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bert-base-uncased-sst-2-16-100

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: bert-base-uncased-sst-2-16-100
    results: []

bert-base-uncased-sst-2-16-100

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: 0.3049
  • Accuracy: 0.9375

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 0.2289 0.9375
No log 2.0 2 0.2289 0.9375
No log 3.0 3 0.2287 0.9375
No log 4.0 4 0.2285 0.9375
No log 5.0 5 0.2281 0.9375
No log 6.0 6 0.2278 0.9375
No log 7.0 7 0.2271 0.9375
No log 8.0 8 0.2265 0.9375
No log 9.0 9 0.2259 0.9375
0.3159 10.0 10 0.2250 0.9375
0.3159 11.0 11 0.2236 0.9375
0.3159 12.0 12 0.2224 0.9375
0.3159 13.0 13 0.2208 0.9375
0.3159 14.0 14 0.2190 0.9375
0.3159 15.0 15 0.2175 0.9375
0.3159 16.0 16 0.2166 0.9375
0.3159 17.0 17 0.2154 0.9375
0.3159 18.0 18 0.2143 0.9375
0.3159 19.0 19 0.2138 0.9375
0.3191 20.0 20 0.2133 0.9375
0.3191 21.0 21 0.2130 0.9375
0.3191 22.0 22 0.2127 0.9375
0.3191 23.0 23 0.2121 0.9375
0.3191 24.0 24 0.2115 0.9375
0.3191 25.0 25 0.2112 0.9375
0.3191 26.0 26 0.2115 0.9375
0.3191 27.0 27 0.2113 0.9375
0.3191 28.0 28 0.2118 0.9375
0.3191 29.0 29 0.2130 0.9375
0.2101 30.0 30 0.2134 0.9375
0.2101 31.0 31 0.2136 0.9375
0.2101 32.0 32 0.2138 0.9375
0.2101 33.0 33 0.2133 0.9375
0.2101 34.0 34 0.2124 0.9375
0.2101 35.0 35 0.2112 0.9375
0.2101 36.0 36 0.2107 0.9375
0.2101 37.0 37 0.2099 0.9375
0.2101 38.0 38 0.2095 0.9375
0.2101 39.0 39 0.2096 0.9375
0.1112 40.0 40 0.2096 0.9375
0.1112 41.0 41 0.2096 0.9375
0.1112 42.0 42 0.2093 0.9375
0.1112 43.0 43 0.2085 0.9375
0.1112 44.0 44 0.2084 0.9375
0.1112 45.0 45 0.2082 0.9375
0.1112 46.0 46 0.2083 0.9375
0.1112 47.0 47 0.2088 0.9375
0.1112 48.0 48 0.2097 0.9375
0.1112 49.0 49 0.2111 0.9375
0.0453 50.0 50 0.2127 0.9375
0.0453 51.0 51 0.2147 0.9375
0.0453 52.0 52 0.2170 0.9375
0.0453 53.0 53 0.2200 0.9375
0.0453 54.0 54 0.2239 0.9375
0.0453 55.0 55 0.2271 0.9375
0.0453 56.0 56 0.2299 0.9375
0.0453 57.0 57 0.2320 0.9375
0.0453 58.0 58 0.2352 0.9375
0.0453 59.0 59 0.2364 0.9375
0.0349 60.0 60 0.2360 0.9375
0.0349 61.0 61 0.2348 0.9375
0.0349 62.0 62 0.2320 0.9375
0.0349 63.0 63 0.2302 0.9375
0.0349 64.0 64 0.2283 0.9375
0.0349 65.0 65 0.2256 0.9375
0.0349 66.0 66 0.2244 0.9375
0.0349 67.0 67 0.2226 0.9375
0.0349 68.0 68 0.2214 0.9375
0.0349 69.0 69 0.2208 0.9375
0.0184 70.0 70 0.2199 0.9375
0.0184 71.0 71 0.2178 0.9375
0.0184 72.0 72 0.2161 0.9375
0.0184 73.0 73 0.2147 0.9375
0.0184 74.0 74 0.2151 0.9375
0.0184 75.0 75 0.2158 0.9375
0.0184 76.0 76 0.2171 0.9375
0.0184 77.0 77 0.2184 0.9375
0.0184 78.0 78 0.2189 0.9375
0.0184 79.0 79 0.2196 0.9375
0.0125 80.0 80 0.2204 0.9375
0.0125 81.0 81 0.2216 0.9375
0.0125 82.0 82 0.2227 0.9375
0.0125 83.0 83 0.2243 0.9375
0.0125 84.0 84 0.2260 0.9375
0.0125 85.0 85 0.2275 0.9375
0.0125 86.0 86 0.2288 0.9375
0.0125 87.0 87 0.2300 0.9375
0.0125 88.0 88 0.2313 0.9375
0.0125 89.0 89 0.2328 0.9375
0.0099 90.0 90 0.2347 0.9375
0.0099 91.0 91 0.2375 0.9375
0.0099 92.0 92 0.2402 0.9375
0.0099 93.0 93 0.2428 0.9375
0.0099 94.0 94 0.2453 0.9375
0.0099 95.0 95 0.2477 0.9375
0.0099 96.0 96 0.2501 0.9375
0.0099 97.0 97 0.2554 0.9375
0.0099 98.0 98 0.2604 0.9375
0.0099 99.0 99 0.2651 0.9375
0.0078 100.0 100 0.2694 0.9375
0.0078 101.0 101 0.2728 0.9375
0.0078 102.0 102 0.2759 0.9375
0.0078 103.0 103 0.2787 0.9375
0.0078 104.0 104 0.2812 0.9375
0.0078 105.0 105 0.2837 0.9375
0.0078 106.0 106 0.2856 0.9375
0.0078 107.0 107 0.2899 0.9375
0.0078 108.0 108 0.2939 0.9375
0.0078 109.0 109 0.2976 0.9375
0.0078 110.0 110 0.3010 0.9375
0.0078 111.0 111 0.3042 0.9375
0.0078 112.0 112 0.3049 0.9375
0.0078 113.0 113 0.3050 0.9375
0.0078 114.0 114 0.3051 0.9375
0.0078 115.0 115 0.3045 0.9375
0.0078 116.0 116 0.3041 0.9375
0.0078 117.0 117 0.3034 0.9375
0.0078 118.0 118 0.2994 0.9375
0.0078 119.0 119 0.2958 0.9375
0.006 120.0 120 0.2924 0.9375
0.006 121.0 121 0.2892 0.9375
0.006 122.0 122 0.2866 0.9375
0.006 123.0 123 0.2842 0.9375
0.006 124.0 124 0.2821 0.9375
0.006 125.0 125 0.2802 0.9375
0.006 126.0 126 0.2785 0.9375
0.006 127.0 127 0.2775 0.9375
0.006 128.0 128 0.2768 0.9375
0.006 129.0 129 0.2762 0.9375
0.005 130.0 130 0.2758 0.9375
0.005 131.0 131 0.2756 0.9375
0.005 132.0 132 0.2756 0.9375
0.005 133.0 133 0.2757 0.9375
0.005 134.0 134 0.2758 0.9375
0.005 135.0 135 0.2762 0.9375
0.005 136.0 136 0.2767 0.9375
0.005 137.0 137 0.2773 0.9375
0.005 138.0 138 0.2780 0.9375
0.005 139.0 139 0.2783 0.9375
0.0042 140.0 140 0.2789 0.9375
0.0042 141.0 141 0.2794 0.9375
0.0042 142.0 142 0.2799 0.9375
0.0042 143.0 143 0.2805 0.9062
0.0042 144.0 144 0.2812 0.9062
0.0042 145.0 145 0.2818 0.9062
0.0042 146.0 146 0.2826 0.9062
0.0042 147.0 147 0.2877 0.9375
0.0042 148.0 148 0.2934 0.9375
0.0042 149.0 149 0.2993 0.9375
0.004 150.0 150 0.3049 0.9375

Framework versions

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