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End of training

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@@ -1,5 +1,6 @@
1
  ---
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  library_name: transformers
 
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  tags:
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  - generated_from_trainer
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  model-index:
@@ -12,9 +13,9 @@ should probably proofread and complete it, then remove this comment. -->
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13
  # calculator_model_test
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15
- This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
16
  It achieves the following results on the evaluation set:
17
- - Loss: 0.0224
18
 
19
  ## Model description
20
 
@@ -39,72 +40,262 @@ The following hyperparameters were used during training:
39
  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
41
  - lr_scheduler_type: linear
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- - num_epochs: 60
43
 
44
  ### Training results
45
 
46
  | Training Loss | Epoch | Step | Validation Loss |
47
  |:-------------:|:-----:|:----:|:---------------:|
48
- | 3.0161 | 1.0 | 6 | 2.2472 |
49
- | 2.0186 | 2.0 | 12 | 1.7397 |
50
- | 1.5559 | 3.0 | 18 | 1.2836 |
51
- | 1.1826 | 4.0 | 24 | 1.0590 |
52
- | 0.9670 | 5.0 | 30 | 0.8865 |
53
- | 0.8522 | 6.0 | 36 | 0.7952 |
54
- | 0.7639 | 7.0 | 42 | 0.7135 |
55
- | 0.6939 | 8.0 | 48 | 0.6666 |
56
- | 0.6353 | 9.0 | 54 | 0.5909 |
57
- | 0.5981 | 10.0 | 60 | 0.5718 |
58
- | 0.5521 | 11.0 | 66 | 0.5305 |
59
- | 0.5135 | 12.0 | 72 | 0.5070 |
60
- | 0.4958 | 13.0 | 78 | 0.4900 |
61
- | 0.4901 | 14.0 | 84 | 0.4483 |
62
- | 0.4499 | 15.0 | 90 | 0.4499 |
63
- | 0.4542 | 16.0 | 96 | 0.4253 |
64
- | 0.4176 | 17.0 | 102 | 0.3849 |
65
- | 0.4059 | 18.0 | 108 | 0.3846 |
66
- | 0.3776 | 19.0 | 114 | 0.3226 |
67
- | 0.3352 | 20.0 | 120 | 0.2924 |
68
- | 0.3041 | 21.0 | 126 | 0.2671 |
69
- | 0.2804 | 22.0 | 132 | 0.2691 |
70
- | 0.2755 | 23.0 | 138 | 0.2339 |
71
- | 0.2347 | 24.0 | 144 | 0.2144 |
72
- | 0.2286 | 25.0 | 150 | 0.1885 |
73
- | 0.1967 | 26.0 | 156 | 0.1548 |
74
- | 0.1710 | 27.0 | 162 | 0.1397 |
75
- | 0.1602 | 28.0 | 168 | 0.1088 |
76
- | 0.1384 | 29.0 | 174 | 0.0990 |
77
- | 0.1198 | 30.0 | 180 | 0.0836 |
78
- | 0.1137 | 31.0 | 186 | 0.0816 |
79
- | 0.1125 | 32.0 | 192 | 0.0710 |
80
- | 0.0997 | 33.0 | 198 | 0.0855 |
81
- | 0.1075 | 34.0 | 204 | 0.0805 |
82
- | 0.1018 | 35.0 | 210 | 0.0636 |
83
- | 0.0866 | 36.0 | 216 | 0.0635 |
84
- | 0.0827 | 37.0 | 222 | 0.0588 |
85
- | 0.0754 | 38.0 | 228 | 0.0536 |
86
- | 0.0735 | 39.0 | 234 | 0.0463 |
87
- | 0.0711 | 40.0 | 240 | 0.0508 |
88
- | 0.0673 | 41.0 | 246 | 0.0404 |
89
- | 0.0649 | 42.0 | 252 | 0.0387 |
90
- | 0.0591 | 43.0 | 258 | 0.0358 |
91
- | 0.0632 | 44.0 | 264 | 0.0344 |
92
- | 0.0575 | 45.0 | 270 | 0.0355 |
93
- | 0.0550 | 46.0 | 276 | 0.0343 |
94
- | 0.0483 | 47.0 | 282 | 0.0323 |
95
- | 0.0480 | 48.0 | 288 | 0.0294 |
96
- | 0.0464 | 49.0 | 294 | 0.0297 |
97
- | 0.0438 | 50.0 | 300 | 0.0269 |
98
- | 0.0393 | 51.0 | 306 | 0.0272 |
99
- | 0.0442 | 52.0 | 312 | 0.0248 |
100
- | 0.0387 | 53.0 | 318 | 0.0249 |
101
- | 0.0381 | 54.0 | 324 | 0.0244 |
102
- | 0.0400 | 55.0 | 330 | 0.0236 |
103
- | 0.0376 | 56.0 | 336 | 0.0234 |
104
- | 0.0375 | 57.0 | 342 | 0.0230 |
105
- | 0.0336 | 58.0 | 348 | 0.0225 |
106
- | 0.0362 | 59.0 | 354 | 0.0222 |
107
- | 0.0332 | 60.0 | 360 | 0.0224 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
108
 
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  ### Framework versions
 
1
  ---
2
  library_name: transformers
3
+ base_model: ReadHegel/calculator_model_test
4
  tags:
5
  - generated_from_trainer
6
  model-index:
 
13
 
14
  # calculator_model_test
15
 
16
+ This model is a fine-tuned version of [ReadHegel/calculator_model_test](https://huggingface.co/ReadHegel/calculator_model_test) on the None dataset.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 0.0006
19
 
20
  ## Model description
21
 
 
40
  - seed: 42
41
  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
42
  - lr_scheduler_type: linear
43
+ - num_epochs: 250
44
 
45
  ### Training results
46
 
47
  | Training Loss | Epoch | Step | Validation Loss |
48
  |:-------------:|:-----:|:----:|:---------------:|
49
+ | 0.6830 | 1.0 | 6 | 0.3474 |
50
+ | 0.2905 | 2.0 | 12 | 0.1705 |
51
+ | 0.1633 | 3.0 | 18 | 0.1282 |
52
+ | 0.1346 | 4.0 | 24 | 0.1065 |
53
+ | 0.1074 | 5.0 | 30 | 0.0834 |
54
+ | 0.1010 | 6.0 | 36 | 0.0615 |
55
+ | 0.1016 | 7.0 | 42 | 0.0634 |
56
+ | 0.0770 | 8.0 | 48 | 0.0620 |
57
+ | 0.0729 | 9.0 | 54 | 0.0903 |
58
+ | 0.0853 | 10.0 | 60 | 0.0888 |
59
+ | 0.1005 | 11.0 | 66 | 0.1555 |
60
+ | 0.1295 | 12.0 | 72 | 0.1269 |
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+ | 0.1342 | 13.0 | 78 | 0.0783 |
62
+ | 0.0937 | 14.0 | 84 | 0.0535 |
63
+ | 0.0596 | 15.0 | 90 | 0.0485 |
64
+ | 0.0538 | 16.0 | 96 | 0.0271 |
65
+ | 0.0479 | 17.0 | 102 | 0.0285 |
66
+ | 0.0511 | 18.0 | 108 | 0.0340 |
67
+ | 0.0576 | 19.0 | 114 | 0.0345 |
68
+ | 0.0502 | 20.0 | 120 | 0.0395 |
69
+ | 0.0462 | 21.0 | 126 | 0.0349 |
70
+ | 0.0474 | 22.0 | 132 | 0.0225 |
71
+ | 0.0341 | 23.0 | 138 | 0.0262 |
72
+ | 0.0328 | 24.0 | 144 | 0.0279 |
73
+ | 0.0378 | 25.0 | 150 | 0.0220 |
74
+ | 0.0421 | 26.0 | 156 | 0.0329 |
75
+ | 0.0383 | 27.0 | 162 | 0.0240 |
76
+ | 0.0371 | 28.0 | 168 | 0.0276 |
77
+ | 0.0297 | 29.0 | 174 | 0.0191 |
78
+ | 0.0283 | 30.0 | 180 | 0.0153 |
79
+ | 0.0350 | 31.0 | 186 | 0.0285 |
80
+ | 0.0348 | 32.0 | 192 | 0.0288 |
81
+ | 0.0389 | 33.0 | 198 | 0.0555 |
82
+ | 0.0441 | 34.0 | 204 | 0.0325 |
83
+ | 0.0454 | 35.0 | 210 | 0.0309 |
84
+ | 0.0403 | 36.0 | 216 | 0.0220 |
85
+ | 0.0374 | 37.0 | 222 | 0.0230 |
86
+ | 0.0395 | 38.0 | 228 | 0.0336 |
87
+ | 0.0417 | 39.0 | 234 | 0.0222 |
88
+ | 0.0382 | 40.0 | 240 | 0.0365 |
89
+ | 0.0341 | 41.0 | 246 | 0.0172 |
90
+ | 0.0323 | 42.0 | 252 | 0.0187 |
91
+ | 0.0364 | 43.0 | 258 | 0.0184 |
92
+ | 0.0388 | 44.0 | 264 | 0.0177 |
93
+ | 0.0324 | 45.0 | 270 | 0.0125 |
94
+ | 0.0270 | 46.0 | 276 | 0.0138 |
95
+ | 0.0270 | 47.0 | 282 | 0.0169 |
96
+ | 0.0255 | 48.0 | 288 | 0.0147 |
97
+ | 0.0286 | 49.0 | 294 | 0.0207 |
98
+ | 0.0255 | 50.0 | 300 | 0.0156 |
99
+ | 0.0176 | 51.0 | 306 | 0.0191 |
100
+ | 0.0223 | 52.0 | 312 | 0.0166 |
101
+ | 0.0174 | 53.0 | 318 | 0.0087 |
102
+ | 0.0130 | 54.0 | 324 | 0.0078 |
103
+ | 0.0116 | 55.0 | 330 | 0.0070 |
104
+ | 0.0097 | 56.0 | 336 | 0.0054 |
105
+ | 0.0098 | 57.0 | 342 | 0.0048 |
106
+ | 0.0074 | 58.0 | 348 | 0.0063 |
107
+ | 0.0081 | 59.0 | 354 | 0.0044 |
108
+ | 0.0070 | 60.0 | 360 | 0.0042 |
109
+ | 0.0068 | 61.0 | 366 | 0.0048 |
110
+ | 0.0065 | 62.0 | 372 | 0.0060 |
111
+ | 0.0058 | 63.0 | 378 | 0.0037 |
112
+ | 0.0058 | 64.0 | 384 | 0.0036 |
113
+ | 0.0049 | 65.0 | 390 | 0.0031 |
114
+ | 0.0048 | 66.0 | 396 | 0.0034 |
115
+ | 0.0041 | 67.0 | 402 | 0.0032 |
116
+ | 0.0041 | 68.0 | 408 | 0.0027 |
117
+ | 0.0042 | 69.0 | 414 | 0.0029 |
118
+ | 0.0036 | 70.0 | 420 | 0.0022 |
119
+ | 0.0039 | 71.0 | 426 | 0.0018 |
120
+ | 0.0032 | 72.0 | 432 | 0.0031 |
121
+ | 0.0039 | 73.0 | 438 | 0.0023 |
122
+ | 0.0050 | 74.0 | 444 | 0.0029 |
123
+ | 0.0091 | 75.0 | 450 | 0.0039 |
124
+ | 0.0069 | 76.0 | 456 | 0.0032 |
125
+ | 0.0080 | 77.0 | 462 | 0.0049 |
126
+ | 0.0103 | 78.0 | 468 | 0.0066 |
127
+ | 0.0109 | 79.0 | 474 | 0.0098 |
128
+ | 0.0082 | 80.0 | 480 | 0.0057 |
129
+ | 0.0110 | 81.0 | 486 | 0.0051 |
130
+ | 0.0109 | 82.0 | 492 | 0.0088 |
131
+ | 0.0175 | 83.0 | 498 | 0.0069 |
132
+ | 0.0161 | 84.0 | 504 | 0.0120 |
133
+ | 0.0178 | 85.0 | 510 | 0.0073 |
134
+ | 0.0188 | 86.0 | 516 | 0.0124 |
135
+ | 0.0138 | 87.0 | 522 | 0.0041 |
136
+ | 0.0079 | 88.0 | 528 | 0.0047 |
137
+ | 0.0089 | 89.0 | 534 | 0.0052 |
138
+ | 0.0133 | 90.0 | 540 | 0.0319 |
139
+ | 0.0211 | 91.0 | 546 | 0.0221 |
140
+ | 0.0208 | 92.0 | 552 | 0.0059 |
141
+ | 0.0126 | 93.0 | 558 | 0.0101 |
142
+ | 0.0136 | 94.0 | 564 | 0.0087 |
143
+ | 0.0103 | 95.0 | 570 | 0.0056 |
144
+ | 0.0106 | 96.0 | 576 | 0.0053 |
145
+ | 0.0145 | 97.0 | 582 | 0.0080 |
146
+ | 0.0125 | 98.0 | 588 | 0.0057 |
147
+ | 0.0119 | 99.0 | 594 | 0.0066 |
148
+ | 0.0087 | 100.0 | 600 | 0.0062 |
149
+ | 0.0088 | 101.0 | 606 | 0.0059 |
150
+ | 0.0086 | 102.0 | 612 | 0.0045 |
151
+ | 0.0069 | 103.0 | 618 | 0.0042 |
152
+ | 0.0098 | 104.0 | 624 | 0.0052 |
153
+ | 0.0075 | 105.0 | 630 | 0.0039 |
154
+ | 0.0071 | 106.0 | 636 | 0.0033 |
155
+ | 0.0107 | 107.0 | 642 | 0.0051 |
156
+ | 0.0074 | 108.0 | 648 | 0.0054 |
157
+ | 0.0077 | 109.0 | 654 | 0.0047 |
158
+ | 0.0120 | 110.0 | 660 | 0.0103 |
159
+ | 0.0087 | 111.0 | 666 | 0.0031 |
160
+ | 0.0076 | 112.0 | 672 | 0.0046 |
161
+ | 0.0061 | 113.0 | 678 | 0.0049 |
162
+ | 0.0056 | 114.0 | 684 | 0.0031 |
163
+ | 0.0046 | 115.0 | 690 | 0.0025 |
164
+ | 0.0044 | 116.0 | 696 | 0.0021 |
165
+ | 0.0026 | 117.0 | 702 | 0.0019 |
166
+ | 0.0032 | 118.0 | 708 | 0.0017 |
167
+ | 0.0062 | 119.0 | 714 | 0.0087 |
168
+ | 0.0172 | 120.0 | 720 | 0.0040 |
169
+ | 0.0089 | 121.0 | 726 | 0.0058 |
170
+ | 0.0091 | 122.0 | 732 | 0.0053 |
171
+ | 0.0150 | 123.0 | 738 | 0.0026 |
172
+ | 0.0091 | 124.0 | 744 | 0.0046 |
173
+ | 0.0113 | 125.0 | 750 | 0.0044 |
174
+ | 0.0080 | 126.0 | 756 | 0.0046 |
175
+ | 0.0135 | 127.0 | 762 | 0.0097 |
176
+ | 0.0224 | 128.0 | 768 | 0.0081 |
177
+ | 0.0214 | 129.0 | 774 | 0.0107 |
178
+ | 0.0159 | 130.0 | 780 | 0.0122 |
179
+ | 0.0179 | 131.0 | 786 | 0.0045 |
180
+ | 0.0148 | 132.0 | 792 | 0.0074 |
181
+ | 0.0139 | 133.0 | 798 | 0.0030 |
182
+ | 0.0072 | 134.0 | 804 | 0.0052 |
183
+ | 0.0065 | 135.0 | 810 | 0.0029 |
184
+ | 0.0037 | 136.0 | 816 | 0.0021 |
185
+ | 0.0031 | 137.0 | 822 | 0.0016 |
186
+ | 0.0027 | 138.0 | 828 | 0.0015 |
187
+ | 0.0020 | 139.0 | 834 | 0.0014 |
188
+ | 0.0028 | 140.0 | 840 | 0.0014 |
189
+ | 0.0017 | 141.0 | 846 | 0.0014 |
190
+ | 0.0021 | 142.0 | 852 | 0.0013 |
191
+ | 0.0020 | 143.0 | 858 | 0.0014 |
192
+ | 0.0016 | 144.0 | 864 | 0.0013 |
193
+ | 0.0021 | 145.0 | 870 | 0.0013 |
194
+ | 0.0015 | 146.0 | 876 | 0.0012 |
195
+ | 0.0015 | 147.0 | 882 | 0.0011 |
196
+ | 0.0014 | 148.0 | 888 | 0.0012 |
197
+ | 0.0014 | 149.0 | 894 | 0.0012 |
198
+ | 0.0014 | 150.0 | 900 | 0.0012 |
199
+ | 0.0012 | 151.0 | 906 | 0.0011 |
200
+ | 0.0018 | 152.0 | 912 | 0.0010 |
201
+ | 0.0015 | 153.0 | 918 | 0.0009 |
202
+ | 0.0017 | 154.0 | 924 | 0.0010 |
203
+ | 0.0024 | 155.0 | 930 | 0.0034 |
204
+ | 0.0018 | 156.0 | 936 | 0.0013 |
205
+ | 0.0013 | 157.0 | 942 | 0.0010 |
206
+ | 0.0012 | 158.0 | 948 | 0.0010 |
207
+ | 0.0010 | 159.0 | 954 | 0.0010 |
208
+ | 0.0009 | 160.0 | 960 | 0.0009 |
209
+ | 0.0008 | 161.0 | 966 | 0.0009 |
210
+ | 0.0009 | 162.0 | 972 | 0.0009 |
211
+ | 0.0008 | 163.0 | 978 | 0.0009 |
212
+ | 0.0007 | 164.0 | 984 | 0.0008 |
213
+ | 0.0009 | 165.0 | 990 | 0.0008 |
214
+ | 0.0008 | 166.0 | 996 | 0.0008 |
215
+ | 0.0009 | 167.0 | 1002 | 0.0009 |
216
+ | 0.0009 | 168.0 | 1008 | 0.0009 |
217
+ | 0.0008 | 169.0 | 1014 | 0.0009 |
218
+ | 0.0012 | 170.0 | 1020 | 0.0009 |
219
+ | 0.0008 | 171.0 | 1026 | 0.0009 |
220
+ | 0.0008 | 172.0 | 1032 | 0.0009 |
221
+ | 0.0007 | 173.0 | 1038 | 0.0008 |
222
+ | 0.0007 | 174.0 | 1044 | 0.0007 |
223
+ | 0.0006 | 175.0 | 1050 | 0.0007 |
224
+ | 0.0007 | 176.0 | 1056 | 0.0007 |
225
+ | 0.0005 | 177.0 | 1062 | 0.0007 |
226
+ | 0.0006 | 178.0 | 1068 | 0.0007 |
227
+ | 0.0007 | 179.0 | 1074 | 0.0007 |
228
+ | 0.0005 | 180.0 | 1080 | 0.0007 |
229
+ | 0.0006 | 181.0 | 1086 | 0.0007 |
230
+ | 0.0005 | 182.0 | 1092 | 0.0006 |
231
+ | 0.0005 | 183.0 | 1098 | 0.0006 |
232
+ | 0.0005 | 184.0 | 1104 | 0.0006 |
233
+ | 0.0007 | 185.0 | 1110 | 0.0006 |
234
+ | 0.0005 | 186.0 | 1116 | 0.0007 |
235
+ | 0.0006 | 187.0 | 1122 | 0.0007 |
236
+ | 0.0005 | 188.0 | 1128 | 0.0007 |
237
+ | 0.0005 | 189.0 | 1134 | 0.0007 |
238
+ | 0.0006 | 190.0 | 1140 | 0.0007 |
239
+ | 0.0006 | 191.0 | 1146 | 0.0007 |
240
+ | 0.0005 | 192.0 | 1152 | 0.0007 |
241
+ | 0.0004 | 193.0 | 1158 | 0.0007 |
242
+ | 0.0004 | 194.0 | 1164 | 0.0007 |
243
+ | 0.0006 | 195.0 | 1170 | 0.0007 |
244
+ | 0.0006 | 196.0 | 1176 | 0.0007 |
245
+ | 0.0004 | 197.0 | 1182 | 0.0007 |
246
+ | 0.0006 | 198.0 | 1188 | 0.0007 |
247
+ | 0.0006 | 199.0 | 1194 | 0.0007 |
248
+ | 0.0005 | 200.0 | 1200 | 0.0007 |
249
+ | 0.0006 | 201.0 | 1206 | 0.0007 |
250
+ | 0.0005 | 202.0 | 1212 | 0.0006 |
251
+ | 0.0005 | 203.0 | 1218 | 0.0006 |
252
+ | 0.0006 | 204.0 | 1224 | 0.0007 |
253
+ | 0.0004 | 205.0 | 1230 | 0.0007 |
254
+ | 0.0004 | 206.0 | 1236 | 0.0007 |
255
+ | 0.0006 | 207.0 | 1242 | 0.0007 |
256
+ | 0.0004 | 208.0 | 1248 | 0.0007 |
257
+ | 0.0005 | 209.0 | 1254 | 0.0006 |
258
+ | 0.0004 | 210.0 | 1260 | 0.0006 |
259
+ | 0.0004 | 211.0 | 1266 | 0.0006 |
260
+ | 0.0004 | 212.0 | 1272 | 0.0006 |
261
+ | 0.0004 | 213.0 | 1278 | 0.0006 |
262
+ | 0.0005 | 214.0 | 1284 | 0.0006 |
263
+ | 0.0005 | 215.0 | 1290 | 0.0006 |
264
+ | 0.0004 | 216.0 | 1296 | 0.0006 |
265
+ | 0.0005 | 217.0 | 1302 | 0.0006 |
266
+ | 0.0005 | 218.0 | 1308 | 0.0006 |
267
+ | 0.0005 | 219.0 | 1314 | 0.0006 |
268
+ | 0.0004 | 220.0 | 1320 | 0.0006 |
269
+ | 0.0004 | 221.0 | 1326 | 0.0006 |
270
+ | 0.0004 | 222.0 | 1332 | 0.0006 |
271
+ | 0.0005 | 223.0 | 1338 | 0.0006 |
272
+ | 0.0004 | 224.0 | 1344 | 0.0005 |
273
+ | 0.0004 | 225.0 | 1350 | 0.0006 |
274
+ | 0.0003 | 226.0 | 1356 | 0.0006 |
275
+ | 0.0004 | 227.0 | 1362 | 0.0006 |
276
+ | 0.0004 | 228.0 | 1368 | 0.0006 |
277
+ | 0.0004 | 229.0 | 1374 | 0.0006 |
278
+ | 0.0005 | 230.0 | 1380 | 0.0006 |
279
+ | 0.0005 | 231.0 | 1386 | 0.0006 |
280
+ | 0.0004 | 232.0 | 1392 | 0.0006 |
281
+ | 0.0005 | 233.0 | 1398 | 0.0006 |
282
+ | 0.0004 | 234.0 | 1404 | 0.0006 |
283
+ | 0.0004 | 235.0 | 1410 | 0.0006 |
284
+ | 0.0005 | 236.0 | 1416 | 0.0006 |
285
+ | 0.0004 | 237.0 | 1422 | 0.0006 |
286
+ | 0.0004 | 238.0 | 1428 | 0.0006 |
287
+ | 0.0003 | 239.0 | 1434 | 0.0006 |
288
+ | 0.0004 | 240.0 | 1440 | 0.0006 |
289
+ | 0.0006 | 241.0 | 1446 | 0.0006 |
290
+ | 0.0003 | 242.0 | 1452 | 0.0006 |
291
+ | 0.0003 | 243.0 | 1458 | 0.0006 |
292
+ | 0.0005 | 244.0 | 1464 | 0.0006 |
293
+ | 0.0003 | 245.0 | 1470 | 0.0006 |
294
+ | 0.0004 | 246.0 | 1476 | 0.0006 |
295
+ | 0.0004 | 247.0 | 1482 | 0.0006 |
296
+ | 0.0005 | 248.0 | 1488 | 0.0006 |
297
+ | 0.0006 | 249.0 | 1494 | 0.0006 |
298
+ | 0.0004 | 250.0 | 1500 | 0.0006 |
299
 
300
 
301
  ### Framework versions