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best_model-sst-2-16-21

Text Classification
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PyTorch
bert
Generated from Trainer
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best_model-sst-2-16-21 / 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-2-16-21
    results: []

best_model-sst-2-16-21

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: 0.4916
  • Accuracy: 0.7812

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 1 0.6220 0.7188
No log 2.0 2 0.6220 0.7188
No log 3.0 3 0.6219 0.7188
No log 4.0 4 0.6218 0.7188
No log 5.0 5 0.6217 0.7188
No log 6.0 6 0.6216 0.7188
No log 7.0 7 0.6215 0.7188
No log 8.0 8 0.6214 0.7188
No log 9.0 9 0.6213 0.7188
0.6205 10.0 10 0.6211 0.7188
0.6205 11.0 11 0.6210 0.7188
0.6205 12.0 12 0.6208 0.7188
0.6205 13.0 13 0.6206 0.7188
0.6205 14.0 14 0.6204 0.7188
0.6205 15.0 15 0.6202 0.7188
0.6205 16.0 16 0.6200 0.7188
0.6205 17.0 17 0.6198 0.7188
0.6205 18.0 18 0.6196 0.7188
0.6205 19.0 19 0.6194 0.7188
0.6217 20.0 20 0.6192 0.6875
0.6217 21.0 21 0.6190 0.6875
0.6217 22.0 22 0.6189 0.6875
0.6217 23.0 23 0.6187 0.6875
0.6217 24.0 24 0.6186 0.6875
0.6217 25.0 25 0.6185 0.6875
0.6217 26.0 26 0.6184 0.6875
0.6217 27.0 27 0.6182 0.6875
0.6217 28.0 28 0.6181 0.6875
0.6217 29.0 29 0.6180 0.6875
0.6001 30.0 30 0.6179 0.6875
0.6001 31.0 31 0.6178 0.6875
0.6001 32.0 32 0.6178 0.6875
0.6001 33.0 33 0.6177 0.6875
0.6001 34.0 34 0.6177 0.6875
0.6001 35.0 35 0.6177 0.6875
0.6001 36.0 36 0.6177 0.6875
0.6001 37.0 37 0.6178 0.6875
0.6001 38.0 38 0.6178 0.6875
0.6001 39.0 39 0.6179 0.6562
0.5564 40.0 40 0.6180 0.6562
0.5564 41.0 41 0.6181 0.6562
0.5564 42.0 42 0.6181 0.6562
0.5564 43.0 43 0.6180 0.6562
0.5564 44.0 44 0.6179 0.6562
0.5564 45.0 45 0.6177 0.6562
0.5564 46.0 46 0.6174 0.6562
0.5564 47.0 47 0.6171 0.6562
0.5564 48.0 48 0.6171 0.6562
0.5564 49.0 49 0.6170 0.6562
0.5364 50.0 50 0.6172 0.6562
0.5364 51.0 51 0.6172 0.625
0.5364 52.0 52 0.6172 0.625
0.5364 53.0 53 0.6170 0.625
0.5364 54.0 54 0.6165 0.625
0.5364 55.0 55 0.6161 0.625
0.5364 56.0 56 0.6155 0.625
0.5364 57.0 57 0.6149 0.625
0.5364 58.0 58 0.6142 0.625
0.5364 59.0 59 0.6137 0.625
0.489 60.0 60 0.6132 0.625
0.489 61.0 61 0.6126 0.625
0.489 62.0 62 0.6121 0.625
0.489 63.0 63 0.6116 0.625
0.489 64.0 64 0.6111 0.5938
0.489 65.0 65 0.6107 0.5938
0.489 66.0 66 0.6103 0.625
0.489 67.0 67 0.6098 0.625
0.489 68.0 68 0.6093 0.625
0.489 69.0 69 0.6088 0.625
0.4517 70.0 70 0.6086 0.625
0.4517 71.0 71 0.6080 0.625
0.4517 72.0 72 0.6068 0.625
0.4517 73.0 73 0.6052 0.625
0.4517 74.0 74 0.6035 0.625
0.4517 75.0 75 0.6014 0.625
0.4517 76.0 76 0.5993 0.625
0.4517 77.0 77 0.5974 0.625
0.4517 78.0 78 0.5951 0.6562
0.4517 79.0 79 0.5932 0.6875
0.4066 80.0 80 0.5912 0.6875
0.4066 81.0 81 0.5895 0.6875
0.4066 82.0 82 0.5880 0.6875
0.4066 83.0 83 0.5868 0.6875
0.4066 84.0 84 0.5856 0.7188
0.4066 85.0 85 0.5843 0.7188
0.4066 86.0 86 0.5829 0.7188
0.4066 87.0 87 0.5816 0.7188
0.4066 88.0 88 0.5803 0.7188
0.4066 89.0 89 0.5790 0.7188
0.3548 90.0 90 0.5778 0.7188
0.3548 91.0 91 0.5766 0.75
0.3548 92.0 92 0.5754 0.75
0.3548 93.0 93 0.5743 0.75
0.3548 94.0 94 0.5732 0.75
0.3548 95.0 95 0.5719 0.75
0.3548 96.0 96 0.5706 0.75
0.3548 97.0 97 0.5693 0.75
0.3548 98.0 98 0.5680 0.75
0.3548 99.0 99 0.5669 0.75
0.3182 100.0 100 0.5659 0.75
0.3182 101.0 101 0.5648 0.7812
0.3182 102.0 102 0.5636 0.7812
0.3182 103.0 103 0.5624 0.7812
0.3182 104.0 104 0.5612 0.7812
0.3182 105.0 105 0.5599 0.7812
0.3182 106.0 106 0.5584 0.7812
0.3182 107.0 107 0.5567 0.8125
0.3182 108.0 108 0.5548 0.8125
0.3182 109.0 109 0.5530 0.8125
0.2758 110.0 110 0.5513 0.8125
0.2758 111.0 111 0.5498 0.8125
0.2758 112.0 112 0.5483 0.8125
0.2758 113.0 113 0.5468 0.7812
0.2758 114.0 114 0.5452 0.7812
0.2758 115.0 115 0.5438 0.7812
0.2758 116.0 116 0.5429 0.7812
0.2758 117.0 117 0.5420 0.75
0.2758 118.0 118 0.5411 0.75
0.2758 119.0 119 0.5400 0.7188
0.2399 120.0 120 0.5390 0.7188
0.2399 121.0 121 0.5375 0.7188
0.2399 122.0 122 0.5356 0.75
0.2399 123.0 123 0.5340 0.75
0.2399 124.0 124 0.5322 0.75
0.2399 125.0 125 0.5303 0.7812
0.2399 126.0 126 0.5281 0.75
0.2399 127.0 127 0.5259 0.7812
0.2399 128.0 128 0.5240 0.7812
0.2399 129.0 129 0.5215 0.7812
0.2062 130.0 130 0.5191 0.75
0.2062 131.0 131 0.5167 0.75
0.2062 132.0 132 0.5143 0.75
0.2062 133.0 133 0.5121 0.75
0.2062 134.0 134 0.5102 0.75
0.2062 135.0 135 0.5085 0.7812
0.2062 136.0 136 0.5071 0.7812
0.2062 137.0 137 0.5059 0.7812
0.2062 138.0 138 0.5048 0.7812
0.2062 139.0 139 0.5035 0.7812
0.1688 140.0 140 0.5023 0.7812
0.1688 141.0 141 0.5011 0.7812
0.1688 142.0 142 0.5001 0.7812
0.1688 143.0 143 0.4991 0.7812
0.1688 144.0 144 0.4980 0.7812
0.1688 145.0 145 0.4970 0.7812
0.1688 146.0 146 0.4960 0.7812
0.1688 147.0 147 0.4950 0.7812
0.1688 148.0 148 0.4938 0.7812
0.1688 149.0 149 0.4926 0.7812
0.1364 150.0 150 0.4916 0.7812

Framework versions

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