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bert-base-uncased-sst-2-32-13

Text Classification
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PyTorch
bert
Generated from Trainer
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1
  ---
2
- license: mit
3
- base_model: roberta-large
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  tags:
5
  - generated_from_trainer
6
  metrics:
7
  - accuracy
8
  model-index:
9
- - name: roberta-large-sst-2-64-13
10
  results: []
11
  ---
12
 
13
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
  should probably proofread and complete it, then remove this comment. -->
15
 
16
- # roberta-large-sst-2-64-13
17
 
18
- This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
- - Loss: 0.7353
21
- - Accuracy: 0.9219
22
 
23
  ## Model description
24
 
@@ -43,163 +43,163 @@ The following hyperparameters were used during training:
43
  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - lr_scheduler_warmup_steps: 500
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  - num_epochs: 150
48
 
49
  ### Training results
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
- | No log | 1.0 | 4 | 0.6943 | 0.5 |
54
- | No log | 2.0 | 8 | 0.6943 | 0.5 |
55
- | 0.6976 | 3.0 | 12 | 0.6942 | 0.5 |
56
- | 0.6976 | 4.0 | 16 | 0.6942 | 0.5 |
57
- | 0.6988 | 5.0 | 20 | 0.6941 | 0.5 |
58
- | 0.6988 | 6.0 | 24 | 0.6940 | 0.5 |
59
- | 0.6988 | 7.0 | 28 | 0.6938 | 0.5 |
60
- | 0.7045 | 8.0 | 32 | 0.6937 | 0.5 |
61
- | 0.7045 | 9.0 | 36 | 0.6936 | 0.5 |
62
- | 0.7032 | 10.0 | 40 | 0.6935 | 0.5 |
63
- | 0.7032 | 11.0 | 44 | 0.6933 | 0.5 |
64
- | 0.7032 | 12.0 | 48 | 0.6932 | 0.5 |
65
- | 0.6994 | 13.0 | 52 | 0.6931 | 0.5078 |
66
- | 0.6994 | 14.0 | 56 | 0.6929 | 0.5 |
67
- | 0.6909 | 15.0 | 60 | 0.6928 | 0.5 |
68
- | 0.6909 | 16.0 | 64 | 0.6926 | 0.5 |
69
- | 0.6909 | 17.0 | 68 | 0.6925 | 0.5 |
70
- | 0.6985 | 18.0 | 72 | 0.6925 | 0.5 |
71
- | 0.6985 | 19.0 | 76 | 0.6924 | 0.5 |
72
- | 0.6963 | 20.0 | 80 | 0.6922 | 0.5 |
73
- | 0.6963 | 21.0 | 84 | 0.6919 | 0.5 |
74
- | 0.6963 | 22.0 | 88 | 0.6915 | 0.5 |
75
- | 0.6926 | 23.0 | 92 | 0.6911 | 0.5 |
76
- | 0.6926 | 24.0 | 96 | 0.6907 | 0.5 |
77
- | 0.6825 | 25.0 | 100 | 0.6904 | 0.5156 |
78
- | 0.6825 | 26.0 | 104 | 0.6905 | 0.6172 |
79
- | 0.6825 | 27.0 | 108 | 0.6907 | 0.625 |
80
- | 0.6758 | 28.0 | 112 | 0.6908 | 0.625 |
81
- | 0.6758 | 29.0 | 116 | 0.6910 | 0.6406 |
82
- | 0.6721 | 30.0 | 120 | 0.6914 | 0.6406 |
83
- | 0.6721 | 31.0 | 124 | 0.6910 | 0.6406 |
84
- | 0.6721 | 32.0 | 128 | 0.6929 | 0.6406 |
85
- | 0.656 | 33.0 | 132 | 0.7045 | 0.6328 |
86
- | 0.656 | 34.0 | 136 | 0.6919 | 0.625 |
87
- | 0.6341 | 35.0 | 140 | 0.6678 | 0.6094 |
88
- | 0.6341 | 36.0 | 144 | 0.6400 | 0.6641 |
89
- | 0.6341 | 37.0 | 148 | 0.6108 | 0.7188 |
90
- | 0.5848 | 38.0 | 152 | 0.5701 | 0.8047 |
91
- | 0.5848 | 39.0 | 156 | 0.5212 | 0.8203 |
92
- | 0.4973 | 40.0 | 160 | 0.4351 | 0.8828 |
93
- | 0.4973 | 41.0 | 164 | 0.3442 | 0.9141 |
94
- | 0.4973 | 42.0 | 168 | 0.3130 | 0.9141 |
95
- | 0.3116 | 43.0 | 172 | 0.2974 | 0.9141 |
96
- | 0.3116 | 44.0 | 176 | 0.2484 | 0.9219 |
97
- | 0.1461 | 45.0 | 180 | 0.3085 | 0.9219 |
98
- | 0.1461 | 46.0 | 184 | 0.2781 | 0.9375 |
99
- | 0.1461 | 47.0 | 188 | 0.3257 | 0.9141 |
100
- | 0.0428 | 48.0 | 192 | 0.4080 | 0.9141 |
101
- | 0.0428 | 49.0 | 196 | 0.4824 | 0.9062 |
102
- | 0.0032 | 50.0 | 200 | 0.5675 | 0.9141 |
103
- | 0.0032 | 51.0 | 204 | 0.6358 | 0.9141 |
104
- | 0.0032 | 52.0 | 208 | 0.6670 | 0.9141 |
105
- | 0.0357 | 53.0 | 212 | 0.4577 | 0.9219 |
106
- | 0.0357 | 54.0 | 216 | 0.6720 | 0.8984 |
107
- | 0.0164 | 55.0 | 220 | 0.4812 | 0.9375 |
108
- | 0.0164 | 56.0 | 224 | 0.6915 | 0.9141 |
109
- | 0.0164 | 57.0 | 228 | 0.6746 | 0.9219 |
110
- | 0.0004 | 58.0 | 232 | 0.6806 | 0.9219 |
111
- | 0.0004 | 59.0 | 236 | 0.6390 | 0.9219 |
112
- | 0.05 | 60.0 | 240 | 0.7852 | 0.8984 |
113
- | 0.05 | 61.0 | 244 | 0.6347 | 0.9219 |
114
- | 0.05 | 62.0 | 248 | 0.8325 | 0.8984 |
115
- | 0.0024 | 63.0 | 252 | 0.8310 | 0.8906 |
116
- | 0.0024 | 64.0 | 256 | 0.5289 | 0.9375 |
117
- | 0.048 | 65.0 | 260 | 0.9447 | 0.875 |
118
- | 0.048 | 66.0 | 264 | 0.8435 | 0.8906 |
119
- | 0.048 | 67.0 | 268 | 0.5268 | 0.9297 |
120
- | 0.0299 | 68.0 | 272 | 0.7885 | 0.9062 |
121
- | 0.0299 | 69.0 | 276 | 0.8814 | 0.8906 |
122
- | 0.0506 | 70.0 | 280 | 0.4846 | 0.9453 |
123
- | 0.0506 | 71.0 | 284 | 0.7900 | 0.8984 |
124
- | 0.0506 | 72.0 | 288 | 0.7042 | 0.9141 |
125
- | 0.0003 | 73.0 | 292 | 0.6504 | 0.9141 |
126
- | 0.0003 | 74.0 | 296 | 0.5608 | 0.9219 |
127
- | 0.0002 | 75.0 | 300 | 0.5473 | 0.9375 |
128
- | 0.0002 | 76.0 | 304 | 0.5033 | 0.9453 |
129
- | 0.0002 | 77.0 | 308 | 0.5022 | 0.9453 |
130
- | 0.0013 | 78.0 | 312 | 0.5551 | 0.9375 |
131
- | 0.0013 | 79.0 | 316 | 0.5786 | 0.9375 |
132
- | 0.0002 | 80.0 | 320 | 0.5848 | 0.9375 |
133
- | 0.0002 | 81.0 | 324 | 0.5882 | 0.9375 |
134
- | 0.0002 | 82.0 | 328 | 0.5904 | 0.9375 |
135
- | 0.0002 | 83.0 | 332 | 0.5924 | 0.9375 |
136
- | 0.0002 | 84.0 | 336 | 0.5952 | 0.9375 |
137
- | 0.0001 | 85.0 | 340 | 0.5985 | 0.9375 |
138
- | 0.0001 | 86.0 | 344 | 0.5289 | 0.9453 |
139
- | 0.0001 | 87.0 | 348 | 0.6739 | 0.9141 |
140
- | 0.0496 | 88.0 | 352 | 0.5325 | 0.9453 |
141
- | 0.0496 | 89.0 | 356 | 0.6028 | 0.9297 |
142
- | 0.0002 | 90.0 | 360 | 0.6686 | 0.9219 |
143
- | 0.0002 | 91.0 | 364 | 0.8086 | 0.8984 |
144
- | 0.0002 | 92.0 | 368 | 0.8812 | 0.8984 |
145
- | 0.0001 | 93.0 | 372 | 0.8931 | 0.8906 |
146
- | 0.0001 | 94.0 | 376 | 0.9064 | 0.8828 |
147
- | 0.0001 | 95.0 | 380 | 0.9333 | 0.8906 |
148
- | 0.0001 | 96.0 | 384 | 0.9439 | 0.8828 |
149
- | 0.0001 | 97.0 | 388 | 0.9442 | 0.8828 |
150
- | 0.0001 | 98.0 | 392 | 0.9427 | 0.8828 |
151
- | 0.0001 | 99.0 | 396 | 0.9359 | 0.8984 |
152
- | 0.0001 | 100.0 | 400 | 0.9406 | 0.8984 |
153
- | 0.0001 | 101.0 | 404 | 0.9439 | 0.8984 |
154
- | 0.0001 | 102.0 | 408 | 0.9448 | 0.8984 |
155
- | 0.0001 | 103.0 | 412 | 0.6907 | 0.9219 |
156
- | 0.0001 | 104.0 | 416 | 0.6708 | 0.9297 |
157
- | 0.0301 | 105.0 | 420 | 0.8172 | 0.9062 |
158
- | 0.0301 | 106.0 | 424 | 0.5607 | 0.9453 |
159
- | 0.0301 | 107.0 | 428 | 0.6901 | 0.9297 |
160
- | 0.0001 | 108.0 | 432 | 0.9126 | 0.9062 |
161
- | 0.0001 | 109.0 | 436 | 0.9327 | 0.9062 |
162
- | 0.0234 | 110.0 | 440 | 0.7272 | 0.9219 |
163
- | 0.0234 | 111.0 | 444 | 0.6046 | 0.9375 |
164
- | 0.0234 | 112.0 | 448 | 0.6453 | 0.9219 |
165
- | 0.0006 | 113.0 | 452 | 0.6153 | 0.9375 |
166
- | 0.0006 | 114.0 | 456 | 0.6766 | 0.9219 |
167
- | 0.0001 | 115.0 | 460 | 0.7189 | 0.9062 |
168
- | 0.0001 | 116.0 | 464 | 0.7305 | 0.9219 |
169
- | 0.0001 | 117.0 | 468 | 0.6532 | 0.9297 |
170
- | 0.0001 | 118.0 | 472 | 0.6502 | 0.9375 |
171
- | 0.0001 | 119.0 | 476 | 0.6532 | 0.9375 |
172
- | 0.0001 | 120.0 | 480 | 0.6551 | 0.9375 |
173
- | 0.0001 | 121.0 | 484 | 0.6564 | 0.9375 |
174
- | 0.0001 | 122.0 | 488 | 0.6568 | 0.9375 |
175
- | 0.0001 | 123.0 | 492 | 0.6573 | 0.9375 |
176
- | 0.0001 | 124.0 | 496 | 0.6582 | 0.9375 |
177
- | 0.0001 | 125.0 | 500 | 0.6598 | 0.9375 |
178
- | 0.0001 | 126.0 | 504 | 0.6614 | 0.9375 |
179
- | 0.0001 | 127.0 | 508 | 0.6634 | 0.9375 |
180
- | 0.0001 | 128.0 | 512 | 0.6651 | 0.9375 |
181
- | 0.0001 | 129.0 | 516 | 0.6666 | 0.9375 |
182
- | 0.0 | 130.0 | 520 | 0.6678 | 0.9375 |
183
- | 0.0 | 131.0 | 524 | 0.6668 | 0.9375 |
184
- | 0.0 | 132.0 | 528 | 0.6638 | 0.9375 |
185
- | 0.0001 | 133.0 | 532 | 0.6921 | 0.9297 |
186
- | 0.0001 | 134.0 | 536 | 0.7904 | 0.9219 |
187
- | 0.0001 | 135.0 | 540 | 0.7349 | 0.9219 |
188
- | 0.0001 | 136.0 | 544 | 0.7261 | 0.9297 |
189
- | 0.0001 | 137.0 | 548 | 0.7197 | 0.9297 |
190
- | 0.0 | 138.0 | 552 | 0.7119 | 0.9297 |
191
- | 0.0 | 139.0 | 556 | 0.7039 | 0.9297 |
192
- | 0.0 | 140.0 | 560 | 0.7150 | 0.9219 |
193
- | 0.0 | 141.0 | 564 | 0.7269 | 0.9219 |
194
- | 0.0 | 142.0 | 568 | 0.7302 | 0.9219 |
195
- | 0.0 | 143.0 | 572 | 0.7314 | 0.9219 |
196
- | 0.0 | 144.0 | 576 | 0.7320 | 0.9219 |
197
- | 0.0 | 145.0 | 580 | 0.7331 | 0.9219 |
198
- | 0.0 | 146.0 | 584 | 0.7339 | 0.9219 |
199
- | 0.0 | 147.0 | 588 | 0.7345 | 0.9219 |
200
- | 0.0 | 148.0 | 592 | 0.7350 | 0.9219 |
201
- | 0.0 | 149.0 | 596 | 0.7352 | 0.9219 |
202
- | 0.0 | 150.0 | 600 | 0.7353 | 0.9219 |
203
 
204
 
205
  ### Framework versions
 
1
  ---
2
+ license: apache-2.0
3
+ base_model: bert-base-uncased
4
  tags:
5
  - generated_from_trainer
6
  metrics:
7
  - accuracy
8
  model-index:
9
+ - name: bert-base-uncased-sst-2-32-13
10
  results: []
11
  ---
12
 
13
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
  should probably proofread and complete it, then remove this comment. -->
15
 
16
+ # bert-base-uncased-sst-2-32-13
17
 
18
+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
19
  It achieves the following results on the evaluation set:
20
+ - Loss: 1.5606
21
+ - Accuracy: 0.625
22
 
23
  ## Model description
24
 
 
43
  - seed: 42
44
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
45
  - lr_scheduler_type: linear
46
+ - lr_scheduler_warmup_steps: 50
47
  - num_epochs: 150
48
 
49
  ### Training results
50
 
51
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
52
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
53
+ | No log | 1.0 | 2 | 0.6827 | 0.6875 |
54
+ | No log | 2.0 | 4 | 0.6826 | 0.6875 |
55
+ | No log | 3.0 | 6 | 0.6822 | 0.7031 |
56
+ | No log | 4.0 | 8 | 0.6818 | 0.6719 |
57
+ | 0.6948 | 5.0 | 10 | 0.6812 | 0.6719 |
58
+ | 0.6948 | 6.0 | 12 | 0.6805 | 0.6406 |
59
+ | 0.6948 | 7.0 | 14 | 0.6797 | 0.6406 |
60
+ | 0.6948 | 8.0 | 16 | 0.6789 | 0.6406 |
61
+ | 0.6948 | 9.0 | 18 | 0.6779 | 0.6562 |
62
+ | 0.6864 | 10.0 | 20 | 0.6768 | 0.6562 |
63
+ | 0.6864 | 11.0 | 22 | 0.6755 | 0.6562 |
64
+ | 0.6864 | 12.0 | 24 | 0.6741 | 0.6875 |
65
+ | 0.6864 | 13.0 | 26 | 0.6726 | 0.6719 |
66
+ | 0.6864 | 14.0 | 28 | 0.6710 | 0.6719 |
67
+ | 0.6517 | 15.0 | 30 | 0.6694 | 0.7031 |
68
+ | 0.6517 | 16.0 | 32 | 0.6676 | 0.6875 |
69
+ | 0.6517 | 17.0 | 34 | 0.6657 | 0.6719 |
70
+ | 0.6517 | 18.0 | 36 | 0.6643 | 0.625 |
71
+ | 0.6517 | 19.0 | 38 | 0.6636 | 0.6094 |
72
+ | 0.6027 | 20.0 | 40 | 0.6642 | 0.5938 |
73
+ | 0.6027 | 21.0 | 42 | 0.6632 | 0.5781 |
74
+ | 0.6027 | 22.0 | 44 | 0.6607 | 0.5781 |
75
+ | 0.6027 | 23.0 | 46 | 0.6582 | 0.6094 |
76
+ | 0.6027 | 24.0 | 48 | 0.6562 | 0.6406 |
77
+ | 0.4998 | 25.0 | 50 | 0.6546 | 0.6094 |
78
+ | 0.4998 | 26.0 | 52 | 0.6503 | 0.5938 |
79
+ | 0.4998 | 27.0 | 54 | 0.6450 | 0.6094 |
80
+ | 0.4998 | 28.0 | 56 | 0.6395 | 0.6094 |
81
+ | 0.4998 | 29.0 | 58 | 0.6362 | 0.5938 |
82
+ | 0.3593 | 30.0 | 60 | 0.6380 | 0.5938 |
83
+ | 0.3593 | 31.0 | 62 | 0.6361 | 0.5938 |
84
+ | 0.3593 | 32.0 | 64 | 0.6348 | 0.5938 |
85
+ | 0.3593 | 33.0 | 66 | 0.6327 | 0.625 |
86
+ | 0.3593 | 34.0 | 68 | 0.6301 | 0.6094 |
87
+ | 0.2483 | 35.0 | 70 | 0.6347 | 0.6094 |
88
+ | 0.2483 | 36.0 | 72 | 0.6401 | 0.5938 |
89
+ | 0.2483 | 37.0 | 74 | 0.6468 | 0.5781 |
90
+ | 0.2483 | 38.0 | 76 | 0.6533 | 0.5781 |
91
+ | 0.2483 | 39.0 | 78 | 0.6600 | 0.5938 |
92
+ | 0.1735 | 40.0 | 80 | 0.6621 | 0.5938 |
93
+ | 0.1735 | 41.0 | 82 | 0.6652 | 0.5938 |
94
+ | 0.1735 | 42.0 | 84 | 0.6745 | 0.6094 |
95
+ | 0.1735 | 43.0 | 86 | 0.6849 | 0.6094 |
96
+ | 0.1735 | 44.0 | 88 | 0.6956 | 0.5938 |
97
+ | 0.111 | 45.0 | 90 | 0.7087 | 0.5938 |
98
+ | 0.111 | 46.0 | 92 | 0.7238 | 0.5938 |
99
+ | 0.111 | 47.0 | 94 | 0.7376 | 0.5938 |
100
+ | 0.111 | 48.0 | 96 | 0.7506 | 0.5938 |
101
+ | 0.111 | 49.0 | 98 | 0.7646 | 0.6094 |
102
+ | 0.0691 | 50.0 | 100 | 0.7817 | 0.6094 |
103
+ | 0.0691 | 51.0 | 102 | 0.8015 | 0.625 |
104
+ | 0.0691 | 52.0 | 104 | 0.8277 | 0.625 |
105
+ | 0.0691 | 53.0 | 106 | 0.8582 | 0.625 |
106
+ | 0.0691 | 54.0 | 108 | 0.8849 | 0.625 |
107
+ | 0.0395 | 55.0 | 110 | 0.9094 | 0.625 |
108
+ | 0.0395 | 56.0 | 112 | 0.9309 | 0.625 |
109
+ | 0.0395 | 57.0 | 114 | 0.9525 | 0.625 |
110
+ | 0.0395 | 58.0 | 116 | 0.9740 | 0.6094 |
111
+ | 0.0395 | 59.0 | 118 | 0.9959 | 0.6094 |
112
+ | 0.0213 | 60.0 | 120 | 1.0209 | 0.6094 |
113
+ | 0.0213 | 61.0 | 122 | 1.0452 | 0.625 |
114
+ | 0.0213 | 62.0 | 124 | 1.0680 | 0.625 |
115
+ | 0.0213 | 63.0 | 126 | 1.0908 | 0.625 |
116
+ | 0.0213 | 64.0 | 128 | 1.1149 | 0.6094 |
117
+ | 0.0129 | 65.0 | 130 | 1.1381 | 0.625 |
118
+ | 0.0129 | 66.0 | 132 | 1.1590 | 0.625 |
119
+ | 0.0129 | 67.0 | 134 | 1.1787 | 0.625 |
120
+ | 0.0129 | 68.0 | 136 | 1.1960 | 0.625 |
121
+ | 0.0129 | 69.0 | 138 | 1.2125 | 0.625 |
122
+ | 0.0093 | 70.0 | 140 | 1.2267 | 0.625 |
123
+ | 0.0093 | 71.0 | 142 | 1.2399 | 0.625 |
124
+ | 0.0093 | 72.0 | 144 | 1.2516 | 0.625 |
125
+ | 0.0093 | 73.0 | 146 | 1.2626 | 0.625 |
126
+ | 0.0093 | 74.0 | 148 | 1.2726 | 0.6406 |
127
+ | 0.0071 | 75.0 | 150 | 1.2825 | 0.6406 |
128
+ | 0.0071 | 76.0 | 152 | 1.2921 | 0.625 |
129
+ | 0.0071 | 77.0 | 154 | 1.3016 | 0.625 |
130
+ | 0.0071 | 78.0 | 156 | 1.3104 | 0.625 |
131
+ | 0.0071 | 79.0 | 158 | 1.3177 | 0.625 |
132
+ | 0.0059 | 80.0 | 160 | 1.3243 | 0.625 |
133
+ | 0.0059 | 81.0 | 162 | 1.3311 | 0.625 |
134
+ | 0.0059 | 82.0 | 164 | 1.3377 | 0.625 |
135
+ | 0.0059 | 83.0 | 166 | 1.3446 | 0.625 |
136
+ | 0.0059 | 84.0 | 168 | 1.3519 | 0.625 |
137
+ | 0.0051 | 85.0 | 170 | 1.3590 | 0.625 |
138
+ | 0.0051 | 86.0 | 172 | 1.3662 | 0.625 |
139
+ | 0.0051 | 87.0 | 174 | 1.3731 | 0.625 |
140
+ | 0.0051 | 88.0 | 176 | 1.3801 | 0.625 |
141
+ | 0.0051 | 89.0 | 178 | 1.3867 | 0.625 |
142
+ | 0.0045 | 90.0 | 180 | 1.3929 | 0.625 |
143
+ | 0.0045 | 91.0 | 182 | 1.3988 | 0.625 |
144
+ | 0.0045 | 92.0 | 184 | 1.4048 | 0.625 |
145
+ | 0.0045 | 93.0 | 186 | 1.4110 | 0.625 |
146
+ | 0.0045 | 94.0 | 188 | 1.4171 | 0.625 |
147
+ | 0.0042 | 95.0 | 190 | 1.4231 | 0.625 |
148
+ | 0.0042 | 96.0 | 192 | 1.4290 | 0.625 |
149
+ | 0.0042 | 97.0 | 194 | 1.4346 | 0.625 |
150
+ | 0.0042 | 98.0 | 196 | 1.4401 | 0.625 |
151
+ | 0.0042 | 99.0 | 198 | 1.4454 | 0.625 |
152
+ | 0.0037 | 100.0 | 200 | 1.4506 | 0.625 |
153
+ | 0.0037 | 101.0 | 202 | 1.4555 | 0.625 |
154
+ | 0.0037 | 102.0 | 204 | 1.4604 | 0.625 |
155
+ | 0.0037 | 103.0 | 206 | 1.4650 | 0.625 |
156
+ | 0.0037 | 104.0 | 208 | 1.4690 | 0.625 |
157
+ | 0.0034 | 105.0 | 210 | 1.4728 | 0.625 |
158
+ | 0.0034 | 106.0 | 212 | 1.4765 | 0.625 |
159
+ | 0.0034 | 107.0 | 214 | 1.4802 | 0.625 |
160
+ | 0.0034 | 108.0 | 216 | 1.4836 | 0.625 |
161
+ | 0.0034 | 109.0 | 218 | 1.4870 | 0.625 |
162
+ | 0.0033 | 110.0 | 220 | 1.4903 | 0.625 |
163
+ | 0.0033 | 111.0 | 222 | 1.4936 | 0.625 |
164
+ | 0.0033 | 112.0 | 224 | 1.4969 | 0.625 |
165
+ | 0.0033 | 113.0 | 226 | 1.5002 | 0.625 |
166
+ | 0.0033 | 114.0 | 228 | 1.5036 | 0.625 |
167
+ | 0.0031 | 115.0 | 230 | 1.5069 | 0.625 |
168
+ | 0.0031 | 116.0 | 232 | 1.5100 | 0.625 |
169
+ | 0.0031 | 117.0 | 234 | 1.5130 | 0.625 |
170
+ | 0.0031 | 118.0 | 236 | 1.5161 | 0.625 |
171
+ | 0.0031 | 119.0 | 238 | 1.5190 | 0.625 |
172
+ | 0.003 | 120.0 | 240 | 1.5216 | 0.625 |
173
+ | 0.003 | 121.0 | 242 | 1.5242 | 0.625 |
174
+ | 0.003 | 122.0 | 244 | 1.5269 | 0.625 |
175
+ | 0.003 | 123.0 | 246 | 1.5295 | 0.625 |
176
+ | 0.003 | 124.0 | 248 | 1.5321 | 0.625 |
177
+ | 0.0028 | 125.0 | 250 | 1.5345 | 0.625 |
178
+ | 0.0028 | 126.0 | 252 | 1.5367 | 0.625 |
179
+ | 0.0028 | 127.0 | 254 | 1.5386 | 0.625 |
180
+ | 0.0028 | 128.0 | 256 | 1.5405 | 0.625 |
181
+ | 0.0028 | 129.0 | 258 | 1.5422 | 0.625 |
182
+ | 0.0027 | 130.0 | 260 | 1.5438 | 0.625 |
183
+ | 0.0027 | 131.0 | 262 | 1.5453 | 0.625 |
184
+ | 0.0027 | 132.0 | 264 | 1.5468 | 0.625 |
185
+ | 0.0027 | 133.0 | 266 | 1.5482 | 0.625 |
186
+ | 0.0027 | 134.0 | 268 | 1.5495 | 0.625 |
187
+ | 0.0027 | 135.0 | 270 | 1.5507 | 0.625 |
188
+ | 0.0027 | 136.0 | 272 | 1.5518 | 0.625 |
189
+ | 0.0027 | 137.0 | 274 | 1.5529 | 0.625 |
190
+ | 0.0027 | 138.0 | 276 | 1.5539 | 0.625 |
191
+ | 0.0027 | 139.0 | 278 | 1.5549 | 0.625 |
192
+ | 0.0026 | 140.0 | 280 | 1.5557 | 0.625 |
193
+ | 0.0026 | 141.0 | 282 | 1.5565 | 0.625 |
194
+ | 0.0026 | 142.0 | 284 | 1.5573 | 0.625 |
195
+ | 0.0026 | 143.0 | 286 | 1.5580 | 0.625 |
196
+ | 0.0026 | 144.0 | 288 | 1.5587 | 0.625 |
197
+ | 0.0025 | 145.0 | 290 | 1.5593 | 0.625 |
198
+ | 0.0025 | 146.0 | 292 | 1.5597 | 0.625 |
199
+ | 0.0025 | 147.0 | 294 | 1.5601 | 0.625 |
200
+ | 0.0025 | 148.0 | 296 | 1.5603 | 0.625 |
201
+ | 0.0025 | 149.0 | 298 | 1.5605 | 0.625 |
202
+ | 0.0026 | 150.0 | 300 | 1.5606 | 0.625 |
203
 
204
 
205
  ### Framework versions