493 | 18.0 | 36 | 0.6254 | 0.9219 |
71
- | 0.0493 | 19.0 | 38 | 0.6044 | 0.9219 |
72
- | 0.0344 | 20.0 | 40 | 0.5844 | 0.9219 |
73
- | 0.0344 | 21.0 | 42 | 0.5757 | 0.9219 |
74
- | 0.0344 | 22.0 | 44 | 0.5695 | 0.9219 |
75
- | 0.0344 | 23.0 | 46 | 0.5683 | 0.9219 |
76
- | 0.0344 | 24.0 | 48 | 0.5848 | 0.9219 |
77
- | 0.0002 | 25.0 | 50 | 0.6016 | 0.9219 |
78
- | 0.0002 | 26.0 | 52 | 0.6158 | 0.9219 |
79
- | 0.0002 | 27.0 | 54 | 0.6269 | 0.9219 |
80
- | 0.0002 | 28.0 | 56 | 0.6424 | 0.9219 |
81
- | 0.0002 | 29.0 | 58 | 0.6560 | 0.9219 |
82
- | 0.0039 | 30.0 | 60 | 0.6640 | 0.9219 |
83
- | 0.0039 | 31.0 | 62 | 0.6670 | 0.9219 |
84
- | 0.0039 | 32.0 | 64 | 0.6696 | 0.9219 |
85
- | 0.0039 | 33.0 | 66 | 0.6720 | 0.9219 |
86
- | 0.0039 | 34.0 | 68 | 0.6731 | 0.9219 |
87
- | 0.0002 | 35.0 | 70 | 0.6740 | 0.9219 |
88
- | 0.0002 | 36.0 | 72 | 0.6748 | 0.9219 |
89
- | 0.0002 | 37.0 | 74 | 0.6735 | 0.9219 |
90
- | 0.0002 | 38.0 | 76 | 0.6727 | 0.9219 |
91
- | 0.0002 | 39.0 | 78 | 0.6710 | 0.9219 |
92
- | 0.0001 | 40.0 | 80 | 0.6682 | 0.9219 |
93
- | 0.0001 | 41.0 | 82 | 0.6650 | 0.9219 |
94
- | 0.0001 | 42.0 | 84 | 0.6767 | 0.9219 |
95
- | 0.0001 | 43.0 | 86 | 0.6856 | 0.9219 |
96
- | 0.0001 | 44.0 | 88 | 0.6906 | 0.9219 |
97
- | 0.0001 | 45.0 | 90 | 0.6949 | 0.9219 |
98
- | 0.0001 | 46.0 | 92 | 0.6931 | 0.9219 |
99
- | 0.0001 | 47.0 | 94 | 0.6904 | 0.9219 |
100
- | 0.0001 | 48.0 | 96 | 0.6855 | 0.9219 |
101
- | 0.0001 | 49.0 | 98 | 0.6793 | 0.9219 |
102
- | 0.0002 | 50.0 | 100 | 0.6721 | 0.9219 |
103
- | 0.0002 | 51.0 | 102 | 0.6642 | 0.9219 |
104
- | 0.0002 | 52.0 | 104 | 0.6566 | 0.9219 |
105
- | 0.0002 | 53.0 | 106 | 0.6494 | 0.9219 |
106
- | 0.0002 | 54.0 | 108 | 0.6429 | 0.9219 |
107
- | 0.0001 | 55.0 | 110 | 0.6377 | 0.9219 |
108
- | 0.0001 | 56.0 | 112 | 0.6401 | 0.9219 |
109
- | 0.0001 | 57.0 | 114 | 0.6488 | 0.9219 |
110
- | 0.0001 | 58.0 | 116 | 0.6571 | 0.9219 |
111
- | 0.0001 | 59.0 | 118 | 0.6641 | 0.9219 |
112
- | 0.0001 | 60.0 | 120 | 0.6696 | 0.9219 |
113
- | 0.0001 | 61.0 | 122 | 0.6740 | 0.9219 |
114
- | 0.0001 | 62.0 | 124 | 0.6776 | 0.9219 |
115
- | 0.0001 | 63.0 | 126 | 0.6806 | 0.9219 |
116
- | 0.0001 | 64.0 | 128 | 0.6831 | 0.9219 |
117
- | 0.0001 | 65.0 | 130 | 0.6851 | 0.9219 |
118
- | 0.0001 | 66.0 | 132 | 0.6871 | 0.9219 |
119
- | 0.0001 | 67.0 | 134 | 0.6893 | 0.9219 |
120
- | 0.0001 | 68.0 | 136 | 0.6912 | 0.9219 |
121
- | 0.0001 | 69.0 | 138 | 0.6925 | 0.9219 |
122
- | 0.0001 | 70.0 | 140 | 0.6936 | 0.9219 |
123
- | 0.0001 | 71.0 | 142 | 0.6946 | 0.9219 |
124
- | 0.0001 | 72.0 | 144 | 0.6956 | 0.9219 |
125
- | 0.0001 | 73.0 | 146 | 0.6963 | 0.9219 |
126
- | 0.0001 | 74.0 | 148 | 0.6969 | 0.9219 |
127
- | 0.0001 | 75.0 | 150 | 0.6972 | 0.9219 |
128
- | 0.0001 | 76.0 | 152 | 0.6977 | 0.9219 |
129
- | 0.0001 | 77.0 | 154 | 0.6982 | 0.9219 |
130
- | 0.0001 | 78.0 | 156 | 0.6984 | 0.9219 |
131
- | 0.0001 | 79.0 | 158 | 0.6989 | 0.9219 |
132
- | 0.0001 | 80.0 | 160 | 0.6996 | 0.9219 |
133
- | 0.0001 | 81.0 | 162 | 0.7006 | 0.9219 |
134
- | 0.0001 | 82.0 | 164 | 0.7011 | 0.9219 |
135
- | 0.0001 | 83.0 | 166 | 0.7016 | 0.9219 |
136
- | 0.0001 | 84.0 | 168 | 0.7024 | 0.9219 |
137
- | 0.0001 | 85.0 | 170 | 0.7030 | 0.9219 |
138
- | 0.0001 | 86.0 | 172 | 0.7038 | 0.9219 |
139
- | 0.0001 | 87.0 | 174 | 0.7051 | 0.9219 |
140
- | 0.0001 | 88.0 | 176 | 0.7061 | 0.9219 |
141
- | 0.0001 | 89.0 | 178 | 0.7072 | 0.9219 |
142
- | 0.0001 | 90.0 | 180 | 0.7082 | 0.9219 |
143
- | 0.0001 | 91.0 | 182 | 0.7091 | 0.9219 |
144
- | 0.0001 | 92.0 | 184 | 0.7099 | 0.9219 |
145
- | 0.0001 | 93.0 | 186 | 0.7107 | 0.9219 |
146
- | 0.0001 | 94.0 | 188 | 0.7116 | 0.9219 |
147
- | 0.0001 | 95.0 | 190 | 0.7126 | 0.9219 |
148
- | 0.0001 | 96.0 | 192 | 0.7136 | 0.9219 |
149
- | 0.0001 | 97.0 | 194 | 0.7146 | 0.9219 |
150
- | 0.0001 | 98.0 | 196 | 0.7156 | 0.9219 |
151
- | 0.0001 | 99.0 | 198 | 0.7165 | 0.9219 |
152
- | 0.0001 | 100.0 | 200 | 0.7172 | 0.9219 |
153
- | 0.0001 | 101.0 | 202 | 0.7172 | 0.9219 |
154
- | 0.0001 | 102.0 | 204 | 0.7174 | 0.9219 |
155
- | 0.0001 | 103.0 | 206 | 0.7178 | 0.9219 |
156
- | 0.0001 | 104.0 | 208 | 0.7188 | 0.9219 |
157
- | 0.0001 | 105.0 | 210 | 0.7195 | 0.9219 |
158
- | 0.0001 | 106.0 | 212 | 0.7203 | 0.9219 |
159
- | 0.0001 | 107.0 | 214 | 0.7212 | 0.9219 |
160
- | 0.0001 | 108.0 | 216 | 0.7220 | 0.9219 |
161
- | 0.0001 | 109.0 | 218 | 0.7230 | 0.9219 |
162
- | 0.0001 | 110.0 | 220 | 0.7247 | 0.9219 |
163
- | 0.0001 | 111.0 | 222 | 0.7264 | 0.9219 |
164
- | 0.0001 | 112.0 | 224 | 0.7280 | 0.9219 |
165
- | 0.0001 | 113.0 | 226 | 0.7294 | 0.9219 |
166
- | 0.0001 | 114.0 | 228 | 0.7313 | 0.9219 |
167
- | 0.0001 | 115.0 | 230 | 0.7328 | 0.9219 |
168
- | 0.0001 | 116.0 | 232 | 0.7343 | 0.9219 |
169
- | 0.0001 | 117.0 | 234 | 0.7357 | 0.9219 |
170
- | 0.0001 | 118.0 | 236 | 0.7369 | 0.9219 |
171
- | 0.0001 | 119.0 | 238 | 0.7378 | 0.9219 |
172
- | 0.0001 | 120.0 | 240 | 0.7387 | 0.9219 |
173
- | 0.0001 | 121.0 | 242 | 0.7394 | 0.9219 |
174
- | 0.0001 | 122.0 | 244 | 0.7401 | 0.9219 |
175
- | 0.0001 | 123.0 | 246 | 0.7409 | 0.9219 |
176
- | 0.0001 | 124.0 | 248 | 0.7418 | 0.9219 |
177
- | 0.0 | 125.0 | 250 | 0.7427 | 0.9219 |
178
- | 0.0 | 126.0 | 252 | 0.7438 | 0.9219 |
179
- | 0.0 | 127.0 | 254 | 0.7451 | 0.9219 |
180
- | 0.0 | 128.0 | 256 | 0.7463 | 0.9219 |
181
- | 0.0 | 129.0 | 258 | 0.7474 | 0.9219 |
182
- | 0.0 | 130.0 | 260 | 0.7486 | 0.9219 |
183
- | 0.0 | 131.0 | 262 | 0.7500 | 0.9219 |
184
- | 0.0 | 132.0 | 264 | 0.7514 | 0.9219 |
185
- | 0.0 | 133.0 | 266 | 0.7528 | 0.9219 |
186
- | 0.0 | 134.0 | 268 | 0.8507 | 0.8906 |
187
- | 0.0001 | 135.0 | 270 | 1.0733 | 0.8906 |
188
- | 0.0001 | 136.0 | 272 | 1.2689 | 0.8594 |
189
- | 0.0001 | 137.0 | 274 | 0.9691 | 0.8906 |
190
- | 0.0001 | 138.0 | 276 | 0.7454 | 0.9062 |
191
- | 0.0001 | 139.0 | 278 | 0.7415 | 0.9219 |
192
- | 0.0136 | 140.0 | 280 | 0.7437 | 0.9219 |
193
- | 0.0136 | 141.0 | 282 | 0.7095 | 0.9219 |
194
- | 0.0136 | 142.0 | 284 | 0.6249 | 0.9219 |
195
- | 0.0136 | 143.0 | 286 | 0.5231 | 0.9375 |
196
- | 0.0136 | 144.0 | 288 | 0.4934 | 0.9531 |
197
- | 0.0 | 145.0 | 290 | 0.4934 | 0.9531 |
198
- | 0.0 | 146.0 | 292 | 0.6506 | 0.9219 |
199
- | 0.0 | 147.0 | 294 | 0.7018 | 0.9219 |
200
- | 0.0 | 148.0 | 296 | 0.6696 | 0.9219 |
201
- | 0.0 | 149.0 | 298 | 0.7124 | 0.9219 |
202
- | 0.022 | 150.0 | 300 | 0.7343 | 0.9219 |
203
 
204
 
205
  ### Framework versions
 
1
  ---
2
+ license: apache-2.0
3
+ base_model: albert-base-v2
4
  tags:
5
  - generated_from_trainer
6
  metrics:
 
15
 
16
  # best_model-yelp_polarity-32-13
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: 0.5144
21
  - Accuracy: 0.9219
22
 
23
  ## Model description
 
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
+ | No log | 1.0 | 2 | 0.6082 | 0.8906 |
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+ | No log | 2.0 | 4 | 0.6076 | 0.8906 |
55
+ | No log | 3.0 | 6 | 0.6029 | 0.9062 |
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+ | No log | 4.0 | 8 | 0.6007 | 0.9062 |
57
+ | 0.5399 | 5.0 | 10 | 0.5942 | 0.9062 |
58
+ | 0.5399 | 6.0 | 12 | 0.5899 | 0.9062 |
59
+ | 0.5399 | 7.0 | 14 | 0.5812 | 0.9062 |
60
+ | 0.5399 | 8.0 | 16 | 0.5718 | 0.9062 |
61
+ | 0.5399 | 9.0 | 18 | 0.5613 | 0.9062 |
62
+ | 0.4539 | 10.0 | 20 | 0.5576 | 0.9219 |
63
+ | 0.4539 | 11.0 | 22 | 0.5573 | 0.9219 |
64
+ | 0.4539 | 12.0 | 24 | 0.5612 | 0.9219 |
65
+ | 0.4539 | 13.0 | 26 | 0.5724 | 0.9062 |
66
+ | 0.4539 | 14.0 | 28 | 0.6101 | 0.8906 |
67
+ | 0.3252 | 15.0 | 30 | 0.6515 | 0.8906 |
68
+ | 0.3252 | 16.0 | 32 | 0.6612 | 0.8906 |
69
+ | 0.3252 | 17.0 | 34 | 0.6215 | 0.8906 |
70
+ | 0.3252 | 18.0 | 36 | 0.5622 | 0.9219 |
71
+ | 0.3252 | 19.0 | 38 | 0.5454 | 0.9219 |
72
+ | 0.2192 | 20.0 | 40 | 0.5331 | 0.9219 |
73
+ | 0.2192 | 21.0 | 42 | 0.5137 | 0.9219 |
74
+ | 0.2192 | 22.0 | 44 | 0.5021 | 0.9219 |
75
+ | 0.2192 | 23.0 | 46 | 0.5023 | 0.9219 |
76
+ | 0.2192 | 24.0 | 48 | 0.5072 | 0.9219 |
77
+ | 0.1347 | 25.0 | 50 | 0.5089 | 0.9219 |
78
+ | 0.1347 | 26.0 | 52 | 0.5062 | 0.9219 |
79
+ | 0.1347 | 27.0 | 54 | 0.5079 | 0.9062 |
80
+ | 0.1347 | 28.0 | 56 | 0.5042 | 0.9062 |
81
+ | 0.1347 | 29.0 | 58 | 0.4885 | 0.9062 |
82
+ | 0.0984 | 30.0 | 60 | 0.4719 | 0.9062 |
83
+ | 0.0984 | 31.0 | 62 | 0.4657 | 0.9062 |
84
+ | 0.0984 | 32.0 | 64 | 0.4671 | 0.9062 |
85
+ | 0.0984 | 33.0 | 66 | 0.4626 | 0.9062 |
86
+ | 0.0984 | 34.0 | 68 | 0.4623 | 0.9062 |
87
+ | 0.0679 | 35.0 | 70 | 0.4629 | 0.9062 |
88
+ | 0.0679 | 36.0 | 72 | 0.4639 | 0.9062 |
89
+ | 0.0679 | 37.0 | 74 | 0.4669 | 0.9062 |
90
+ | 0.0679 | 38.0 | 76 | 0.4707 | 0.9062 |
91
+ | 0.0679 | 39.0 | 78 | 0.4729 | 0.9062 |
92
+ | 0.0447 | 40.0 | 80 | 0.4741 | 0.9062 |
93
+ | 0.0447 | 41.0 | 82 | 0.4789 | 0.9062 |
94
+ | 0.0447 | 42.0 | 84 | 0.4829 | 0.9062 |
95
+ | 0.0447 | 43.0 | 86 | 0.4858 | 0.9062 |
96
+ | 0.0447 | 44.0 | 88 | 0.4855 | 0.9062 |
97
+ | 0.0337 | 45.0 | 90 | 0.4863 | 0.9062 |
98
+ | 0.0337 | 46.0 | 92 | 0.4884 | 0.9062 |
99
+ | 0.0337 | 47.0 | 94 | 0.4888 | 0.9062 |
100
+ | 0.0337 | 48.0 | 96 | 0.4901 | 0.9062 |
101
+ | 0.0337 | 49.0 | 98 | 0.4937 | 0.9062 |
102
+ | 0.0241 | 50.0 | 100 | 0.5010 | 0.9062 |
103
+ | 0.0241 | 51.0 | 102 | 0.5028 | 0.9062 |
104
+ | 0.0241 | 52.0 | 104 | 0.4960 | 0.9062 |
105
+ | 0.0241 | 53.0 | 106 | 0.5056 | 0.9062 |
106
+ | 0.0241 | 54.0 | 108 | 0.5088 | 0.9062 |
107
+ | 0.0139 | 55.0 | 110 | 0.4949 | 0.9062 |
108
+ | 0.0139 | 56.0 | 112 | 0.4853 | 0.9062 |
109
+ | 0.0139 | 57.0 | 114 | 0.4616 | 0.9062 |
110
+ | 0.0139 | 58.0 | 116 | 0.4451 | 0.9219 |
111
+ | 0.0139 | 59.0 | 118 | 0.4400 | 0.9219 |
112
+ | 0.0064 | 60.0 | 120 | 0.4371 | 0.9219 |
113
+ | 0.0064 | 61.0 | 122 | 0.4255 | 0.9375 |
114
+ | 0.0064 | 62.0 | 124 | 0.4178 | 0.9375 |
115
+ | 0.0064 | 63.0 | 126 | 0.4154 | 0.9375 |
116
+ | 0.0064 | 64.0 | 128 | 0.4194 | 0.9375 |
117
+ | 0.0023 | 65.0 | 130 | 0.4217 | 0.9375 |
118
+ | 0.0023 | 66.0 | 132 | 0.4193 | 0.9375 |
119
+ | 0.0023 | 67.0 | 134 | 0.4165 | 0.9375 |
120
+ | 0.0023 | 68.0 | 136 | 0.4159 | 0.9375 |
121
+ | 0.0023 | 69.0 | 138 | 0.4167 | 0.9375 |
122
+ | 0.0009 | 70.0 | 140 | 0.4178 | 0.9375 |
123
+ | 0.0009 | 71.0 | 142 | 0.4197 | 0.9375 |
124
+ | 0.0009 | 72.0 | 144 | 0.4218 | 0.9375 |
125
+ | 0.0009 | 73.0 | 146 | 0.4239 | 0.9375 |
126
+ | 0.0009 | 74.0 | 148 | 0.4260 | 0.9375 |
127
+ | 0.0005 | 75.0 | 150 | 0.4281 | 0.9375 |
128
+ | 0.0005 | 76.0 | 152 | 0.4300 | 0.9375 |
129
+ | 0.0005 | 77.0 | 154 | 0.4318 | 0.9375 |
130
+ | 0.0005 | 78.0 | 156 | 0.4336 | 0.9375 |
131
+ | 0.0005 | 79.0 | 158 | 0.4353 | 0.9375 |
132
+ | 0.0003 | 80.0 | 160 | 0.4369 | 0.9375 |
133
+ | 0.0003 | 81.0 | 162 | 0.4384 | 0.9375 |
134
+ | 0.0003 | 82.0 | 164 | 0.4400 | 0.9375 |
135
+ | 0.0003 | 83.0 | 166 | 0.4414 | 0.9375 |
136
+ | 0.0003 | 84.0 | 168 | 0.4428 | 0.9375 |
137
+ | 0.0003 | 85.0 | 170 | 0.4441 | 0.9375 |
138
+ | 0.0003 | 86.0 | 172 | 0.4454 | 0.9375 |
139
+ | 0.0003 | 87.0 | 174 | 0.4466 | 0.9375 |
140
+ | 0.0003 | 88.0 | 176 | 0.4479 | 0.9375 |
141
+ | 0.0003 | 89.0 | 178 | 0.4491 | 0.9375 |
142
+ | 0.0002 | 90.0 | 180 | 0.4503 | 0.9375 |
143
+ | 0.0002 | 91.0 | 182 | 0.4515 | 0.9375 |
144
+ | 0.0002 | 92.0 | 184 | 0.4527 | 0.9375 |
145
+ | 0.0002 | 93.0 | 186 | 0.4540 | 0.9375 |
146
+ | 0.0002 | 94.0 | 188 | 0.4552 | 0.9375 |
147
+ | 0.0002 | 95.0 | 190 | 0.4565 | 0.9375 |
148
+ | 0.0002 | 96.0 | 192 | 0.4577 | 0.9375 |
149
+ | 0.0002 | 97.0 | 194 | 0.4592 | 0.9375 |
150
+ | 0.0002 | 98.0 | 196 | 0.4605 | 0.9375 |
151
+ | 0.0002 | 99.0 | 198 | 0.4619 | 0.9375 |
152
+ | 0.0002 | 100.0 | 200 | 0.4631 | 0.9375 |
153
+ | 0.0002 | 101.0 | 202 | 0.4645 | 0.9219 |
154
+ | 0.0002 | 102.0 | 204 | 0.4659 | 0.9219 |
155
+ | 0.0002 | 103.0 | 206 | 0.4671 | 0.9219 |
156
+ | 0.0002 | 104.0 | 208 | 0.4683 | 0.9219 |
157
+ | 0.0002 | 105.0 | 210 | 0.4696 | 0.9219 |
158
+ | 0.0002 | 106.0 | 212 | 0.4710 | 0.9219 |
159
+ | 0.0002 | 107.0 | 214 | 0.4723 | 0.9219 |
160
+ | 0.0002 | 108.0 | 216 | 0.4736 | 0.9219 |
161
+ | 0.0002 | 109.0 | 218 | 0.4748 | 0.9219 |
162
+ | 0.0002 | 110.0 | 220 | 0.4760 | 0.9219 |
163
+ | 0.0002 | 111.0 | 222 | 0.4773 | 0.9219 |
164
+ | 0.0002 | 112.0 | 224 | 0.4785 | 0.9219 |
165
+ | 0.0002 | 113.0 | 226 | 0.4798 | 0.9219 |
166
+ | 0.0002 | 114.0 | 228 | 0.4809 | 0.9219 |
167
+ | 0.0002 | 115.0 | 230 | 0.4820 | 0.9219 |
168
+ | 0.0002 | 116.0 | 232 | 0.4830 | 0.9219 |
169
+ | 0.0002 | 117.0 | 234 | 0.4839 | 0.9219 |
170
+ | 0.0002 | 118.0 | 236 | 0.4847 | 0.9219 |
171
+ | 0.0002 | 119.0 | 238 | 0.4853 | 0.9219 |
172
+ | 0.0001 | 120.0 | 240 | 0.4861 | 0.9219 |
173
+ | 0.0001 | 121.0 | 242 | 0.4868 | 0.9219 |
174
+ | 0.0001 | 122.0 | 244 | 0.4876 | 0.9219 |
175
+ | 0.0001 | 123.0 | 246 | 0.4883 | 0.9219 |
176
+ | 0.0001 | 124.0 | 248 | 0.4892 | 0.9219 |
177
+ | 0.0001 | 125.0 | 250 | 0.4901 | 0.9219 |
178
+ | 0.0001 | 126.0 | 252 | 0.4912 | 0.9219 |
179
+ | 0.0001 | 127.0 | 254 | 0.4921 | 0.9219 |
180
+ | 0.0001 | 128.0 | 256 | 0.4932 | 0.9219 |
181
+ | 0.0001 | 129.0 | 258 | 0.4944 | 0.9219 |
182
+ | 0.0001 | 130.0 | 260 | 0.4956 | 0.9219 |
183
+ | 0.0001 | 131.0 | 262 | 0.4967 | 0.9219 |
184
+ | 0.0001 | 132.0 | 264 | 0.4980 | 0.9219 |
185
+ | 0.0001 | 133.0 | 266 | 0.4993 | 0.9219 |
186
+ | 0.0001 | 134.0 | 268 | 0.5003 | 0.9219 |
187
+ | 0.0001 | 135.0 | 270 | 0.5014 | 0.9219 |
188
+ | 0.0001 | 136.0 | 272 | 0.5025 | 0.9219 |
189
+ | 0.0001 | 137.0 | 274 | 0.5034 | 0.9219 |
190
+ | 0.0001 | 138.0 | 276 | 0.5042 | 0.9219 |
191
+ | 0.0001 | 139.0 | 278 | 0.5048 | 0.9219 |
192
+ | 0.0001 | 140.0 | 280 | 0.5058 | 0.9219 |
193
+ | 0.0001 | 141.0 | 282 | 0.5064 | 0.9219 |
194
+ | 0.0001 | 142.0 | 284 | 0.5070 | 0.9219 |
195
+ | 0.0001 | 143.0 | 286 | 0.5079 | 0.9219 |
196
+ | 0.0001 | 144.0 | 288 | 0.5088 | 0.9219 |
197
+ | 0.0001 | 145.0 | 290 | 0.5096 | 0.9219 |
198
+ | 0.0001 | 146.0 | 292 | 0.5107 | 0.9219 |
199
+ | 0.0001 | 147.0 | 294 | 0.5118 | 0.9219 |
200
+ | 0.0001 | 148.0 | 296 | 0.5127 | 0.9219 |
201
+ | 0.0001 | 149.0 | 298 | 0.5136 | 0.9219 |
202
+ | 0.0001 | 150.0 | 300 | 0.5144 | 0.9219 |
203
 
204
 
205
  ### Framework versions