update model card README.md
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README.md
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---
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license:
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base_model:
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tags:
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- generated_from_trainer
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metrics:
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# best_model-yelp_polarity-32-42
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This model is a fine-tuned version of [
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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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.
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| No log | 2.0 | 4 | 0.
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| No log | 3.0 | 6 | 0.
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| No log | 4.0 | 8 | 0.
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| 0.0 | 30.0 | 60 | 0.
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| 0.0 | 31.0 | 62 | 0.
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| 84 |
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| 0.0 | 32.0 | 64 | 0.
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| 85 |
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| 0.0 | 33.0 | 66 | 0.
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| 86 |
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| 0.0 | 34.0 | 68 | 0.
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| 0.
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| 0.0 | 40.0 | 80 | 0.
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| 0.0 | 41.0 | 82 | 0.
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| 0.0 | 42.0 | 84 | 0.
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| 0.0 | 43.0 | 86 | 0.
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| 96 |
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| 0.0 | 44.0 | 88 | 0.
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| 97 |
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| 0.0 | 45.0 | 90 | 0.
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| 98 |
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| 0.0 | 46.0 | 92 | 0.
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| 99 |
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| 0.0 | 47.0 | 94 | 0.
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| 0.0 | 48.0 | 96 | 0.
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| 0.0 | 49.0 | 98 | 0.
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| 0.0 | 50.0 | 100 | 0.
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| 0.0 | 51.0 | 102 | 0.
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| 0.0 | 52.0 | 104 | 0.
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| 0.0 | 53.0 | 106 | 0.
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| 0.0 | 54.0 | 108 | 0.
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| 107 |
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| 0.0 | 55.0 | 110 | 0.
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| 108 |
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| 0.0 | 56.0 | 112 | 0.
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| 109 |
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| 0.0 | 57.0 | 114 | 0.
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| 110 |
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| 0.0 | 58.0 | 116 | 0.
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| 111 |
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| 0.0 | 59.0 | 118 | 0.
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| 112 |
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| 0.0 | 60.0 | 120 | 0.
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| 113 |
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| 0.0 | 61.0 | 122 | 0.
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| 114 |
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| 0.0 | 62.0 | 124 | 0.
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| 0.0 | 63.0 | 126 | 0.
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| 0.0 | 64.0 | 128 | 0.
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| 0.0 | 65.0 | 130 | 0.
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| 0.0 | 66.0 | 132 | 0.
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| 119 |
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| 0.0 | 67.0 | 134 | 0.
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| 120 |
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| 0.0 | 68.0 | 136 | 0.
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| 121 |
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| 0.0 | 69.0 | 138 | 0.
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| 122 |
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| 0.0 | 70.0 | 140 | 0.
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| 123 |
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| 0.0 | 71.0 | 142 | 0.
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| 0.0 | 72.0 | 144 | 0.
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| 0.0 | 73.0 | 146 | 0.
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| 126 |
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| 0.0 | 74.0 | 148 | 0.
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| 0.0 | 75.0 | 150 | 0.
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| 128 |
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| 0.0 | 76.0 | 152 | 0.
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| 0.0 | 77.0 | 154 | 0.
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| 0.0 | 78.0 | 156 | 0.
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| 0.0 | 79.0 | 158 | 0.
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| 132 |
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| 0.0 | 80.0 | 160 | 0.
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| 133 |
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| 0.0 | 81.0 | 162 | 0.
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| 0.0 | 82.0 | 164 | 0.
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| 0.0 | 83.0 | 166 | 0.
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| 0.0 | 84.0 | 168 | 0.
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| 0.0 | 85.0 | 170 | 0.
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| 0.0 | 86.0 | 172 | 0.
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| 0.0 | 87.0 | 174 | 0.
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| 0.0 | 88.0 | 176 | 0.
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| 141 |
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| 0.0 | 89.0 | 178 | 0.
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| 142 |
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| 0.0 | 90.0 | 180 | 0.
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| 143 |
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| 0.0 | 91.0 | 182 | 0.
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| 144 |
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| 0.0 | 92.0 | 184 | 0.
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| 0.0 | 93.0 | 186 | 0.
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| 146 |
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| 0.0 | 94.0 | 188 | 0.
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| 0.0 | 95.0 | 190 | 0.
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| 148 |
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| 0.0 | 96.0 | 192 | 0.
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| 0.0 | 97.0 | 194 | 0.
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| 0.0 | 98.0 | 196 | 0.
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| 0.0 | 99.0 | 198 | 0.
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| 0.0 | 100.0 | 200 | 0.
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| 0.0 | 101.0 | 202 | 0.
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| 0.0 | 102.0 | 204 | 0.
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| 0.0 | 103.0 | 206 | 0.
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| 0.0 | 104.0 | 208 | 0.
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| 0.0 | 105.0 | 210 | 0.
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| 0.0 | 106.0 | 212 | 0.
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| 0.0 | 107.0 | 214 | 0.
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| 160 |
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| 0.0 | 108.0 | 216 | 0.
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| 161 |
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| 0.0 | 109.0 | 218 | 0.
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| 162 |
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| 0.0 | 110.0 | 220 | 0.
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| 163 |
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| 0.0 | 111.0 | 222 | 0.
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| 164 |
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| 0.0 | 112.0 | 224 | 0.
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| 0.0 | 113.0 | 226 | 0.
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| 166 |
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| 0.0 | 114.0 | 228 | 0.
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| 0.0 | 115.0 | 230 | 0.
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| 168 |
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| 0.0 | 116.0 | 232 | 0.
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| 169 |
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| 0.0 | 117.0 | 234 | 0.
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| 170 |
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| 0.0 | 118.0 | 236 | 0.
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| 171 |
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| 0.0 | 119.0 | 238 | 0.
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| 172 |
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| 0.0 | 120.0 | 240 | 0.
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| 173 |
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| 0.0 | 121.0 | 242 | 0.
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| 174 |
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| 0.0 | 122.0 | 244 | 0.
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| 175 |
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| 0.0 | 123.0 | 246 | 0.
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| 176 |
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| 0.0 | 124.0 | 248 | 0.
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| 0.0 | 125.0 | 250 | 0.
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| 0.0 | 126.0 | 252 | 0.
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| 0.0 | 127.0 | 254 | 0.
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| 0.0 | 128.0 | 256 | 0.
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| 0.0 | 129.0 | 258 | 0.
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| 0.0 | 130.0 | 260 | 0.
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| 0.0 | 131.0 | 262 | 0.
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| 184 |
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| 0.0 | 132.0 | 264 | 0.
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| 0.0 | 133.0 | 266 | 0.
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| 0.0 | 134.0 | 268 | 0.
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| 0.0 | 135.0 | 270 | 0.
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| 0.0 | 136.0 | 272 | 0.
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| 0.0 | 137.0 | 274 | 0.
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| 0.0 | 138.0 | 276 | 0.
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| 0.0 | 139.0 | 278 | 0.
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| 0.0 | 140.0 | 280 | 0.
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| 0.0 | 141.0 | 282 | 0.
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| 0.0 | 142.0 | 284 | 0.
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| 195 |
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| 0.0 | 143.0 | 286 | 0.
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| 0.0 | 144.0 | 288 | 0.
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| 0.0 | 145.0 | 290 | 0.
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| 0.0 | 146.0 | 292 | 0.
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| 0.0 | 147.0 | 294 | 0.
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| 0.0 | 148.0 | 296 | 0.
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| 0.0 | 149.0 | 298 | 0.
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| 0.0 | 150.0 | 300 | 0.
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### Framework versions
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---
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license: apache-2.0
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base_model: albert-base-v2
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tags:
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- generated_from_trainer
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metrics:
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# best_model-yelp_polarity-32-42
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This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5674
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- Accuracy: 0.9375
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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 | 0.5344 | 0.9219 |
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| No log | 2.0 | 4 | 0.5340 | 0.9219 |
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| 55 |
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| No log | 3.0 | 6 | 0.5330 | 0.9219 |
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| No log | 4.0 | 8 | 0.5309 | 0.9219 |
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| 0.4102 | 5.0 | 10 | 0.5192 | 0.9219 |
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| 0.4102 | 6.0 | 12 | 0.5130 | 0.9219 |
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| 0.4102 | 7.0 | 14 | 0.4996 | 0.9219 |
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| 0.4102 | 8.0 | 16 | 0.4697 | 0.9375 |
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| 0.4102 | 9.0 | 18 | 0.4622 | 0.9375 |
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| 0.2817 | 10.0 | 20 | 0.4615 | 0.9375 |
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| 63 |
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| 0.2817 | 11.0 | 22 | 0.4620 | 0.9375 |
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| 64 |
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| 0.2817 | 12.0 | 24 | 0.4612 | 0.9375 |
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| 0.2817 | 13.0 | 26 | 0.4623 | 0.9375 |
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| 0.2817 | 14.0 | 28 | 0.5263 | 0.9219 |
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| 0.064 | 15.0 | 30 | 0.5614 | 0.9219 |
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| 0.064 | 16.0 | 32 | 0.5627 | 0.9219 |
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| 0.064 | 17.0 | 34 | 0.5183 | 0.9219 |
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| 0.064 | 18.0 | 36 | 0.4753 | 0.9375 |
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| 0.064 | 19.0 | 38 | 0.4826 | 0.9375 |
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| 0.002 | 20.0 | 40 | 0.4912 | 0.9375 |
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| 0.002 | 21.0 | 42 | 0.5235 | 0.9219 |
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| 0.002 | 22.0 | 44 | 0.5333 | 0.9219 |
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| 0.002 | 23.0 | 46 | 0.5318 | 0.9219 |
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| 0.002 | 24.0 | 48 | 0.5192 | 0.9219 |
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| 0.0001 | 25.0 | 50 | 0.5060 | 0.9375 |
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| 0.0001 | 26.0 | 52 | 0.4997 | 0.9375 |
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| 0.0001 | 27.0 | 54 | 0.4982 | 0.9375 |
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| 0.0001 | 28.0 | 56 | 0.4982 | 0.9375 |
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| 0.0001 | 29.0 | 58 | 0.4984 | 0.9375 |
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| 0.0 | 30.0 | 60 | 0.4987 | 0.9375 |
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| 0.0 | 31.0 | 62 | 0.4989 | 0.9375 |
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| 0.0 | 32.0 | 64 | 0.4992 | 0.9375 |
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| 0.0 | 33.0 | 66 | 0.4994 | 0.9375 |
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| 86 |
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| 0.0 | 34.0 | 68 | 0.4997 | 0.9375 |
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| 0.0 | 35.0 | 70 | 0.4999 | 0.9375 |
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| 0.0 | 36.0 | 72 | 0.5002 | 0.9375 |
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| 89 |
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| 0.0 | 37.0 | 74 | 0.5005 | 0.9375 |
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| 0.0 | 38.0 | 76 | 0.5009 | 0.9375 |
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| 0.0 | 39.0 | 78 | 0.5012 | 0.9375 |
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| 0.0 | 40.0 | 80 | 0.5016 | 0.9375 |
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| 0.0 | 41.0 | 82 | 0.5020 | 0.9375 |
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| 0.0 | 42.0 | 84 | 0.5024 | 0.9375 |
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| 0.0 | 43.0 | 86 | 0.5029 | 0.9375 |
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| 96 |
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| 0.0 | 44.0 | 88 | 0.5033 | 0.9375 |
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| 97 |
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| 0.0 | 45.0 | 90 | 0.5038 | 0.9375 |
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| 0.0 | 46.0 | 92 | 0.5043 | 0.9375 |
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| 0.0 | 47.0 | 94 | 0.5047 | 0.9375 |
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| 100 |
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| 0.0 | 48.0 | 96 | 0.5052 | 0.9375 |
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| 101 |
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| 0.0 | 49.0 | 98 | 0.5057 | 0.9375 |
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| 0.0 | 50.0 | 100 | 0.5062 | 0.9375 |
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| 0.0 | 51.0 | 102 | 0.5068 | 0.9375 |
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| 104 |
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| 0.0 | 52.0 | 104 | 0.5073 | 0.9375 |
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| 105 |
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| 0.0 | 53.0 | 106 | 0.5078 | 0.9375 |
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| 0.0 | 54.0 | 108 | 0.5083 | 0.9375 |
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| 107 |
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| 0.0 | 55.0 | 110 | 0.5089 | 0.9375 |
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| 108 |
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| 0.0 | 56.0 | 112 | 0.5094 | 0.9375 |
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| 109 |
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| 0.0 | 57.0 | 114 | 0.5100 | 0.9375 |
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| 110 |
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| 0.0 | 58.0 | 116 | 0.5105 | 0.9375 |
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| 111 |
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| 0.0 | 59.0 | 118 | 0.5110 | 0.9375 |
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| 112 |
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| 0.0 | 60.0 | 120 | 0.5116 | 0.9375 |
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| 113 |
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| 0.0 | 61.0 | 122 | 0.5122 | 0.9375 |
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| 114 |
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| 0.0 | 62.0 | 124 | 0.5128 | 0.9375 |
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| 115 |
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| 0.0 | 63.0 | 126 | 0.5133 | 0.9375 |
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| 116 |
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| 0.0 | 64.0 | 128 | 0.5139 | 0.9375 |
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| 117 |
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| 0.0 | 65.0 | 130 | 0.5145 | 0.9375 |
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| 118 |
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| 0.0 | 66.0 | 132 | 0.5150 | 0.9375 |
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| 119 |
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| 0.0 | 67.0 | 134 | 0.5156 | 0.9375 |
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| 120 |
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| 0.0 | 68.0 | 136 | 0.5161 | 0.9375 |
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| 121 |
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| 0.0 | 69.0 | 138 | 0.5166 | 0.9375 |
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| 122 |
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| 0.0 | 70.0 | 140 | 0.5172 | 0.9375 |
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| 123 |
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| 0.0 | 71.0 | 142 | 0.5177 | 0.9375 |
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| 0.0 | 72.0 | 144 | 0.5182 | 0.9375 |
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| 0.0 | 73.0 | 146 | 0.5188 | 0.9375 |
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| 126 |
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| 0.0 | 74.0 | 148 | 0.5193 | 0.9375 |
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| 127 |
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| 0.0 | 75.0 | 150 | 0.5199 | 0.9375 |
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| 128 |
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| 0.0 | 76.0 | 152 | 0.5204 | 0.9375 |
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| 129 |
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| 0.0 | 77.0 | 154 | 0.5210 | 0.9375 |
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| 0.0 | 78.0 | 156 | 0.5216 | 0.9375 |
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| 0.0 | 79.0 | 158 | 0.5222 | 0.9375 |
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| 0.0 | 80.0 | 160 | 0.5228 | 0.9375 |
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| 0.0 | 81.0 | 162 | 0.5233 | 0.9375 |
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| 0.0 | 82.0 | 164 | 0.5239 | 0.9375 |
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| 135 |
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| 0.0 | 83.0 | 166 | 0.5245 | 0.9375 |
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| 136 |
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| 0.0 | 84.0 | 168 | 0.5251 | 0.9375 |
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| 137 |
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| 0.0 | 85.0 | 170 | 0.5257 | 0.9375 |
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| 138 |
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| 0.0 | 86.0 | 172 | 0.5263 | 0.9375 |
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| 139 |
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| 0.0 | 87.0 | 174 | 0.5269 | 0.9375 |
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| 140 |
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| 0.0 | 88.0 | 176 | 0.5275 | 0.9375 |
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| 141 |
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| 0.0 | 89.0 | 178 | 0.5281 | 0.9375 |
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| 142 |
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| 0.0 | 90.0 | 180 | 0.5288 | 0.9375 |
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| 143 |
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| 0.0 | 91.0 | 182 | 0.5294 | 0.9375 |
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| 144 |
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| 0.0 | 92.0 | 184 | 0.5300 | 0.9375 |
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| 145 |
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| 0.0 | 93.0 | 186 | 0.5306 | 0.9375 |
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| 146 |
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| 0.0 | 94.0 | 188 | 0.5312 | 0.9375 |
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| 147 |
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| 0.0 | 95.0 | 190 | 0.5318 | 0.9375 |
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| 148 |
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| 0.0 | 96.0 | 192 | 0.5325 | 0.9375 |
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| 149 |
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| 0.0 | 97.0 | 194 | 0.5331 | 0.9375 |
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| 150 |
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| 0.0 | 98.0 | 196 | 0.5337 | 0.9375 |
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| 151 |
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| 0.0 | 99.0 | 198 | 0.5343 | 0.9375 |
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| 152 |
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| 0.0 | 100.0 | 200 | 0.5349 | 0.9375 |
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| 153 |
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| 0.0 | 101.0 | 202 | 0.5355 | 0.9375 |
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| 154 |
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| 0.0 | 102.0 | 204 | 0.5362 | 0.9375 |
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| 155 |
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| 0.0 | 103.0 | 206 | 0.5368 | 0.9375 |
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| 156 |
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| 0.0 | 104.0 | 208 | 0.5374 | 0.9375 |
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| 157 |
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| 0.0 | 105.0 | 210 | 0.5381 | 0.9375 |
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| 158 |
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| 0.0 | 106.0 | 212 | 0.5387 | 0.9375 |
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| 159 |
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| 0.0 | 107.0 | 214 | 0.5394 | 0.9375 |
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| 160 |
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| 0.0 | 108.0 | 216 | 0.5400 | 0.9375 |
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| 161 |
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| 0.0 | 109.0 | 218 | 0.5407 | 0.9375 |
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| 162 |
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| 0.0 | 110.0 | 220 | 0.5413 | 0.9375 |
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| 163 |
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| 0.0 | 111.0 | 222 | 0.5419 | 0.9375 |
|
| 164 |
+
| 0.0 | 112.0 | 224 | 0.5425 | 0.9375 |
|
| 165 |
+
| 0.0 | 113.0 | 226 | 0.5432 | 0.9375 |
|
| 166 |
+
| 0.0 | 114.0 | 228 | 0.5438 | 0.9375 |
|
| 167 |
+
| 0.0 | 115.0 | 230 | 0.5444 | 0.9375 |
|
| 168 |
+
| 0.0 | 116.0 | 232 | 0.5450 | 0.9375 |
|
| 169 |
+
| 0.0 | 117.0 | 234 | 0.5457 | 0.9375 |
|
| 170 |
+
| 0.0 | 118.0 | 236 | 0.5463 | 0.9375 |
|
| 171 |
+
| 0.0 | 119.0 | 238 | 0.5469 | 0.9375 |
|
| 172 |
+
| 0.0 | 120.0 | 240 | 0.5476 | 0.9375 |
|
| 173 |
+
| 0.0 | 121.0 | 242 | 0.5482 | 0.9375 |
|
| 174 |
+
| 0.0 | 122.0 | 244 | 0.5489 | 0.9375 |
|
| 175 |
+
| 0.0 | 123.0 | 246 | 0.5495 | 0.9375 |
|
| 176 |
+
| 0.0 | 124.0 | 248 | 0.5502 | 0.9375 |
|
| 177 |
+
| 0.0 | 125.0 | 250 | 0.5509 | 0.9375 |
|
| 178 |
+
| 0.0 | 126.0 | 252 | 0.5516 | 0.9375 |
|
| 179 |
+
| 0.0 | 127.0 | 254 | 0.5522 | 0.9375 |
|
| 180 |
+
| 0.0 | 128.0 | 256 | 0.5529 | 0.9375 |
|
| 181 |
+
| 0.0 | 129.0 | 258 | 0.5536 | 0.9375 |
|
| 182 |
+
| 0.0 | 130.0 | 260 | 0.5543 | 0.9375 |
|
| 183 |
+
| 0.0 | 131.0 | 262 | 0.5549 | 0.9375 |
|
| 184 |
+
| 0.0 | 132.0 | 264 | 0.5556 | 0.9375 |
|
| 185 |
+
| 0.0 | 133.0 | 266 | 0.5563 | 0.9375 |
|
| 186 |
+
| 0.0 | 134.0 | 268 | 0.5570 | 0.9375 |
|
| 187 |
+
| 0.0 | 135.0 | 270 | 0.5576 | 0.9375 |
|
| 188 |
+
| 0.0 | 136.0 | 272 | 0.5583 | 0.9375 |
|
| 189 |
+
| 0.0 | 137.0 | 274 | 0.5590 | 0.9375 |
|
| 190 |
+
| 0.0 | 138.0 | 276 | 0.5597 | 0.9375 |
|
| 191 |
+
| 0.0 | 139.0 | 278 | 0.5603 | 0.9375 |
|
| 192 |
+
| 0.0 | 140.0 | 280 | 0.5610 | 0.9375 |
|
| 193 |
+
| 0.0 | 141.0 | 282 | 0.5616 | 0.9375 |
|
| 194 |
+
| 0.0 | 142.0 | 284 | 0.5623 | 0.9375 |
|
| 195 |
+
| 0.0 | 143.0 | 286 | 0.5629 | 0.9375 |
|
| 196 |
+
| 0.0 | 144.0 | 288 | 0.5635 | 0.9375 |
|
| 197 |
+
| 0.0 | 145.0 | 290 | 0.5642 | 0.9375 |
|
| 198 |
+
| 0.0 | 146.0 | 292 | 0.5648 | 0.9375 |
|
| 199 |
+
| 0.0 | 147.0 | 294 | 0.5655 | 0.9375 |
|
| 200 |
+
| 0.0 | 148.0 | 296 | 0.5661 | 0.9375 |
|
| 201 |
+
| 0.0 | 149.0 | 298 | 0.5667 | 0.9375 |
|
| 202 |
+
| 0.0 | 150.0 | 300 | 0.5674 | 0.9375 |
|
| 203 |
|
| 204 |
|
| 205 |
### Framework versions
|