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bert-base-uncased-sst-2-32-100

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: bert-base-uncased-sst-2-32-100
    results: []

bert-base-uncased-sst-2-32-100

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: 0.4379
  • Accuracy: 0.9219

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 0.5385 0.9219
No log 2.0 4 0.5392 0.9219
No log 3.0 6 0.5398 0.9219
No log 4.0 8 0.5410 0.9219
0.733 5.0 10 0.5426 0.9219
0.733 6.0 12 0.5443 0.9062
0.733 7.0 14 0.5461 0.9062
0.733 8.0 16 0.5481 0.9062
0.733 9.0 18 0.5487 0.9062
0.6383 10.0 20 0.5495 0.9062
0.6383 11.0 22 0.5546 0.8906
0.6383 12.0 24 0.5643 0.9062
0.6383 13.0 26 0.5742 0.9062
0.6383 14.0 28 0.5875 0.9062
0.4993 15.0 30 0.5982 0.9062
0.4993 16.0 32 0.6100 0.9062
0.4993 17.0 34 0.6222 0.9062
0.4993 18.0 36 0.6263 0.9062
0.4993 19.0 38 0.6305 0.9062
0.4891 20.0 40 0.6335 0.9062
0.4891 21.0 42 0.6368 0.9062
0.4891 22.0 44 0.6351 0.9062
0.4891 23.0 46 0.6301 0.9062
0.4891 24.0 48 0.6212 0.9062
0.377 25.0 50 0.6100 0.9062
0.377 26.0 52 0.5999 0.9062
0.377 27.0 54 0.5852 0.9062
0.377 28.0 56 0.5737 0.9062
0.377 29.0 58 0.5606 0.9219
0.3369 30.0 60 0.5466 0.9062
0.3369 31.0 62 0.5319 0.9062
0.3369 32.0 64 0.5205 0.9062
0.3369 33.0 66 0.5074 0.9219
0.3369 34.0 68 0.5025 0.9219
0.19 35.0 70 0.4984 0.9219
0.19 36.0 72 0.4934 0.9219
0.19 37.0 74 0.4927 0.9375
0.19 38.0 76 0.4955 0.9375
0.19 39.0 78 0.4968 0.9375
0.0507 40.0 80 0.4956 0.9375
0.0507 41.0 82 0.4882 0.9375
0.0507 42.0 84 0.4784 0.9375
0.0507 43.0 86 0.4710 0.9219
0.0507 44.0 88 0.4650 0.9219
0.0102 45.0 90 0.4578 0.9219
0.0102 46.0 92 0.4540 0.9219
0.0102 47.0 94 0.4566 0.9062
0.0102 48.0 96 0.4682 0.9062
0.0102 49.0 98 0.4831 0.9219
0.0026 50.0 100 0.4922 0.9219
0.0026 51.0 102 0.4985 0.9219
0.0026 52.0 104 0.5029 0.9219
0.0026 53.0 106 0.5062 0.9219
0.0026 54.0 108 0.5087 0.9219
0.001 55.0 110 0.5100 0.9219
0.001 56.0 112 0.5110 0.9219
0.001 57.0 114 0.5112 0.9219
0.001 58.0 116 0.5112 0.9219
0.001 59.0 118 0.5110 0.9219
0.0004 60.0 120 0.5087 0.9219
0.0004 61.0 122 0.5028 0.9219
0.0004 62.0 124 0.4965 0.9219
0.0004 63.0 126 0.4903 0.9219
0.0004 64.0 128 0.4848 0.9219
0.0003 65.0 130 0.4802 0.9219
0.0003 66.0 132 0.4767 0.9219
0.0003 67.0 134 0.4739 0.9219
0.0003 68.0 136 0.4719 0.9219
0.0003 69.0 138 0.4707 0.9219
0.0024 70.0 140 0.4600 0.9219
0.0024 71.0 142 0.4439 0.9219
0.0024 72.0 144 0.4336 0.9062
0.0024 73.0 146 0.4283 0.9062
0.0024 74.0 148 0.4253 0.9219
0.0002 75.0 150 0.4237 0.9219
0.0002 76.0 152 0.4232 0.9375
0.0002 77.0 154 0.4230 0.9375
0.0002 78.0 156 0.4229 0.9375
0.0002 79.0 158 0.4228 0.9375
0.0002 80.0 160 0.4228 0.9375
0.0002 81.0 162 0.4225 0.9375
0.0002 82.0 164 0.4237 0.9062
0.0002 83.0 166 0.4384 0.9219
0.0002 84.0 168 0.4565 0.9219
0.0004 85.0 170 0.4717 0.9219
0.0004 86.0 172 0.4813 0.9219
0.0004 87.0 174 0.4858 0.9219
0.0004 88.0 176 0.4885 0.9219
0.0004 89.0 178 0.4897 0.9219
0.0002 90.0 180 0.4904 0.9219
0.0002 91.0 182 0.4865 0.9219
0.0002 92.0 184 0.4732 0.9219
0.0002 93.0 186 0.4557 0.9219
0.0002 94.0 188 0.4388 0.9219
0.0053 95.0 190 0.4254 0.9219
0.0053 96.0 192 0.4171 0.9219
0.0053 97.0 194 0.4132 0.9375
0.0053 98.0 196 0.4118 0.9375
0.0053 99.0 198 0.4115 0.9219
0.0002 100.0 200 0.4118 0.9219
0.0002 101.0 202 0.4122 0.9219
0.0002 102.0 204 0.4125 0.9219
0.0002 103.0 206 0.4128 0.9219
0.0002 104.0 208 0.4131 0.9219
0.0002 105.0 210 0.4133 0.9219
0.0002 106.0 212 0.4134 0.9219
0.0002 107.0 214 0.4140 0.9219
0.0002 108.0 216 0.4149 0.9219
0.0002 109.0 218 0.4158 0.9219
0.0002 110.0 220 0.4167 0.9219
0.0002 111.0 222 0.4175 0.9219
0.0002 112.0 224 0.4183 0.9375
0.0002 113.0 226 0.4190 0.9375
0.0002 114.0 228 0.4197 0.9375
0.0001 115.0 230 0.4203 0.9375
0.0001 116.0 232 0.4208 0.9375
0.0001 117.0 234 0.4218 0.9219
0.0001 118.0 236 0.4228 0.9219
0.0001 119.0 238 0.4237 0.9219
0.0002 120.0 240 0.4244 0.9219
0.0002 121.0 242 0.4251 0.9219
0.0002 122.0 244 0.4257 0.9219
0.0002 123.0 246 0.4263 0.9219
0.0002 124.0 248 0.4269 0.9219
0.0002 125.0 250 0.4273 0.9219
0.0002 126.0 252 0.4277 0.9219
0.0002 127.0 254 0.4280 0.9219
0.0002 128.0 256 0.4284 0.9219
0.0002 129.0 258 0.4287 0.9219
0.0008 130.0 260 0.4330 0.9219
0.0008 131.0 262 0.4554 0.9219
0.0008 132.0 264 0.4714 0.9219
0.0008 133.0 266 0.4845 0.9375
0.0008 134.0 268 0.5000 0.9219
0.0001 135.0 270 0.5167 0.9219
0.0001 136.0 272 0.5308 0.9062
0.0001 137.0 274 0.5417 0.9062
0.0001 138.0 276 0.5480 0.9062
0.0001 139.0 278 0.5529 0.9062
0.0001 140.0 280 0.5566 0.9062
0.0001 141.0 282 0.5570 0.9062
0.0001 142.0 284 0.5565 0.9062
0.0001 143.0 286 0.5555 0.9062
0.0001 144.0 288 0.5544 0.9062
0.0001 145.0 290 0.5511 0.9062
0.0001 146.0 292 0.5096 0.9219
0.0001 147.0 294 0.4811 0.9375
0.0001 148.0 296 0.4624 0.9219
0.0001 149.0 298 0.4488 0.9219
0.0002 150.0 300 0.4379 0.9219

Framework versions

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