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best_model-sst-2-32-13

Text Classification
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PyTorch
bert
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best_model-sst-2-32-13 / README.md
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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: 1.0073
  • Accuracy: 0.6875

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 0.6566 0.6719
No log 2.0 4 0.6561 0.6719
No log 3.0 6 0.6553 0.6719
No log 4.0 8 0.6542 0.6719
0.4607 5.0 10 0.6525 0.6719
0.4607 6.0 12 0.6504 0.6719
0.4607 7.0 14 0.6480 0.6719
0.4607 8.0 16 0.6454 0.6719
0.4607 9.0 18 0.6432 0.6719
0.4685 10.0 20 0.6408 0.6719
0.4685 11.0 22 0.6383 0.6562
0.4685 12.0 24 0.6357 0.6719
0.4685 13.0 26 0.6331 0.6562
0.4685 14.0 28 0.6310 0.6719
0.444 15.0 30 0.6289 0.6719
0.444 16.0 32 0.6270 0.6562
0.444 17.0 34 0.6253 0.6562
0.444 18.0 36 0.6235 0.6406
0.444 19.0 38 0.6216 0.6406
0.4147 20.0 40 0.6199 0.6406
0.4147 21.0 42 0.6183 0.6562
0.4147 22.0 44 0.6168 0.6562
0.4147 23.0 46 0.6155 0.6562
0.4147 24.0 48 0.6144 0.6562
0.409 25.0 50 0.6133 0.6406
0.409 26.0 52 0.6123 0.6406
0.409 27.0 54 0.6116 0.6562
0.409 28.0 56 0.6110 0.6562
0.409 29.0 58 0.6103 0.6562
0.3614 30.0 60 0.6099 0.6719
0.3614 31.0 62 0.6094 0.6719
0.3614 32.0 64 0.6090 0.6719
0.3614 33.0 66 0.6090 0.6719
0.3614 34.0 68 0.6090 0.6875
0.3297 35.0 70 0.6093 0.6875
0.3297 36.0 72 0.6095 0.6875
0.3297 37.0 74 0.6093 0.6875
0.3297 38.0 76 0.6089 0.6875
0.3297 39.0 78 0.6081 0.6875
0.2957 40.0 80 0.6072 0.6875
0.2957 41.0 82 0.6064 0.7031
0.2957 42.0 84 0.6054 0.6875
0.2957 43.0 86 0.6041 0.6719
0.2957 44.0 88 0.6031 0.6719
0.2704 45.0 90 0.6022 0.6875
0.2704 46.0 92 0.6017 0.6875
0.2704 47.0 94 0.6012 0.6875
0.2704 48.0 96 0.6008 0.6719
0.2704 49.0 98 0.5998 0.6719
0.2302 50.0 100 0.5987 0.6875
0.2302 51.0 102 0.5977 0.6875
0.2302 52.0 104 0.5959 0.6875
0.2302 53.0 106 0.5935 0.6719
0.2302 54.0 108 0.5913 0.6719
0.198 55.0 110 0.5895 0.6719
0.198 56.0 112 0.5882 0.6719
0.198 57.0 114 0.5865 0.6875
0.198 58.0 116 0.5854 0.7031
0.198 59.0 118 0.5852 0.7031
0.1729 60.0 120 0.5854 0.7031
0.1729 61.0 122 0.5857 0.7031
0.1729 62.0 124 0.5861 0.6875
0.1729 63.0 126 0.5864 0.6875
0.1729 64.0 128 0.5866 0.6875
0.1495 65.0 130 0.5863 0.6719
0.1495 66.0 132 0.5858 0.6719
0.1495 67.0 134 0.5856 0.6875
0.1495 68.0 136 0.5860 0.6875
0.1495 69.0 138 0.5860 0.6719
0.1223 70.0 140 0.5863 0.6719
0.1223 71.0 142 0.5870 0.6875
0.1223 72.0 144 0.5881 0.6875
0.1223 73.0 146 0.5890 0.6719
0.1223 74.0 148 0.5899 0.6719
0.1034 75.0 150 0.5909 0.6719
0.1034 76.0 152 0.5920 0.6719
0.1034 77.0 154 0.5935 0.6562
0.1034 78.0 156 0.5953 0.6562
0.1034 79.0 158 0.5969 0.6562
0.087 80.0 160 0.5984 0.6562
0.087 81.0 162 0.6008 0.6562
0.087 82.0 164 0.6031 0.6719
0.087 83.0 166 0.6057 0.6562
0.087 84.0 168 0.6088 0.6562
0.0705 85.0 170 0.6126 0.6562
0.0705 86.0 172 0.6164 0.6562
0.0705 87.0 174 0.6198 0.6719
0.0705 88.0 176 0.6227 0.6719
0.0705 89.0 178 0.6257 0.6719
0.0551 90.0 180 0.6288 0.6719
0.0551 91.0 182 0.6321 0.6719
0.0551 92.0 184 0.6360 0.6719
0.0551 93.0 186 0.6403 0.6875
0.0551 94.0 188 0.6443 0.6875
0.0447 95.0 190 0.6489 0.6875
0.0447 96.0 192 0.6534 0.6875
0.0447 97.0 194 0.6609 0.7031
0.0447 98.0 196 0.6689 0.7031
0.0447 99.0 198 0.6772 0.7188
0.0345 100.0 200 0.6875 0.7188
0.0345 101.0 202 0.6968 0.7188
0.0345 102.0 204 0.7052 0.7188
0.0345 103.0 206 0.7126 0.7188
0.0345 104.0 208 0.7181 0.7188
0.0274 105.0 210 0.7234 0.7188
0.0274 106.0 212 0.7282 0.7188
0.0274 107.0 214 0.7361 0.7188
0.0274 108.0 216 0.7440 0.7031
0.0274 109.0 218 0.7539 0.7031
0.0218 110.0 220 0.7658 0.7031
0.0218 111.0 222 0.7772 0.7031
0.0218 112.0 224 0.7882 0.7031
0.0218 113.0 226 0.7977 0.7031
0.0218 114.0 228 0.8047 0.7031
0.0168 115.0 230 0.8103 0.7031
0.0168 116.0 232 0.8135 0.7031
0.0168 117.0 234 0.8157 0.7031
0.0168 118.0 236 0.8186 0.7031
0.0168 119.0 238 0.8213 0.7031
0.0138 120.0 240 0.8240 0.6875
0.0138 121.0 242 0.8262 0.6875
0.0138 122.0 244 0.8296 0.6875
0.0138 123.0 246 0.8339 0.6875
0.0138 124.0 248 0.8392 0.6875
0.0115 125.0 250 0.8451 0.6875
0.0115 126.0 252 0.8508 0.6875
0.0115 127.0 254 0.8574 0.6875
0.0115 128.0 256 0.8640 0.6875
0.0115 129.0 258 0.8707 0.6875
0.0096 130.0 260 0.8776 0.6875
0.0096 131.0 262 0.8845 0.6875
0.0096 132.0 264 0.8938 0.6875
0.0096 133.0 266 0.9021 0.6875
0.0096 134.0 268 0.9102 0.6875
0.0083 135.0 270 0.9181 0.6875
0.0083 136.0 272 0.9260 0.6875
0.0083 137.0 274 0.9338 0.6875
0.0083 138.0 276 0.9416 0.6875
0.0083 139.0 278 0.9488 0.6875
0.0071 140.0 280 0.9566 0.6875
0.0071 141.0 282 0.9635 0.6875
0.0071 142.0 284 0.9707 0.6875
0.0071 143.0 286 0.9769 0.6875
0.0071 144.0 288 0.9825 0.6875
0.0061 145.0 290 0.9878 0.6875
0.0061 146.0 292 0.9926 0.6875
0.0061 147.0 294 0.9962 0.6875
0.0061 148.0 296 1.0002 0.6875
0.0061 149.0 298 1.0036 0.6875
0.0054 150.0 300 1.0073 0.6875

Framework versions

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