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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: 0.6847
  • Accuracy: 0.625

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 0.6990 0.5
No log 2.0 2 0.6990 0.5
No log 3.0 3 0.6990 0.5
No log 4.0 4 0.6989 0.5
No log 5.0 5 0.6989 0.5
No log 6.0 6 0.6988 0.5
No log 7.0 7 0.6987 0.5
No log 8.0 8 0.6986 0.5
No log 9.0 9 0.6985 0.5
0.6966 10.0 10 0.6984 0.5
0.6966 11.0 11 0.6982 0.5
0.6966 12.0 12 0.6980 0.5
0.6966 13.0 13 0.6979 0.5
0.6966 14.0 14 0.6977 0.5
0.6966 15.0 15 0.6974 0.5
0.6966 16.0 16 0.6972 0.5
0.6966 17.0 17 0.6969 0.5
0.6966 18.0 18 0.6967 0.5
0.6966 19.0 19 0.6964 0.5
0.6896 20.0 20 0.6961 0.5
0.6896 21.0 21 0.6958 0.5
0.6896 22.0 22 0.6955 0.5
0.6896 23.0 23 0.6951 0.5
0.6896 24.0 24 0.6947 0.5
0.6896 25.0 25 0.6944 0.5
0.6896 26.0 26 0.6940 0.5
0.6896 27.0 27 0.6936 0.5
0.6896 28.0 28 0.6932 0.5
0.6896 29.0 29 0.6928 0.5
0.6844 30.0 30 0.6924 0.5
0.6844 31.0 31 0.6920 0.5
0.6844 32.0 32 0.6916 0.5
0.6844 33.0 33 0.6912 0.5
0.6844 34.0 34 0.6908 0.5
0.6844 35.0 35 0.6904 0.5
0.6844 36.0 36 0.6900 0.5
0.6844 37.0 37 0.6896 0.5
0.6844 38.0 38 0.6892 0.5
0.6844 39.0 39 0.6887 0.5
0.6747 40.0 40 0.6883 0.5
0.6747 41.0 41 0.6879 0.5
0.6747 42.0 42 0.6875 0.5
0.6747 43.0 43 0.6871 0.5
0.6747 44.0 44 0.6867 0.5
0.6747 45.0 45 0.6864 0.5
0.6747 46.0 46 0.6860 0.5
0.6747 47.0 47 0.6856 0.5312
0.6747 48.0 48 0.6852 0.5625
0.6747 49.0 49 0.6849 0.5625
0.6545 50.0 50 0.6845 0.5625
0.6545 51.0 51 0.6841 0.5625
0.6545 52.0 52 0.6838 0.5625
0.6545 53.0 53 0.6834 0.5625
0.6545 54.0 54 0.6830 0.5625
0.6545 55.0 55 0.6826 0.5938
0.6545 56.0 56 0.6823 0.5938
0.6545 57.0 57 0.6819 0.625
0.6545 58.0 58 0.6815 0.6562
0.6545 59.0 59 0.6811 0.6562
0.6293 60.0 60 0.6808 0.6875
0.6293 61.0 61 0.6806 0.7188
0.6293 62.0 62 0.6806 0.7188
0.6293 63.0 63 0.6805 0.75
0.6293 64.0 64 0.6805 0.75
0.6293 65.0 65 0.6804 0.75
0.6293 66.0 66 0.6802 0.7188
0.6293 67.0 67 0.6799 0.7188
0.6293 68.0 68 0.6796 0.6875
0.6293 69.0 69 0.6792 0.7188
0.5938 70.0 70 0.6789 0.6875
0.5938 71.0 71 0.6787 0.6875
0.5938 72.0 72 0.6789 0.6562
0.5938 73.0 73 0.6797 0.6875
0.5938 74.0 74 0.6809 0.6875
0.5938 75.0 75 0.6818 0.6875
0.5938 76.0 76 0.6819 0.6875
0.5938 77.0 77 0.6811 0.6875
0.5938 78.0 78 0.6800 0.6875
0.5938 79.0 79 0.6790 0.6875
0.5521 80.0 80 0.6786 0.6875
0.5521 81.0 81 0.6785 0.6875
0.5521 82.0 82 0.6783 0.6562
0.5521 83.0 83 0.6781 0.6562
0.5521 84.0 84 0.6780 0.625
0.5521 85.0 85 0.6780 0.625
0.5521 86.0 86 0.6781 0.625
0.5521 87.0 87 0.6783 0.625
0.5521 88.0 88 0.6788 0.625
0.5521 89.0 89 0.6793 0.625
0.4936 90.0 90 0.6800 0.625
0.4936 91.0 91 0.6804 0.625
0.4936 92.0 92 0.6797 0.625
0.4936 93.0 93 0.6779 0.625
0.4936 94.0 94 0.6759 0.625
0.4936 95.0 95 0.6745 0.625
0.4936 96.0 96 0.6734 0.625
0.4936 97.0 97 0.6721 0.625
0.4936 98.0 98 0.6711 0.625
0.4936 99.0 99 0.6704 0.5938
0.4373 100.0 100 0.6697 0.5938
0.4373 101.0 101 0.6693 0.5625
0.4373 102.0 102 0.6694 0.5625
0.4373 103.0 103 0.6701 0.5625
0.4373 104.0 104 0.6707 0.5625
0.4373 105.0 105 0.6711 0.5625
0.4373 106.0 106 0.6716 0.5625
0.4373 107.0 107 0.6720 0.5625
0.4373 108.0 108 0.6725 0.5938
0.4373 109.0 109 0.6731 0.5938
0.3728 110.0 110 0.6742 0.5938
0.3728 111.0 111 0.6755 0.5938
0.3728 112.0 112 0.6773 0.5938
0.3728 113.0 113 0.6789 0.5938
0.3728 114.0 114 0.6801 0.5938
0.3728 115.0 115 0.6809 0.5938
0.3728 116.0 116 0.6815 0.5938
0.3728 117.0 117 0.6815 0.5938
0.3728 118.0 118 0.6811 0.5938
0.3728 119.0 119 0.6800 0.5938
0.3198 120.0 120 0.6787 0.5938
0.3198 121.0 121 0.6775 0.625
0.3198 122.0 122 0.6765 0.625
0.3198 123.0 123 0.6760 0.625
0.3198 124.0 124 0.6758 0.625
0.3198 125.0 125 0.6756 0.625
0.3198 126.0 126 0.6754 0.625
0.3198 127.0 127 0.6754 0.625
0.3198 128.0 128 0.6753 0.625
0.3198 129.0 129 0.6751 0.625
0.2713 130.0 130 0.6750 0.625
0.2713 131.0 131 0.6748 0.625
0.2713 132.0 132 0.6747 0.625
0.2713 133.0 133 0.6747 0.625
0.2713 134.0 134 0.6745 0.625
0.2713 135.0 135 0.6738 0.625
0.2713 136.0 136 0.6731 0.625
0.2713 137.0 137 0.6723 0.625
0.2713 138.0 138 0.6719 0.625
0.2713 139.0 139 0.6717 0.625
0.2331 140.0 140 0.6717 0.6562
0.2331 141.0 141 0.6719 0.6562
0.2331 142.0 142 0.6722 0.6562
0.2331 143.0 143 0.6725 0.6562
0.2331 144.0 144 0.6732 0.6562
0.2331 145.0 145 0.6744 0.6562
0.2331 146.0 146 0.6764 0.625
0.2331 147.0 147 0.6793 0.625
0.2331 148.0 148 0.6816 0.625
0.2331 149.0 149 0.6833 0.625
0.2017 150.0 150 0.6847 0.625

Framework versions

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