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best_model-yelp_polarity-16-87

Text Classification
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PyTorch
albert
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: best_model-yelp_polarity-16-87
    results: []

best_model-yelp_polarity-16-87

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: 0.2887
  • Accuracy: 0.8438

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 0.3259 0.875
No log 2.0 2 0.3259 0.875
No log 3.0 3 0.3257 0.875
No log 4.0 4 0.3256 0.875
No log 5.0 5 0.3254 0.875
No log 6.0 6 0.3251 0.875
No log 7.0 7 0.3247 0.875
No log 8.0 8 0.3243 0.875
No log 9.0 9 0.3238 0.875
0.2717 10.0 10 0.3233 0.875
0.2717 11.0 11 0.3227 0.875
0.2717 12.0 12 0.3220 0.875
0.2717 13.0 13 0.3212 0.875
0.2717 14.0 14 0.3204 0.875
0.2717 15.0 15 0.3195 0.875
0.2717 16.0 16 0.3185 0.875
0.2717 17.0 17 0.3174 0.875
0.2717 18.0 18 0.3161 0.875
0.2717 19.0 19 0.3148 0.875
0.2339 20.0 20 0.3134 0.875
0.2339 21.0 21 0.3119 0.875
0.2339 22.0 22 0.3103 0.875
0.2339 23.0 23 0.3087 0.875
0.2339 24.0 24 0.3072 0.875
0.2339 25.0 25 0.3056 0.875
0.2339 26.0 26 0.3038 0.875
0.2339 27.0 27 0.3021 0.875
0.2339 28.0 28 0.3003 0.875
0.2339 29.0 29 0.2985 0.875
0.1912 30.0 30 0.2967 0.875
0.1912 31.0 31 0.2948 0.875
0.1912 32.0 32 0.2931 0.875
0.1912 33.0 33 0.2913 0.875
0.1912 34.0 34 0.2895 0.875
0.1912 35.0 35 0.2876 0.875
0.1912 36.0 36 0.2858 0.875
0.1912 37.0 37 0.2840 0.875
0.1912 38.0 38 0.2822 0.875
0.1912 39.0 39 0.2803 0.875
0.115 40.0 40 0.2785 0.875
0.115 41.0 41 0.2767 0.9062
0.115 42.0 42 0.2750 0.9062
0.115 43.0 43 0.2732 0.9062
0.115 44.0 44 0.2713 0.9062
0.115 45.0 45 0.2694 0.9062
0.115 46.0 46 0.2676 0.9062
0.115 47.0 47 0.2658 0.9062
0.115 48.0 48 0.2640 0.9062
0.115 49.0 49 0.2625 0.9062
0.0852 50.0 50 0.2612 0.9062
0.0852 51.0 51 0.2604 0.875
0.0852 52.0 52 0.2601 0.875
0.0852 53.0 53 0.2607 0.8438
0.0852 54.0 54 0.2623 0.8438
0.0852 55.0 55 0.2655 0.8438
0.0852 56.0 56 0.2683 0.8438
0.0852 57.0 57 0.2702 0.8438
0.0852 58.0 58 0.2712 0.875
0.0852 59.0 59 0.2724 0.875
0.0595 60.0 60 0.2739 0.875
0.0595 61.0 61 0.2749 0.875
0.0595 62.0 62 0.2746 0.8438
0.0595 63.0 63 0.2741 0.875
0.0595 64.0 64 0.2728 0.875
0.0595 65.0 65 0.2725 0.875
0.0595 66.0 66 0.2714 0.875
0.0595 67.0 67 0.2707 0.875
0.0595 68.0 68 0.2710 0.875
0.0595 69.0 69 0.2717 0.875
0.0512 70.0 70 0.2730 0.875
0.0512 71.0 71 0.2744 0.875
0.0512 72.0 72 0.2770 0.875
0.0512 73.0 73 0.2799 0.875
0.0512 74.0 74 0.2819 0.875
0.0512 75.0 75 0.2848 0.875
0.0512 76.0 76 0.2876 0.875
0.0512 77.0 77 0.2896 0.875
0.0512 78.0 78 0.2917 0.875
0.0512 79.0 79 0.2941 0.875
0.0434 80.0 80 0.2939 0.875
0.0434 81.0 81 0.2912 0.875
0.0434 82.0 82 0.2886 0.875
0.0434 83.0 83 0.2858 0.875
0.0434 84.0 84 0.2824 0.875
0.0434 85.0 85 0.2793 0.875
0.0434 86.0 86 0.2744 0.875
0.0434 87.0 87 0.2724 0.875
0.0434 88.0 88 0.2710 0.875
0.0434 89.0 89 0.2697 0.875
0.0369 90.0 90 0.2690 0.875
0.0369 91.0 91 0.2681 0.875
0.0369 92.0 92 0.2665 0.875
0.0369 93.0 93 0.2653 0.875
0.0369 94.0 94 0.2647 0.875
0.0369 95.0 95 0.2633 0.875
0.0369 96.0 96 0.2627 0.875
0.0369 97.0 97 0.2625 0.875
0.0369 98.0 98 0.2644 0.875
0.0369 99.0 99 0.2635 0.875
0.0322 100.0 100 0.2641 0.875
0.0322 101.0 101 0.2578 0.875
0.0322 102.0 102 0.2545 0.875
0.0322 103.0 103 0.2523 0.875
0.0322 104.0 104 0.2487 0.875
0.0322 105.0 105 0.2455 0.875
0.0322 106.0 106 0.2446 0.875
0.0322 107.0 107 0.2448 0.875
0.0322 108.0 108 0.2457 0.875
0.0322 109.0 109 0.2491 0.875
0.029 110.0 110 0.2533 0.875
0.029 111.0 111 0.2583 0.875
0.029 112.0 112 0.2636 0.875
0.029 113.0 113 0.2695 0.875
0.029 114.0 114 0.2741 0.875
0.029 115.0 115 0.2807 0.8438
0.029 116.0 116 0.2901 0.8438
0.029 117.0 117 0.2972 0.8438
0.029 118.0 118 0.3048 0.8438
0.029 119.0 119 0.3109 0.8438
0.025 120.0 120 0.3177 0.8438
0.025 121.0 121 0.3216 0.8438
0.025 122.0 122 0.3244 0.8438
0.025 123.0 123 0.3253 0.8438
0.025 124.0 124 0.3263 0.8438
0.025 125.0 125 0.3257 0.8438
0.025 126.0 126 0.3258 0.8438
0.025 127.0 127 0.3259 0.8438
0.025 128.0 128 0.3269 0.8438
0.025 129.0 129 0.3269 0.8125
0.0213 130.0 130 0.3278 0.8125
0.0213 131.0 131 0.3265 0.8125
0.0213 132.0 132 0.3268 0.8125
0.0213 133.0 133 0.3242 0.8125
0.0213 134.0 134 0.3193 0.8438
0.0213 135.0 135 0.3127 0.8438
0.0213 136.0 136 0.3047 0.8438
0.0213 137.0 137 0.2973 0.8438
0.0213 138.0 138 0.2891 0.8438
0.0213 139.0 139 0.2836 0.8438
0.0196 140.0 140 0.2794 0.8438
0.0196 141.0 141 0.2769 0.8438
0.0196 142.0 142 0.2762 0.8438
0.0196 143.0 143 0.2764 0.8438
0.0196 144.0 144 0.2776 0.8438
0.0196 145.0 145 0.2806 0.8438
0.0196 146.0 146 0.2858 0.8438
0.0196 147.0 147 0.2876 0.8438
0.0196 148.0 148 0.2899 0.8438
0.0196 149.0 149 0.2893 0.8438
0.0171 150.0 150 0.2887 0.8438

Framework versions

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