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best_model-sst-2-32-13

Text Classification
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PyTorch
bert
Generated from Trainer
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metadata
license: apache-2.0
base_model: bert-base-uncased
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: best_model-sst-2-32-13
    results: []

best_model-sst-2-32-13

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 1.2039
  • Accuracy: 0.8281

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 150

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 2 0.7856 0.8125
No log 2.0 4 0.7856 0.8125
No log 3.0 6 0.7857 0.8125
No log 4.0 8 0.7862 0.8125
0.5036 5.0 10 0.7867 0.8125
0.5036 6.0 12 0.7873 0.8125
0.5036 7.0 14 0.7883 0.8125
0.5036 8.0 16 0.7908 0.8125
0.5036 9.0 18 0.7955 0.8281
0.4185 10.0 20 0.8014 0.8125
0.4185 11.0 22 0.8066 0.8125
0.4185 12.0 24 0.8128 0.8125
0.4185 13.0 26 0.8208 0.8125
0.4185 14.0 28 0.8292 0.8125
0.2904 15.0 30 0.8390 0.8281
0.2904 16.0 32 0.8441 0.8125
0.2904 17.0 34 0.8451 0.8125
0.2904 18.0 36 0.8484 0.8125
0.2904 19.0 38 0.8510 0.8125
0.257 20.0 40 0.8506 0.8125
0.257 21.0 42 0.8471 0.8125
0.257 22.0 44 0.8397 0.8125
0.257 23.0 46 0.8311 0.8281
0.257 24.0 48 0.8248 0.8281
0.2216 25.0 50 0.8175 0.8281
0.2216 26.0 52 0.8108 0.8281
0.2216 27.0 54 0.8012 0.8281
0.2216 28.0 56 0.7907 0.8281
0.2216 29.0 58 0.7851 0.8281
0.1811 30.0 60 0.7800 0.8281
0.1811 31.0 62 0.7713 0.8281
0.1811 32.0 64 0.7620 0.8281
0.1811 33.0 66 0.7502 0.8125
0.1811 34.0 68 0.7386 0.8125
0.1015 35.0 70 0.7320 0.8125
0.1015 36.0 72 0.7296 0.8125
0.1015 37.0 74 0.7315 0.8125
0.1015 38.0 76 0.7371 0.8281
0.1015 39.0 78 0.7442 0.8281
0.0725 40.0 80 0.7475 0.8281
0.0725 41.0 82 0.7474 0.8281
0.0725 42.0 84 0.7479 0.8281
0.0725 43.0 86 0.7501 0.8281
0.0725 44.0 88 0.7523 0.8281
0.0249 45.0 90 0.7491 0.8281
0.0249 46.0 92 0.7537 0.8281
0.0249 47.0 94 0.7615 0.8281
0.0249 48.0 96 0.7767 0.8281
0.0249 49.0 98 0.7909 0.8281
0.0071 50.0 100 0.8011 0.8281
0.0071 51.0 102 0.8145 0.8281
0.0071 52.0 104 0.8286 0.8281
0.0071 53.0 106 0.8415 0.8281
0.0071 54.0 108 0.8451 0.8281
0.0057 55.0 110 0.8438 0.8281
0.0057 56.0 112 0.8368 0.8281
0.0057 57.0 114 0.8340 0.8281
0.0057 58.0 116 0.8431 0.8281
0.0057 59.0 118 0.8509 0.8281
0.0052 60.0 120 0.8579 0.8281
0.0052 61.0 122 0.8640 0.8281
0.0052 62.0 124 0.8691 0.8281
0.0052 63.0 126 0.8733 0.8281
0.0052 64.0 128 0.8767 0.8281
0.0027 65.0 130 0.8800 0.8281
0.0027 66.0 132 0.8826 0.8281
0.0027 67.0 134 0.8865 0.8281
0.0027 68.0 136 0.8929 0.8281
0.0027 69.0 138 0.9006 0.8281
0.0025 70.0 140 0.9079 0.8281
0.0025 71.0 142 0.9226 0.8281
0.0025 72.0 144 0.9417 0.8281
0.0025 73.0 146 0.9560 0.8281
0.0025 74.0 148 0.9663 0.8281
0.0028 75.0 150 0.9737 0.8125
0.0028 76.0 152 0.9761 0.8281
0.0028 77.0 154 0.9724 0.8281
0.0028 78.0 156 0.9675 0.8281
0.0028 79.0 158 0.9602 0.8281
0.0029 80.0 160 0.9534 0.8281
0.0029 81.0 162 0.9478 0.8281
0.0029 82.0 164 0.9437 0.8281
0.0029 83.0 166 0.9400 0.8281
0.0029 84.0 168 0.9366 0.8281
0.0016 85.0 170 0.9346 0.8281
0.0016 86.0 172 0.9343 0.8281
0.0016 87.0 174 0.9353 0.8281
0.0016 88.0 176 0.9367 0.8281
0.0016 89.0 178 0.9386 0.8281
0.0015 90.0 180 0.9413 0.8281
0.0015 91.0 182 0.9439 0.8281
0.0015 92.0 184 0.9472 0.8281
0.0015 93.0 186 0.9510 0.8281
0.0015 94.0 188 0.9552 0.8281
0.0013 95.0 190 0.9596 0.8281
0.0013 96.0 192 0.9641 0.8281
0.0013 97.0 194 0.9684 0.8281
0.0013 98.0 196 0.9725 0.8281
0.0013 99.0 198 0.9777 0.8281
0.0012 100.0 200 0.9881 0.8281
0.0012 101.0 202 0.9981 0.8281
0.0012 102.0 204 1.0066 0.8281
0.0012 103.0 206 1.0043 0.8281
0.0012 104.0 208 1.0029 0.8281
0.0011 105.0 210 1.0022 0.8281
0.0011 106.0 212 1.0017 0.8281
0.0011 107.0 214 1.0021 0.8281
0.0011 108.0 216 1.0029 0.8281
0.0011 109.0 218 1.0048 0.8281
0.001 110.0 220 1.0069 0.8281
0.001 111.0 222 1.0114 0.8281
0.001 112.0 224 1.0171 0.8281
0.001 113.0 226 1.0225 0.8281
0.001 114.0 228 1.0273 0.8281
0.0009 115.0 230 1.0325 0.8281
0.0009 116.0 232 1.0375 0.8281
0.0009 117.0 234 1.0419 0.8281
0.0009 118.0 236 1.0460 0.8281
0.0009 119.0 238 1.0500 0.8281
0.0009 120.0 240 1.0538 0.8281
0.0009 121.0 242 1.0572 0.8281
0.0009 122.0 244 1.0611 0.8281
0.0009 123.0 246 1.0650 0.8281
0.0009 124.0 248 1.0664 0.8281
0.0015 125.0 250 1.1047 0.8281
0.0015 126.0 252 1.1348 0.8281
0.0015 127.0 254 1.1568 0.8125
0.0015 128.0 256 1.1730 0.8125
0.0015 129.0 258 1.1849 0.8125
0.0007 130.0 260 1.1937 0.8125
0.0007 131.0 262 1.2006 0.8125
0.0007 132.0 264 1.2057 0.8125
0.0007 133.0 266 1.2096 0.8125
0.0007 134.0 268 1.2120 0.8125
0.0007 135.0 270 1.2140 0.8125
0.0007 136.0 272 1.2134 0.8125
0.0007 137.0 274 1.2122 0.8125
0.0007 138.0 276 1.2105 0.8125
0.0007 139.0 278 1.2089 0.8125
0.0006 140.0 280 1.2075 0.8125
0.0006 141.0 282 1.2063 0.8125
0.0006 142.0 284 1.2054 0.8125
0.0006 143.0 286 1.2049 0.8125
0.0006 144.0 288 1.2039 0.8125
0.0005 145.0 290 1.2032 0.8281
0.0005 146.0 292 1.2029 0.8281
0.0005 147.0 294 1.2028 0.8281
0.0005 148.0 296 1.2029 0.8281
0.0005 149.0 298 1.2032 0.8281
0.0005 150.0 300 1.2039 0.8281

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.4.0
  • Tokenizers 0.13.3