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best_model-sst-2-16-13

Text Classification
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PyTorch
bert
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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-2-16-13
    results: []

best_model-sst-2-16-13

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: 0.9572
  • Accuracy: 0.4688

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 0.7460 0.5
No log 2.0 2 0.7459 0.5
No log 3.0 3 0.7459 0.5
No log 4.0 4 0.7457 0.5
No log 5.0 5 0.7456 0.5
No log 6.0 6 0.7454 0.5
No log 7.0 7 0.7452 0.5
No log 8.0 8 0.7449 0.5
No log 9.0 9 0.7446 0.5
0.7277 10.0 10 0.7443 0.5
0.7277 11.0 11 0.7439 0.5
0.7277 12.0 12 0.7435 0.5
0.7277 13.0 13 0.7430 0.5
0.7277 14.0 14 0.7426 0.5
0.7277 15.0 15 0.7420 0.5
0.7277 16.0 16 0.7415 0.5
0.7277 17.0 17 0.7409 0.5
0.7277 18.0 18 0.7403 0.5
0.7277 19.0 19 0.7397 0.5
0.7081 20.0 20 0.7390 0.5
0.7081 21.0 21 0.7382 0.5
0.7081 22.0 22 0.7375 0.5
0.7081 23.0 23 0.7367 0.5
0.7081 24.0 24 0.7359 0.5
0.7081 25.0 25 0.7351 0.5
0.7081 26.0 26 0.7342 0.5
0.7081 27.0 27 0.7334 0.5
0.7081 28.0 28 0.7325 0.5
0.7081 29.0 29 0.7316 0.5
0.7107 30.0 30 0.7306 0.5
0.7107 31.0 31 0.7297 0.5
0.7107 32.0 32 0.7287 0.5
0.7107 33.0 33 0.7277 0.5
0.7107 34.0 34 0.7266 0.5
0.7107 35.0 35 0.7256 0.5
0.7107 36.0 36 0.7246 0.5
0.7107 37.0 37 0.7235 0.5
0.7107 38.0 38 0.7225 0.5
0.7107 39.0 39 0.7214 0.5
0.6761 40.0 40 0.7204 0.5
0.6761 41.0 41 0.7193 0.5
0.6761 42.0 42 0.7182 0.4688
0.6761 43.0 43 0.7172 0.4688
0.6761 44.0 44 0.7161 0.4688
0.6761 45.0 45 0.7150 0.4688
0.6761 46.0 46 0.7140 0.4688
0.6761 47.0 47 0.7130 0.4688
0.6761 48.0 48 0.7119 0.4688
0.6761 49.0 49 0.7110 0.4688
0.657 50.0 50 0.7100 0.4688
0.657 51.0 51 0.7091 0.4375
0.657 52.0 52 0.7083 0.4688
0.657 53.0 53 0.7074 0.4688
0.657 54.0 54 0.7067 0.4688
0.657 55.0 55 0.7059 0.4688
0.657 56.0 56 0.7054 0.4375
0.657 57.0 57 0.7049 0.4688
0.657 58.0 58 0.7045 0.4688
0.657 59.0 59 0.7042 0.4688
0.621 60.0 60 0.7041 0.4688
0.621 61.0 61 0.7040 0.4688
0.621 62.0 62 0.7041 0.4688
0.621 63.0 63 0.7043 0.5
0.621 64.0 64 0.7047 0.5
0.621 65.0 65 0.7054 0.4688
0.621 66.0 66 0.7063 0.4688
0.621 67.0 67 0.7072 0.4688
0.621 68.0 68 0.7082 0.4688
0.621 69.0 69 0.7092 0.4688
0.5793 70.0 70 0.7102 0.4688
0.5793 71.0 71 0.7112 0.4688
0.5793 72.0 72 0.7124 0.4688
0.5793 73.0 73 0.7137 0.4688
0.5793 74.0 74 0.7151 0.4688
0.5793 75.0 75 0.7167 0.4688
0.5793 76.0 76 0.7184 0.4688
0.5793 77.0 77 0.7202 0.5
0.5793 78.0 78 0.7220 0.5
0.5793 79.0 79 0.7238 0.5
0.524 80.0 80 0.7257 0.5
0.524 81.0 81 0.7276 0.5
0.524 82.0 82 0.7295 0.5
0.524 83.0 83 0.7315 0.5
0.524 84.0 84 0.7336 0.4688
0.524 85.0 85 0.7358 0.4688
0.524 86.0 86 0.7381 0.4688
0.524 87.0 87 0.7406 0.4688
0.524 88.0 88 0.7431 0.4688
0.524 89.0 89 0.7458 0.4688
0.4597 90.0 90 0.7488 0.4688
0.4597 91.0 91 0.7520 0.4688
0.4597 92.0 92 0.7549 0.4688
0.4597 93.0 93 0.7574 0.4375
0.4597 94.0 94 0.7599 0.4375
0.4597 95.0 95 0.7627 0.4375
0.4597 96.0 96 0.7659 0.4375
0.4597 97.0 97 0.7694 0.4375
0.4597 98.0 98 0.7730 0.4375
0.4597 99.0 99 0.7765 0.4375
0.3918 100.0 100 0.7799 0.4375
0.3918 101.0 101 0.7834 0.4375
0.3918 102.0 102 0.7867 0.4375
0.3918 103.0 103 0.7898 0.4375
0.3918 104.0 104 0.7931 0.4375
0.3918 105.0 105 0.7963 0.4375
0.3918 106.0 106 0.7996 0.4375
0.3918 107.0 107 0.8029 0.4375
0.3918 108.0 108 0.8060 0.4375
0.3918 109.0 109 0.8090 0.4375
0.3216 110.0 110 0.8121 0.4688
0.3216 111.0 111 0.8155 0.4375
0.3216 112.0 112 0.8191 0.4375
0.3216 113.0 113 0.8227 0.4375
0.3216 114.0 114 0.8260 0.4375
0.3216 115.0 115 0.8293 0.4375
0.3216 116.0 116 0.8326 0.4688
0.3216 117.0 117 0.8356 0.4688
0.3216 118.0 118 0.8387 0.4375
0.3216 119.0 119 0.8420 0.4375
0.267 120.0 120 0.8454 0.4375
0.267 121.0 121 0.8488 0.4375
0.267 122.0 122 0.8525 0.4375
0.267 123.0 123 0.8563 0.4375
0.267 124.0 124 0.8601 0.4375
0.267 125.0 125 0.8639 0.4375
0.267 126.0 126 0.8677 0.4375
0.267 127.0 127 0.8716 0.4375
0.267 128.0 128 0.8762 0.4375
0.267 129.0 129 0.8807 0.4375
0.2376 130.0 130 0.8853 0.4375
0.2376 131.0 131 0.8898 0.4375
0.2376 132.0 132 0.8943 0.4375
0.2376 133.0 133 0.8988 0.4375
0.2376 134.0 134 0.9029 0.4375
0.2376 135.0 135 0.9061 0.4375
0.2376 136.0 136 0.9092 0.4062
0.2376 137.0 137 0.9113 0.4062
0.2376 138.0 138 0.9130 0.4375
0.2376 139.0 139 0.9146 0.4375
0.2042 140.0 140 0.9163 0.4375
0.2042 141.0 141 0.9178 0.4375
0.2042 142.0 142 0.9193 0.4375
0.2042 143.0 143 0.9206 0.4375
0.2042 144.0 144 0.9222 0.4375
0.2042 145.0 145 0.9268 0.4375
0.2042 146.0 146 0.9325 0.4375
0.2042 147.0 147 0.9385 0.4375
0.2042 148.0 148 0.9448 0.4375
0.2042 149.0 149 0.9509 0.4375
0.1738 150.0 150 0.9572 0.4688

Framework versions

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