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best_model-yelp_polarity-16-42

Text Classification
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PyTorch
albert
Generated from Trainer
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1
  ---
2
- license: mit
3
- base_model: roberta-base
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  tags:
5
  - generated_from_trainer
6
  metrics:
@@ -15,10 +15,10 @@ should probably proofread and complete it, then remove this comment. -->
15
 
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  # best_model-yelp_polarity-16-42
17
 
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- This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
- - Loss: 0.2717
21
- - Accuracy: 0.9375
22
 
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  ## Model description
24
 
@@ -50,156 +50,156 @@ The following hyperparameters were used during training:
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
- | No log | 1.0 | 1 | 0.2161 | 0.9375 |
54
- | No log | 2.0 | 2 | 0.2155 | 0.9375 |
55
- | No log | 3.0 | 3 | 0.2142 | 0.9375 |
56
- | No log | 4.0 | 4 | 0.2123 | 0.9375 |
57
- | No log | 5.0 | 5 | 0.2097 | 0.9375 |
58
- | No log | 6.0 | 6 | 0.2064 | 0.9375 |
59
- | No log | 7.0 | 7 | 0.2032 | 0.9375 |
60
- | No log | 8.0 | 8 | 0.1996 | 0.9375 |
61
- | No log | 9.0 | 9 | 0.1958 | 0.9375 |
62
- | 0.4487 | 10.0 | 10 | 0.1921 | 0.9375 |
63
- | 0.4487 | 11.0 | 11 | 0.1882 | 0.9375 |
64
- | 0.4487 | 12.0 | 12 | 0.1845 | 0.9688 |
65
- | 0.4487 | 13.0 | 13 | 0.1812 | 0.9688 |
66
- | 0.4487 | 14.0 | 14 | 0.1785 | 0.9688 |
67
- | 0.4487 | 15.0 | 15 | 0.1765 | 0.9688 |
68
- | 0.4487 | 16.0 | 16 | 0.1754 | 0.9688 |
69
- | 0.4487 | 17.0 | 17 | 0.1752 | 0.9688 |
70
- | 0.4487 | 18.0 | 18 | 0.1758 | 0.9688 |
71
- | 0.4487 | 19.0 | 19 | 0.1772 | 0.9688 |
72
- | 0.4206 | 20.0 | 20 | 0.1797 | 0.9688 |
73
- | 0.4206 | 21.0 | 21 | 0.1836 | 0.9688 |
74
- | 0.4206 | 22.0 | 22 | 0.1891 | 0.9688 |
75
- | 0.4206 | 23.0 | 23 | 0.1962 | 0.9375 |
76
- | 0.4206 | 24.0 | 24 | 0.2053 | 0.9375 |
77
- | 0.4206 | 25.0 | 25 | 0.2167 | 0.9375 |
78
- | 0.4206 | 26.0 | 26 | 0.2280 | 0.9375 |
79
- | 0.4206 | 27.0 | 27 | 0.2410 | 0.9375 |
80
- | 0.4206 | 28.0 | 28 | 0.2550 | 0.9375 |
81
- | 0.4206 | 29.0 | 29 | 0.2667 | 0.9375 |
82
- | 0.214 | 30.0 | 30 | 0.2789 | 0.9375 |
83
- | 0.214 | 31.0 | 31 | 0.2895 | 0.9375 |
84
- | 0.214 | 32.0 | 32 | 0.2970 | 0.9375 |
85
- | 0.214 | 33.0 | 33 | 0.3027 | 0.9375 |
86
- | 0.214 | 34.0 | 34 | 0.3057 | 0.9375 |
87
- | 0.214 | 35.0 | 35 | 0.3061 | 0.9375 |
88
- | 0.214 | 36.0 | 36 | 0.3038 | 0.9375 |
89
- | 0.214 | 37.0 | 37 | 0.2990 | 0.9375 |
90
- | 0.214 | 38.0 | 38 | 0.2912 | 0.9375 |
91
- | 0.214 | 39.0 | 39 | 0.2808 | 0.9375 |
92
- | 0.1164 | 40.0 | 40 | 0.2672 | 0.9375 |
93
- | 0.1164 | 41.0 | 41 | 0.2505 | 0.9375 |
94
- | 0.1164 | 42.0 | 42 | 0.2319 | 0.9375 |
95
- | 0.1164 | 43.0 | 43 | 0.2172 | 0.9375 |
96
- | 0.1164 | 44.0 | 44 | 0.2071 | 0.9375 |
97
- | 0.1164 | 45.0 | 45 | 0.1979 | 0.9375 |
98
- | 0.1164 | 46.0 | 46 | 0.1928 | 0.9375 |
99
- | 0.1164 | 47.0 | 47 | 0.1884 | 0.9688 |
100
- | 0.1164 | 48.0 | 48 | 0.1868 | 0.9688 |
101
- | 0.1164 | 49.0 | 49 | 0.1871 | 0.9688 |
102
- | 0.032 | 50.0 | 50 | 0.1898 | 0.9375 |
103
- | 0.032 | 51.0 | 51 | 0.1953 | 0.9375 |
104
- | 0.032 | 52.0 | 52 | 0.2052 | 0.9375 |
105
- | 0.032 | 53.0 | 53 | 0.2205 | 0.9375 |
106
- | 0.032 | 54.0 | 54 | 0.2332 | 0.9375 |
107
- | 0.032 | 55.0 | 55 | 0.2420 | 0.9375 |
108
- | 0.032 | 56.0 | 56 | 0.2458 | 0.9375 |
109
- | 0.032 | 57.0 | 57 | 0.2462 | 0.9375 |
110
- | 0.032 | 58.0 | 58 | 0.2419 | 0.9375 |
111
- | 0.032 | 59.0 | 59 | 0.2325 | 0.9375 |
112
- | 0.0168 | 60.0 | 60 | 0.2284 | 0.9375 |
113
- | 0.0168 | 61.0 | 61 | 0.2304 | 0.9375 |
114
- | 0.0168 | 62.0 | 62 | 0.2372 | 0.9375 |
115
- | 0.0168 | 63.0 | 63 | 0.2469 | 0.9375 |
116
- | 0.0168 | 64.0 | 64 | 0.2518 | 0.9375 |
117
- | 0.0168 | 65.0 | 65 | 0.2557 | 0.9375 |
118
- | 0.0168 | 66.0 | 66 | 0.2580 | 0.9375 |
119
- | 0.0168 | 67.0 | 67 | 0.2535 | 0.9375 |
120
- | 0.0168 | 68.0 | 68 | 0.2500 | 0.9375 |
121
- | 0.0168 | 69.0 | 69 | 0.2480 | 0.9375 |
122
- | 0.0063 | 70.0 | 70 | 0.2459 | 0.9375 |
123
- | 0.0063 | 71.0 | 71 | 0.2437 | 0.9375 |
124
- | 0.0063 | 72.0 | 72 | 0.2393 | 0.9375 |
125
- | 0.0063 | 73.0 | 73 | 0.2342 | 0.9375 |
126
- | 0.0063 | 74.0 | 74 | 0.2297 | 0.9375 |
127
- | 0.0063 | 75.0 | 75 | 0.2264 | 0.9375 |
128
- | 0.0063 | 76.0 | 76 | 0.2254 | 0.9375 |
129
- | 0.0063 | 77.0 | 77 | 0.2250 | 0.9375 |
130
- | 0.0063 | 78.0 | 78 | 0.2243 | 0.9375 |
131
- | 0.0063 | 79.0 | 79 | 0.2238 | 0.9375 |
132
- | 0.0041 | 80.0 | 80 | 0.2226 | 0.9375 |
133
- | 0.0041 | 81.0 | 81 | 0.2221 | 0.9375 |
134
- | 0.0041 | 82.0 | 82 | 0.2227 | 0.9375 |
135
- | 0.0041 | 83.0 | 83 | 0.2234 | 0.9375 |
136
- | 0.0041 | 84.0 | 84 | 0.2244 | 0.9375 |
137
- | 0.0041 | 85.0 | 85 | 0.2257 | 0.9375 |
138
- | 0.0041 | 86.0 | 86 | 0.2263 | 0.9375 |
139
- | 0.0041 | 87.0 | 87 | 0.2270 | 0.9375 |
140
- | 0.0041 | 88.0 | 88 | 0.2279 | 0.9375 |
141
- | 0.0041 | 89.0 | 89 | 0.2288 | 0.9375 |
142
- | 0.0028 | 90.0 | 90 | 0.2297 | 0.9375 |
143
- | 0.0028 | 91.0 | 91 | 0.2313 | 0.9375 |
144
- | 0.0028 | 92.0 | 92 | 0.2333 | 0.9375 |
145
- | 0.0028 | 93.0 | 93 | 0.2355 | 0.9375 |
146
- | 0.0028 | 94.0 | 94 | 0.2377 | 0.9375 |
147
- | 0.0028 | 95.0 | 95 | 0.2406 | 0.9375 |
148
- | 0.0028 | 96.0 | 96 | 0.2436 | 0.9375 |
149
- | 0.0028 | 97.0 | 97 | 0.2486 | 0.9375 |
150
- | 0.0028 | 98.0 | 98 | 0.2527 | 0.9375 |
151
- | 0.0028 | 99.0 | 99 | 0.2570 | 0.9375 |
152
- | 0.0022 | 100.0 | 100 | 0.2612 | 0.9375 |
153
- | 0.0022 | 101.0 | 101 | 0.2649 | 0.9375 |
154
- | 0.0022 | 102.0 | 102 | 0.2684 | 0.9375 |
155
- | 0.0022 | 103.0 | 103 | 0.2722 | 0.9375 |
156
- | 0.0022 | 104.0 | 104 | 0.2756 | 0.9375 |
157
- | 0.0022 | 105.0 | 105 | 0.2787 | 0.9375 |
158
- | 0.0022 | 106.0 | 106 | 0.2775 | 0.9375 |
159
- | 0.0022 | 107.0 | 107 | 0.2748 | 0.9375 |
160
- | 0.0022 | 108.0 | 108 | 0.2719 | 0.9375 |
161
- | 0.0022 | 109.0 | 109 | 0.2684 | 0.9375 |
162
- | 0.0018 | 110.0 | 110 | 0.2651 | 0.9375 |
163
- | 0.0018 | 111.0 | 111 | 0.2619 | 0.9375 |
164
- | 0.0018 | 112.0 | 112 | 0.2589 | 0.9375 |
165
- | 0.0018 | 113.0 | 113 | 0.2561 | 0.9375 |
166
- | 0.0018 | 114.0 | 114 | 0.2537 | 0.9375 |
167
- | 0.0018 | 115.0 | 115 | 0.2517 | 0.9375 |
168
- | 0.0018 | 116.0 | 116 | 0.2499 | 0.9375 |
169
- | 0.0018 | 117.0 | 117 | 0.2491 | 0.9375 |
170
- | 0.0018 | 118.0 | 118 | 0.2489 | 0.9375 |
171
- | 0.0018 | 119.0 | 119 | 0.2487 | 0.9375 |
172
- | 0.0015 | 120.0 | 120 | 0.2483 | 0.9375 |
173
- | 0.0015 | 121.0 | 121 | 0.2483 | 0.9375 |
174
- | 0.0015 | 122.0 | 122 | 0.2483 | 0.9375 |
175
- | 0.0015 | 123.0 | 123 | 0.2485 | 0.9375 |
176
- | 0.0015 | 124.0 | 124 | 0.2488 | 0.9375 |
177
- | 0.0015 | 125.0 | 125 | 0.2490 | 0.9375 |
178
- | 0.0015 | 126.0 | 126 | 0.2493 | 0.9375 |
179
- | 0.0015 | 127.0 | 127 | 0.2497 | 0.9375 |
180
- | 0.0015 | 128.0 | 128 | 0.2508 | 0.9375 |
181
- | 0.0015 | 129.0 | 129 | 0.2507 | 0.9375 |
182
- | 0.0013 | 130.0 | 130 | 0.2507 | 0.9375 |
183
- | 0.0013 | 131.0 | 131 | 0.2511 | 0.9375 |
184
- | 0.0013 | 132.0 | 132 | 0.2510 | 0.9375 |
185
- | 0.0013 | 133.0 | 133 | 0.2514 | 0.9375 |
186
- | 0.0013 | 134.0 | 134 | 0.2520 | 0.9375 |
187
- | 0.0013 | 135.0 | 135 | 0.2527 | 0.9375 |
188
- | 0.0013 | 136.0 | 136 | 0.2537 | 0.9375 |
189
- | 0.0013 | 137.0 | 137 | 0.2547 | 0.9375 |
190
- | 0.0013 | 138.0 | 138 | 0.2556 | 0.9375 |
191
- | 0.0013 | 139.0 | 139 | 0.2564 | 0.9375 |
192
- | 0.0011 | 140.0 | 140 | 0.2574 | 0.9375 |
193
- | 0.0011 | 141.0 | 141 | 0.2587 | 0.9375 |
194
- | 0.0011 | 142.0 | 142 | 0.2600 | 0.9375 |
195
- | 0.0011 | 143.0 | 143 | 0.2614 | 0.9375 |
196
- | 0.0011 | 144.0 | 144 | 0.2626 | 0.9375 |
197
- | 0.0011 | 145.0 | 145 | 0.2638 | 0.9375 |
198
- | 0.0011 | 146.0 | 146 | 0.2654 | 0.9375 |
199
- | 0.0011 | 147.0 | 147 | 0.2674 | 0.9375 |
200
- | 0.0011 | 148.0 | 148 | 0.2692 | 0.9375 |
201
- | 0.0011 | 149.0 | 149 | 0.2706 | 0.9375 |
202
- | 0.001 | 150.0 | 150 | 0.2717 | 0.9375 |
203
 
204
 
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-16-42
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: 0.6276
21
+ - Accuracy: 0.8125
22
 
23
  ## Model description
24
 
 
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
+ | No log | 1.0 | 1 | 0.7035 | 0.8438 |
54
+ | No log | 2.0 | 2 | 0.7063 | 0.8438 |
55
+ | No log | 3.0 | 3 | 0.7119 | 0.8438 |
56
+ | No log | 4.0 | 4 | 0.7198 | 0.8438 |
57
+ | No log | 5.0 | 5 | 0.7298 | 0.8125 |
58
+ | No log | 6.0 | 6 | 0.7415 | 0.8125 |
59
+ | No log | 7.0 | 7 | 0.7542 | 0.8125 |
60
+ | No log | 8.0 | 8 | 0.7678 | 0.8125 |
61
+ | No log | 9.0 | 9 | 0.7815 | 0.8125 |
62
+ | 0.4002 | 10.0 | 10 | 0.7947 | 0.8125 |
63
+ | 0.4002 | 11.0 | 11 | 0.8066 | 0.8125 |
64
+ | 0.4002 | 12.0 | 12 | 0.8164 | 0.8125 |
65
+ | 0.4002 | 13.0 | 13 | 0.8228 | 0.8125 |
66
+ | 0.4002 | 14.0 | 14 | 0.8259 | 0.8125 |
67
+ | 0.4002 | 15.0 | 15 | 0.8260 | 0.8125 |
68
+ | 0.4002 | 16.0 | 16 | 0.8231 | 0.8125 |
69
+ | 0.4002 | 17.0 | 17 | 0.8172 | 0.8125 |
70
+ | 0.4002 | 18.0 | 18 | 0.8084 | 0.8125 |
71
+ | 0.4002 | 19.0 | 19 | 0.7968 | 0.8125 |
72
+ | 0.3498 | 20.0 | 20 | 0.7826 | 0.8125 |
73
+ | 0.3498 | 21.0 | 21 | 0.7660 | 0.8125 |
74
+ | 0.3498 | 22.0 | 22 | 0.7474 | 0.8438 |
75
+ | 0.3498 | 23.0 | 23 | 0.7272 | 0.8438 |
76
+ | 0.3498 | 24.0 | 24 | 0.7053 | 0.8438 |
77
+ | 0.3498 | 25.0 | 25 | 0.6813 | 0.8438 |
78
+ | 0.3498 | 26.0 | 26 | 0.6547 | 0.8438 |
79
+ | 0.3498 | 27.0 | 27 | 0.6255 | 0.8438 |
80
+ | 0.3498 | 28.0 | 28 | 0.5952 | 0.8438 |
81
+ | 0.3498 | 29.0 | 29 | 0.5656 | 0.8125 |
82
+ | 0.2773 | 30.0 | 30 | 0.5407 | 0.8125 |
83
+ | 0.2773 | 31.0 | 31 | 0.5221 | 0.8125 |
84
+ | 0.2773 | 32.0 | 32 | 0.5096 | 0.8125 |
85
+ | 0.2773 | 33.0 | 33 | 0.5026 | 0.8125 |
86
+ | 0.2773 | 34.0 | 34 | 0.5080 | 0.8125 |
87
+ | 0.2773 | 35.0 | 35 | 0.5248 | 0.8125 |
88
+ | 0.2773 | 36.0 | 36 | 0.5517 | 0.8125 |
89
+ | 0.2773 | 37.0 | 37 | 0.5838 | 0.8125 |
90
+ | 0.2773 | 38.0 | 38 | 0.6122 | 0.8125 |
91
+ | 0.2773 | 39.0 | 39 | 0.6332 | 0.8125 |
92
+ | 0.1446 | 40.0 | 40 | 0.6455 | 0.8125 |
93
+ | 0.1446 | 41.0 | 41 | 0.6491 | 0.8125 |
94
+ | 0.1446 | 42.0 | 42 | 0.6449 | 0.8125 |
95
+ | 0.1446 | 43.0 | 43 | 0.6330 | 0.8125 |
96
+ | 0.1446 | 44.0 | 44 | 0.6121 | 0.8125 |
97
+ | 0.1446 | 45.0 | 45 | 0.5814 | 0.8125 |
98
+ | 0.1446 | 46.0 | 46 | 0.5390 | 0.8125 |
99
+ | 0.1446 | 47.0 | 47 | 0.4913 | 0.8125 |
100
+ | 0.1446 | 48.0 | 48 | 0.4598 | 0.8125 |
101
+ | 0.1446 | 49.0 | 49 | 0.4469 | 0.8438 |
102
+ | 0.066 | 50.0 | 50 | 0.4535 | 0.8438 |
103
+ | 0.066 | 51.0 | 51 | 0.4775 | 0.8125 |
104
+ | 0.066 | 52.0 | 52 | 0.5153 | 0.8125 |
105
+ | 0.066 | 53.0 | 53 | 0.5618 | 0.8125 |
106
+ | 0.066 | 54.0 | 54 | 0.6090 | 0.8125 |
107
+ | 0.066 | 55.0 | 55 | 0.6490 | 0.8125 |
108
+ | 0.066 | 56.0 | 56 | 0.6785 | 0.8125 |
109
+ | 0.066 | 57.0 | 57 | 0.6962 | 0.8125 |
110
+ | 0.066 | 58.0 | 58 | 0.7045 | 0.8125 |
111
+ | 0.066 | 59.0 | 59 | 0.7056 | 0.8125 |
112
+ | 0.0171 | 60.0 | 60 | 0.7001 | 0.8125 |
113
+ | 0.0171 | 61.0 | 61 | 0.6878 | 0.8125 |
114
+ | 0.0171 | 62.0 | 62 | 0.6688 | 0.8125 |
115
+ | 0.0171 | 63.0 | 63 | 0.6427 | 0.8125 |
116
+ | 0.0171 | 64.0 | 64 | 0.6110 | 0.8125 |
117
+ | 0.0171 | 65.0 | 65 | 0.5764 | 0.8125 |
118
+ | 0.0171 | 66.0 | 66 | 0.5422 | 0.8125 |
119
+ | 0.0171 | 67.0 | 67 | 0.5147 | 0.8125 |
120
+ | 0.0171 | 68.0 | 68 | 0.4976 | 0.8125 |
121
+ | 0.0171 | 69.0 | 69 | 0.4883 | 0.8125 |
122
+ | 0.0058 | 70.0 | 70 | 0.4876 | 0.8438 |
123
+ | 0.0058 | 71.0 | 71 | 0.4932 | 0.8438 |
124
+ | 0.0058 | 72.0 | 72 | 0.5018 | 0.8438 |
125
+ | 0.0058 | 73.0 | 73 | 0.5127 | 0.8125 |
126
+ | 0.0058 | 74.0 | 74 | 0.5251 | 0.8125 |
127
+ | 0.0058 | 75.0 | 75 | 0.5385 | 0.8125 |
128
+ | 0.0058 | 76.0 | 76 | 0.5517 | 0.8125 |
129
+ | 0.0058 | 77.0 | 77 | 0.5644 | 0.8125 |
130
+ | 0.0058 | 78.0 | 78 | 0.5758 | 0.8125 |
131
+ | 0.0058 | 79.0 | 79 | 0.5858 | 0.8125 |
132
+ | 0.0037 | 80.0 | 80 | 0.5941 | 0.8125 |
133
+ | 0.0037 | 81.0 | 81 | 0.6009 | 0.8125 |
134
+ | 0.0037 | 82.0 | 82 | 0.6064 | 0.8125 |
135
+ | 0.0037 | 83.0 | 83 | 0.6102 | 0.8125 |
136
+ | 0.0037 | 84.0 | 84 | 0.6119 | 0.8125 |
137
+ | 0.0037 | 85.0 | 85 | 0.6123 | 0.8125 |
138
+ | 0.0037 | 86.0 | 86 | 0.6108 | 0.8125 |
139
+ | 0.0037 | 87.0 | 87 | 0.6081 | 0.8125 |
140
+ | 0.0037 | 88.0 | 88 | 0.6040 | 0.8125 |
141
+ | 0.0037 | 89.0 | 89 | 0.5987 | 0.8125 |
142
+ | 0.0028 | 90.0 | 90 | 0.5923 | 0.8125 |
143
+ | 0.0028 | 91.0 | 91 | 0.5853 | 0.8125 |
144
+ | 0.0028 | 92.0 | 92 | 0.5779 | 0.8125 |
145
+ | 0.0028 | 93.0 | 93 | 0.5703 | 0.8125 |
146
+ | 0.0028 | 94.0 | 94 | 0.5627 | 0.8125 |
147
+ | 0.0028 | 95.0 | 95 | 0.5552 | 0.8125 |
148
+ | 0.0028 | 96.0 | 96 | 0.5481 | 0.8438 |
149
+ | 0.0028 | 97.0 | 97 | 0.5417 | 0.8438 |
150
+ | 0.0028 | 98.0 | 98 | 0.5365 | 0.8438 |
151
+ | 0.0028 | 99.0 | 99 | 0.5318 | 0.8438 |
152
+ | 0.0023 | 100.0 | 100 | 0.5280 | 0.8438 |
153
+ | 0.0023 | 101.0 | 101 | 0.5249 | 0.8438 |
154
+ | 0.0023 | 102.0 | 102 | 0.5220 | 0.8438 |
155
+ | 0.0023 | 103.0 | 103 | 0.5198 | 0.8438 |
156
+ | 0.0023 | 104.0 | 104 | 0.5180 | 0.8438 |
157
+ | 0.0023 | 105.0 | 105 | 0.5169 | 0.8438 |
158
+ | 0.0023 | 106.0 | 106 | 0.5167 | 0.8438 |
159
+ | 0.0023 | 107.0 | 107 | 0.5172 | 0.8438 |
160
+ | 0.0023 | 108.0 | 108 | 0.5184 | 0.8438 |
161
+ | 0.0023 | 109.0 | 109 | 0.5203 | 0.8438 |
162
+ | 0.0019 | 110.0 | 110 | 0.5224 | 0.8438 |
163
+ | 0.0019 | 111.0 | 111 | 0.5249 | 0.8438 |
164
+ | 0.0019 | 112.0 | 112 | 0.5278 | 0.8438 |
165
+ | 0.0019 | 113.0 | 113 | 0.5309 | 0.8438 |
166
+ | 0.0019 | 114.0 | 114 | 0.5343 | 0.8438 |
167
+ | 0.0019 | 115.0 | 115 | 0.5381 | 0.8438 |
168
+ | 0.0019 | 116.0 | 116 | 0.5422 | 0.8438 |
169
+ | 0.0019 | 117.0 | 117 | 0.5467 | 0.8438 |
170
+ | 0.0019 | 118.0 | 118 | 0.5514 | 0.8125 |
171
+ | 0.0019 | 119.0 | 119 | 0.5561 | 0.8125 |
172
+ | 0.0016 | 120.0 | 120 | 0.5609 | 0.8125 |
173
+ | 0.0016 | 121.0 | 121 | 0.5655 | 0.8125 |
174
+ | 0.0016 | 122.0 | 122 | 0.5703 | 0.8125 |
175
+ | 0.0016 | 123.0 | 123 | 0.5750 | 0.8125 |
176
+ | 0.0016 | 124.0 | 124 | 0.5796 | 0.8125 |
177
+ | 0.0016 | 125.0 | 125 | 0.5838 | 0.8125 |
178
+ | 0.0016 | 126.0 | 126 | 0.5877 | 0.8125 |
179
+ | 0.0016 | 127.0 | 127 | 0.5915 | 0.8125 |
180
+ | 0.0016 | 128.0 | 128 | 0.5950 | 0.8125 |
181
+ | 0.0016 | 129.0 | 129 | 0.5978 | 0.8125 |
182
+ | 0.0013 | 130.0 | 130 | 0.6002 | 0.8125 |
183
+ | 0.0013 | 131.0 | 131 | 0.6024 | 0.8125 |
184
+ | 0.0013 | 132.0 | 132 | 0.6045 | 0.8125 |
185
+ | 0.0013 | 133.0 | 133 | 0.6065 | 0.8125 |
186
+ | 0.0013 | 134.0 | 134 | 0.6082 | 0.8125 |
187
+ | 0.0013 | 135.0 | 135 | 0.6097 | 0.8125 |
188
+ | 0.0013 | 136.0 | 136 | 0.6113 | 0.8125 |
189
+ | 0.0013 | 137.0 | 137 | 0.6125 | 0.8125 |
190
+ | 0.0013 | 138.0 | 138 | 0.6136 | 0.8125 |
191
+ | 0.0013 | 139.0 | 139 | 0.6148 | 0.8125 |
192
+ | 0.0012 | 140.0 | 140 | 0.6158 | 0.8125 |
193
+ | 0.0012 | 141.0 | 141 | 0.6165 | 0.8125 |
194
+ | 0.0012 | 142.0 | 142 | 0.6172 | 0.8125 |
195
+ | 0.0012 | 143.0 | 143 | 0.6180 | 0.8125 |
196
+ | 0.0012 | 144.0 | 144 | 0.6190 | 0.8125 |
197
+ | 0.0012 | 145.0 | 145 | 0.6201 | 0.8125 |
198
+ | 0.0012 | 146.0 | 146 | 0.6215 | 0.8125 |
199
+ | 0.0012 | 147.0 | 147 | 0.6227 | 0.8125 |
200
+ | 0.0012 | 148.0 | 148 | 0.6239 | 0.8125 |
201
+ | 0.0012 | 149.0 | 149 | 0.6256 | 0.8125 |
202
+ | 0.001 | 150.0 | 150 | 0.6276 | 0.8125 |
203
 
204
 
205
  ### Framework versions