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best_model-sst-2-32-13

Text Classification
Transformers
PyTorch
bert
Generated from Trainer
Inference Endpoints
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@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0073
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- - Accuracy: 0.6875
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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 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | No log | 1.0 | 2 | 0.6566 | 0.6719 |
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- | No log | 2.0 | 4 | 0.6561 | 0.6719 |
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- | No log | 3.0 | 6 | 0.6553 | 0.6719 |
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- | No log | 4.0 | 8 | 0.6542 | 0.6719 |
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- | 0.4607 | 5.0 | 10 | 0.6525 | 0.6719 |
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- | 0.4607 | 6.0 | 12 | 0.6504 | 0.6719 |
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- | 0.4607 | 7.0 | 14 | 0.6480 | 0.6719 |
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- | 0.4607 | 8.0 | 16 | 0.6454 | 0.6719 |
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- | 0.4607 | 9.0 | 18 | 0.6432 | 0.6719 |
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- | 0.4685 | 10.0 | 20 | 0.6408 | 0.6719 |
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- | 0.4685 | 11.0 | 22 | 0.6383 | 0.6562 |
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- | 0.4685 | 12.0 | 24 | 0.6357 | 0.6719 |
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- | 0.4685 | 13.0 | 26 | 0.6331 | 0.6562 |
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- | 0.4685 | 14.0 | 28 | 0.6310 | 0.6719 |
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- | 0.444 | 15.0 | 30 | 0.6289 | 0.6719 |
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- | 0.444 | 16.0 | 32 | 0.6270 | 0.6562 |
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- | 0.444 | 17.0 | 34 | 0.6253 | 0.6562 |
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- | 0.444 | 18.0 | 36 | 0.6235 | 0.6406 |
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- | 0.444 | 19.0 | 38 | 0.6216 | 0.6406 |
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- | 0.4147 | 20.0 | 40 | 0.6199 | 0.6406 |
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- | 0.4147 | 21.0 | 42 | 0.6183 | 0.6562 |
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- | 0.4147 | 22.0 | 44 | 0.6168 | 0.6562 |
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- | 0.4147 | 23.0 | 46 | 0.6155 | 0.6562 |
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- | 0.4147 | 24.0 | 48 | 0.6144 | 0.6562 |
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- | 0.409 | 25.0 | 50 | 0.6133 | 0.6406 |
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- | 0.409 | 26.0 | 52 | 0.6123 | 0.6406 |
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- | 0.409 | 27.0 | 54 | 0.6116 | 0.6562 |
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- | 0.409 | 28.0 | 56 | 0.6110 | 0.6562 |
81
- | 0.409 | 29.0 | 58 | 0.6103 | 0.6562 |
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- | 0.3614 | 30.0 | 60 | 0.6099 | 0.6719 |
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- | 0.3614 | 31.0 | 62 | 0.6094 | 0.6719 |
84
- | 0.3614 | 32.0 | 64 | 0.6090 | 0.6719 |
85
- | 0.3614 | 33.0 | 66 | 0.6090 | 0.6719 |
86
- | 0.3614 | 34.0 | 68 | 0.6090 | 0.6875 |
87
- | 0.3297 | 35.0 | 70 | 0.6093 | 0.6875 |
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- | 0.3297 | 36.0 | 72 | 0.6095 | 0.6875 |
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- | 0.3297 | 37.0 | 74 | 0.6093 | 0.6875 |
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- | 0.3297 | 38.0 | 76 | 0.6089 | 0.6875 |
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- | 0.3297 | 39.0 | 78 | 0.6081 | 0.6875 |
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- | 0.2957 | 40.0 | 80 | 0.6072 | 0.6875 |
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- | 0.2957 | 41.0 | 82 | 0.6064 | 0.7031 |
94
- | 0.2957 | 42.0 | 84 | 0.6054 | 0.6875 |
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- | 0.2957 | 43.0 | 86 | 0.6041 | 0.6719 |
96
- | 0.2957 | 44.0 | 88 | 0.6031 | 0.6719 |
97
- | 0.2704 | 45.0 | 90 | 0.6022 | 0.6875 |
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- | 0.2704 | 46.0 | 92 | 0.6017 | 0.6875 |
99
- | 0.2704 | 47.0 | 94 | 0.6012 | 0.6875 |
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- | 0.2704 | 48.0 | 96 | 0.6008 | 0.6719 |
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- | 0.2704 | 49.0 | 98 | 0.5998 | 0.6719 |
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- | 0.2302 | 50.0 | 100 | 0.5987 | 0.6875 |
103
- | 0.2302 | 51.0 | 102 | 0.5977 | 0.6875 |
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- | 0.2302 | 52.0 | 104 | 0.5959 | 0.6875 |
105
- | 0.2302 | 53.0 | 106 | 0.5935 | 0.6719 |
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- | 0.2302 | 54.0 | 108 | 0.5913 | 0.6719 |
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- | 0.198 | 55.0 | 110 | 0.5895 | 0.6719 |
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- | 0.198 | 56.0 | 112 | 0.5882 | 0.6719 |
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- | 0.198 | 57.0 | 114 | 0.5865 | 0.6875 |
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- | 0.198 | 58.0 | 116 | 0.5854 | 0.7031 |
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- | 0.198 | 59.0 | 118 | 0.5852 | 0.7031 |
112
- | 0.1729 | 60.0 | 120 | 0.5854 | 0.7031 |
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- | 0.1729 | 61.0 | 122 | 0.5857 | 0.7031 |
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- | 0.1729 | 62.0 | 124 | 0.5861 | 0.6875 |
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- | 0.1729 | 63.0 | 126 | 0.5864 | 0.6875 |
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- | 0.1729 | 64.0 | 128 | 0.5866 | 0.6875 |
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- | 0.1495 | 65.0 | 130 | 0.5863 | 0.6719 |
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- | 0.1495 | 66.0 | 132 | 0.5858 | 0.6719 |
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- | 0.1495 | 67.0 | 134 | 0.5856 | 0.6875 |
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- | 0.1495 | 68.0 | 136 | 0.5860 | 0.6875 |
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- | 0.1495 | 69.0 | 138 | 0.5860 | 0.6719 |
122
- | 0.1223 | 70.0 | 140 | 0.5863 | 0.6719 |
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- | 0.1223 | 71.0 | 142 | 0.5870 | 0.6875 |
124
- | 0.1223 | 72.0 | 144 | 0.5881 | 0.6875 |
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- | 0.1223 | 73.0 | 146 | 0.5890 | 0.6719 |
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- | 0.1223 | 74.0 | 148 | 0.5899 | 0.6719 |
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- | 0.1034 | 75.0 | 150 | 0.5909 | 0.6719 |
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- | 0.1034 | 76.0 | 152 | 0.5920 | 0.6719 |
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- | 0.1034 | 77.0 | 154 | 0.5935 | 0.6562 |
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- | 0.1034 | 78.0 | 156 | 0.5953 | 0.6562 |
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- | 0.1034 | 79.0 | 158 | 0.5969 | 0.6562 |
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- | 0.087 | 80.0 | 160 | 0.5984 | 0.6562 |
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- | 0.087 | 81.0 | 162 | 0.6008 | 0.6562 |
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- | 0.087 | 82.0 | 164 | 0.6031 | 0.6719 |
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- | 0.087 | 83.0 | 166 | 0.6057 | 0.6562 |
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- | 0.087 | 84.0 | 168 | 0.6088 | 0.6562 |
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- | 0.0705 | 85.0 | 170 | 0.6126 | 0.6562 |
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- | 0.0705 | 86.0 | 172 | 0.6164 | 0.6562 |
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- | 0.0705 | 87.0 | 174 | 0.6198 | 0.6719 |
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- | 0.0705 | 88.0 | 176 | 0.6227 | 0.6719 |
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- | 0.0705 | 89.0 | 178 | 0.6257 | 0.6719 |
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- | 0.0551 | 90.0 | 180 | 0.6288 | 0.6719 |
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- | 0.0551 | 91.0 | 182 | 0.6321 | 0.6719 |
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- | 0.0551 | 92.0 | 184 | 0.6360 | 0.6719 |
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- | 0.0551 | 93.0 | 186 | 0.6403 | 0.6875 |
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- | 0.0551 | 94.0 | 188 | 0.6443 | 0.6875 |
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- | 0.0447 | 95.0 | 190 | 0.6489 | 0.6875 |
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- | 0.0447 | 96.0 | 192 | 0.6534 | 0.6875 |
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- | 0.0447 | 97.0 | 194 | 0.6609 | 0.7031 |
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- | 0.0447 | 98.0 | 196 | 0.6689 | 0.7031 |
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- | 0.0447 | 99.0 | 198 | 0.6772 | 0.7188 |
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- | 0.0345 | 100.0 | 200 | 0.6875 | 0.7188 |
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- | 0.0345 | 101.0 | 202 | 0.6968 | 0.7188 |
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- | 0.0345 | 102.0 | 204 | 0.7052 | 0.7188 |
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- | 0.0345 | 103.0 | 206 | 0.7126 | 0.7188 |
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- | 0.0345 | 104.0 | 208 | 0.7181 | 0.7188 |
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- | 0.0274 | 105.0 | 210 | 0.7234 | 0.7188 |
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- | 0.0274 | 106.0 | 212 | 0.7282 | 0.7188 |
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- | 0.0274 | 107.0 | 214 | 0.7361 | 0.7188 |
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- | 0.0274 | 108.0 | 216 | 0.7440 | 0.7031 |
161
- | 0.0274 | 109.0 | 218 | 0.7539 | 0.7031 |
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- | 0.0218 | 110.0 | 220 | 0.7658 | 0.7031 |
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- | 0.0218 | 111.0 | 222 | 0.7772 | 0.7031 |
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- | 0.0218 | 112.0 | 224 | 0.7882 | 0.7031 |
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- | 0.0218 | 113.0 | 226 | 0.7977 | 0.7031 |
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- | 0.0218 | 114.0 | 228 | 0.8047 | 0.7031 |
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- | 0.0168 | 115.0 | 230 | 0.8103 | 0.7031 |
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- | 0.0168 | 116.0 | 232 | 0.8135 | 0.7031 |
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- | 0.0168 | 117.0 | 234 | 0.8157 | 0.7031 |
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- | 0.0168 | 118.0 | 236 | 0.8186 | 0.7031 |
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- | 0.0168 | 119.0 | 238 | 0.8213 | 0.7031 |
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- | 0.0138 | 120.0 | 240 | 0.8240 | 0.6875 |
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- | 0.0138 | 121.0 | 242 | 0.8262 | 0.6875 |
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- | 0.0138 | 122.0 | 244 | 0.8296 | 0.6875 |
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- | 0.0138 | 123.0 | 246 | 0.8339 | 0.6875 |
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- | 0.0138 | 124.0 | 248 | 0.8392 | 0.6875 |
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- | 0.0115 | 125.0 | 250 | 0.8451 | 0.6875 |
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- | 0.0115 | 126.0 | 252 | 0.8508 | 0.6875 |
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- | 0.0115 | 127.0 | 254 | 0.8574 | 0.6875 |
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- | 0.0115 | 128.0 | 256 | 0.8640 | 0.6875 |
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- | 0.0115 | 129.0 | 258 | 0.8707 | 0.6875 |
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- | 0.0096 | 130.0 | 260 | 0.8776 | 0.6875 |
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- | 0.0096 | 131.0 | 262 | 0.8845 | 0.6875 |
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- | 0.0096 | 132.0 | 264 | 0.8938 | 0.6875 |
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- | 0.0096 | 133.0 | 266 | 0.9021 | 0.6875 |
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- | 0.0096 | 134.0 | 268 | 0.9102 | 0.6875 |
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- | 0.0083 | 135.0 | 270 | 0.9181 | 0.6875 |
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- | 0.0083 | 136.0 | 272 | 0.9260 | 0.6875 |
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- | 0.0083 | 137.0 | 274 | 0.9338 | 0.6875 |
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- | 0.0083 | 138.0 | 276 | 0.9416 | 0.6875 |
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- | 0.0083 | 139.0 | 278 | 0.9488 | 0.6875 |
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- | 0.0071 | 140.0 | 280 | 0.9566 | 0.6875 |
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- | 0.0071 | 141.0 | 282 | 0.9635 | 0.6875 |
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- | 0.0071 | 142.0 | 284 | 0.9707 | 0.6875 |
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- | 0.0071 | 143.0 | 286 | 0.9769 | 0.6875 |
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- | 0.0071 | 144.0 | 288 | 0.9825 | 0.6875 |
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- | 0.0061 | 145.0 | 290 | 0.9878 | 0.6875 |
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- | 0.0061 | 146.0 | 292 | 0.9926 | 0.6875 |
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- | 0.0061 | 147.0 | 294 | 0.9962 | 0.6875 |
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- | 0.0061 | 148.0 | 296 | 1.0002 | 0.6875 |
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- | 0.0061 | 149.0 | 298 | 1.0036 | 0.6875 |
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- | 0.0054 | 150.0 | 300 | 1.0073 | 0.6875 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.2039
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+ - Accuracy: 0.8281
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 2 | 0.7856 | 0.8125 |
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+ | No log | 2.0 | 4 | 0.7856 | 0.8125 |
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+ | No log | 3.0 | 6 | 0.7857 | 0.8125 |
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+ | No log | 4.0 | 8 | 0.7862 | 0.8125 |
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+ | 0.5036 | 5.0 | 10 | 0.7867 | 0.8125 |
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+ | 0.5036 | 6.0 | 12 | 0.7873 | 0.8125 |
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+ | 0.5036 | 7.0 | 14 | 0.7883 | 0.8125 |
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+ | 0.5036 | 8.0 | 16 | 0.7908 | 0.8125 |
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+ | 0.5036 | 9.0 | 18 | 0.7955 | 0.8281 |
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+ | 0.4185 | 10.0 | 20 | 0.8014 | 0.8125 |
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+ | 0.4185 | 11.0 | 22 | 0.8066 | 0.8125 |
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+ | 0.4185 | 12.0 | 24 | 0.8128 | 0.8125 |
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+ | 0.4185 | 13.0 | 26 | 0.8208 | 0.8125 |
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+ | 0.4185 | 14.0 | 28 | 0.8292 | 0.8125 |
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+ | 0.2904 | 15.0 | 30 | 0.8390 | 0.8281 |
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+ | 0.2904 | 16.0 | 32 | 0.8441 | 0.8125 |
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+ | 0.2904 | 17.0 | 34 | 0.8451 | 0.8125 |
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+ | 0.2904 | 18.0 | 36 | 0.8484 | 0.8125 |
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+ | 0.2904 | 19.0 | 38 | 0.8510 | 0.8125 |
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+ | 0.257 | 20.0 | 40 | 0.8506 | 0.8125 |
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+ | 0.257 | 21.0 | 42 | 0.8471 | 0.8125 |
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+ | 0.257 | 22.0 | 44 | 0.8397 | 0.8125 |
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+ | 0.257 | 23.0 | 46 | 0.8311 | 0.8281 |
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+ | 0.257 | 24.0 | 48 | 0.8248 | 0.8281 |
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+ | 0.2216 | 25.0 | 50 | 0.8175 | 0.8281 |
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+ | 0.2216 | 26.0 | 52 | 0.8108 | 0.8281 |
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+ | 0.2216 | 27.0 | 54 | 0.8012 | 0.8281 |
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+ | 0.2216 | 28.0 | 56 | 0.7907 | 0.8281 |
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+ | 0.2216 | 29.0 | 58 | 0.7851 | 0.8281 |
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+ | 0.1811 | 30.0 | 60 | 0.7800 | 0.8281 |
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+ | 0.1811 | 31.0 | 62 | 0.7713 | 0.8281 |
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+ | 0.1811 | 32.0 | 64 | 0.7620 | 0.8281 |
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+ | 0.1811 | 33.0 | 66 | 0.7502 | 0.8125 |
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+ | 0.1811 | 34.0 | 68 | 0.7386 | 0.8125 |
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+ | 0.1015 | 35.0 | 70 | 0.7320 | 0.8125 |
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+ | 0.1015 | 36.0 | 72 | 0.7296 | 0.8125 |
89
+ | 0.1015 | 37.0 | 74 | 0.7315 | 0.8125 |
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+ | 0.1015 | 38.0 | 76 | 0.7371 | 0.8281 |
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+ | 0.1015 | 39.0 | 78 | 0.7442 | 0.8281 |
92
+ | 0.0725 | 40.0 | 80 | 0.7475 | 0.8281 |
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+ | 0.0725 | 41.0 | 82 | 0.7474 | 0.8281 |
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+ | 0.0725 | 42.0 | 84 | 0.7479 | 0.8281 |
95
+ | 0.0725 | 43.0 | 86 | 0.7501 | 0.8281 |
96
+ | 0.0725 | 44.0 | 88 | 0.7523 | 0.8281 |
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+ | 0.0249 | 45.0 | 90 | 0.7491 | 0.8281 |
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+ | 0.0249 | 46.0 | 92 | 0.7537 | 0.8281 |
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+ | 0.0249 | 47.0 | 94 | 0.7615 | 0.8281 |
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+ | 0.0249 | 48.0 | 96 | 0.7767 | 0.8281 |
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+ | 0.0249 | 49.0 | 98 | 0.7909 | 0.8281 |
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+ | 0.0071 | 50.0 | 100 | 0.8011 | 0.8281 |
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+ | 0.0071 | 51.0 | 102 | 0.8145 | 0.8281 |
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+ | 0.0071 | 52.0 | 104 | 0.8286 | 0.8281 |
105
+ | 0.0071 | 53.0 | 106 | 0.8415 | 0.8281 |
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+ | 0.0071 | 54.0 | 108 | 0.8451 | 0.8281 |
107
+ | 0.0057 | 55.0 | 110 | 0.8438 | 0.8281 |
108
+ | 0.0057 | 56.0 | 112 | 0.8368 | 0.8281 |
109
+ | 0.0057 | 57.0 | 114 | 0.8340 | 0.8281 |
110
+ | 0.0057 | 58.0 | 116 | 0.8431 | 0.8281 |
111
+ | 0.0057 | 59.0 | 118 | 0.8509 | 0.8281 |
112
+ | 0.0052 | 60.0 | 120 | 0.8579 | 0.8281 |
113
+ | 0.0052 | 61.0 | 122 | 0.8640 | 0.8281 |
114
+ | 0.0052 | 62.0 | 124 | 0.8691 | 0.8281 |
115
+ | 0.0052 | 63.0 | 126 | 0.8733 | 0.8281 |
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+ | 0.0052 | 64.0 | 128 | 0.8767 | 0.8281 |
117
+ | 0.0027 | 65.0 | 130 | 0.8800 | 0.8281 |
118
+ | 0.0027 | 66.0 | 132 | 0.8826 | 0.8281 |
119
+ | 0.0027 | 67.0 | 134 | 0.8865 | 0.8281 |
120
+ | 0.0027 | 68.0 | 136 | 0.8929 | 0.8281 |
121
+ | 0.0027 | 69.0 | 138 | 0.9006 | 0.8281 |
122
+ | 0.0025 | 70.0 | 140 | 0.9079 | 0.8281 |
123
+ | 0.0025 | 71.0 | 142 | 0.9226 | 0.8281 |
124
+ | 0.0025 | 72.0 | 144 | 0.9417 | 0.8281 |
125
+ | 0.0025 | 73.0 | 146 | 0.9560 | 0.8281 |
126
+ | 0.0025 | 74.0 | 148 | 0.9663 | 0.8281 |
127
+ | 0.0028 | 75.0 | 150 | 0.9737 | 0.8125 |
128
+ | 0.0028 | 76.0 | 152 | 0.9761 | 0.8281 |
129
+ | 0.0028 | 77.0 | 154 | 0.9724 | 0.8281 |
130
+ | 0.0028 | 78.0 | 156 | 0.9675 | 0.8281 |
131
+ | 0.0028 | 79.0 | 158 | 0.9602 | 0.8281 |
132
+ | 0.0029 | 80.0 | 160 | 0.9534 | 0.8281 |
133
+ | 0.0029 | 81.0 | 162 | 0.9478 | 0.8281 |
134
+ | 0.0029 | 82.0 | 164 | 0.9437 | 0.8281 |
135
+ | 0.0029 | 83.0 | 166 | 0.9400 | 0.8281 |
136
+ | 0.0029 | 84.0 | 168 | 0.9366 | 0.8281 |
137
+ | 0.0016 | 85.0 | 170 | 0.9346 | 0.8281 |
138
+ | 0.0016 | 86.0 | 172 | 0.9343 | 0.8281 |
139
+ | 0.0016 | 87.0 | 174 | 0.9353 | 0.8281 |
140
+ | 0.0016 | 88.0 | 176 | 0.9367 | 0.8281 |
141
+ | 0.0016 | 89.0 | 178 | 0.9386 | 0.8281 |
142
+ | 0.0015 | 90.0 | 180 | 0.9413 | 0.8281 |
143
+ | 0.0015 | 91.0 | 182 | 0.9439 | 0.8281 |
144
+ | 0.0015 | 92.0 | 184 | 0.9472 | 0.8281 |
145
+ | 0.0015 | 93.0 | 186 | 0.9510 | 0.8281 |
146
+ | 0.0015 | 94.0 | 188 | 0.9552 | 0.8281 |
147
+ | 0.0013 | 95.0 | 190 | 0.9596 | 0.8281 |
148
+ | 0.0013 | 96.0 | 192 | 0.9641 | 0.8281 |
149
+ | 0.0013 | 97.0 | 194 | 0.9684 | 0.8281 |
150
+ | 0.0013 | 98.0 | 196 | 0.9725 | 0.8281 |
151
+ | 0.0013 | 99.0 | 198 | 0.9777 | 0.8281 |
152
+ | 0.0012 | 100.0 | 200 | 0.9881 | 0.8281 |
153
+ | 0.0012 | 101.0 | 202 | 0.9981 | 0.8281 |
154
+ | 0.0012 | 102.0 | 204 | 1.0066 | 0.8281 |
155
+ | 0.0012 | 103.0 | 206 | 1.0043 | 0.8281 |
156
+ | 0.0012 | 104.0 | 208 | 1.0029 | 0.8281 |
157
+ | 0.0011 | 105.0 | 210 | 1.0022 | 0.8281 |
158
+ | 0.0011 | 106.0 | 212 | 1.0017 | 0.8281 |
159
+ | 0.0011 | 107.0 | 214 | 1.0021 | 0.8281 |
160
+ | 0.0011 | 108.0 | 216 | 1.0029 | 0.8281 |
161
+ | 0.0011 | 109.0 | 218 | 1.0048 | 0.8281 |
162
+ | 0.001 | 110.0 | 220 | 1.0069 | 0.8281 |
163
+ | 0.001 | 111.0 | 222 | 1.0114 | 0.8281 |
164
+ | 0.001 | 112.0 | 224 | 1.0171 | 0.8281 |
165
+ | 0.001 | 113.0 | 226 | 1.0225 | 0.8281 |
166
+ | 0.001 | 114.0 | 228 | 1.0273 | 0.8281 |
167
+ | 0.0009 | 115.0 | 230 | 1.0325 | 0.8281 |
168
+ | 0.0009 | 116.0 | 232 | 1.0375 | 0.8281 |
169
+ | 0.0009 | 117.0 | 234 | 1.0419 | 0.8281 |
170
+ | 0.0009 | 118.0 | 236 | 1.0460 | 0.8281 |
171
+ | 0.0009 | 119.0 | 238 | 1.0500 | 0.8281 |
172
+ | 0.0009 | 120.0 | 240 | 1.0538 | 0.8281 |
173
+ | 0.0009 | 121.0 | 242 | 1.0572 | 0.8281 |
174
+ | 0.0009 | 122.0 | 244 | 1.0611 | 0.8281 |
175
+ | 0.0009 | 123.0 | 246 | 1.0650 | 0.8281 |
176
+ | 0.0009 | 124.0 | 248 | 1.0664 | 0.8281 |
177
+ | 0.0015 | 125.0 | 250 | 1.1047 | 0.8281 |
178
+ | 0.0015 | 126.0 | 252 | 1.1348 | 0.8281 |
179
+ | 0.0015 | 127.0 | 254 | 1.1568 | 0.8125 |
180
+ | 0.0015 | 128.0 | 256 | 1.1730 | 0.8125 |
181
+ | 0.0015 | 129.0 | 258 | 1.1849 | 0.8125 |
182
+ | 0.0007 | 130.0 | 260 | 1.1937 | 0.8125 |
183
+ | 0.0007 | 131.0 | 262 | 1.2006 | 0.8125 |
184
+ | 0.0007 | 132.0 | 264 | 1.2057 | 0.8125 |
185
+ | 0.0007 | 133.0 | 266 | 1.2096 | 0.8125 |
186
+ | 0.0007 | 134.0 | 268 | 1.2120 | 0.8125 |
187
+ | 0.0007 | 135.0 | 270 | 1.2140 | 0.8125 |
188
+ | 0.0007 | 136.0 | 272 | 1.2134 | 0.8125 |
189
+ | 0.0007 | 137.0 | 274 | 1.2122 | 0.8125 |
190
+ | 0.0007 | 138.0 | 276 | 1.2105 | 0.8125 |
191
+ | 0.0007 | 139.0 | 278 | 1.2089 | 0.8125 |
192
+ | 0.0006 | 140.0 | 280 | 1.2075 | 0.8125 |
193
+ | 0.0006 | 141.0 | 282 | 1.2063 | 0.8125 |
194
+ | 0.0006 | 142.0 | 284 | 1.2054 | 0.8125 |
195
+ | 0.0006 | 143.0 | 286 | 1.2049 | 0.8125 |
196
+ | 0.0006 | 144.0 | 288 | 1.2039 | 0.8125 |
197
+ | 0.0005 | 145.0 | 290 | 1.2032 | 0.8281 |
198
+ | 0.0005 | 146.0 | 292 | 1.2029 | 0.8281 |
199
+ | 0.0005 | 147.0 | 294 | 1.2028 | 0.8281 |
200
+ | 0.0005 | 148.0 | 296 | 1.2029 | 0.8281 |
201
+ | 0.0005 | 149.0 | 298 | 1.2032 | 0.8281 |
202
+ | 0.0005 | 150.0 | 300 | 1.2039 | 0.8281 |
203
 
204
 
205
  ### Framework versions