--- 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](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5420 - Accuracy: 0.8125 ## 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 | 0.6945 | 0.5938 | | No log | 2.0 | 2 | 0.6945 | 0.5938 | | No log | 3.0 | 3 | 0.6945 | 0.5938 | | No log | 4.0 | 4 | 0.6944 | 0.5938 | | No log | 5.0 | 5 | 0.6944 | 0.5938 | | No log | 6.0 | 6 | 0.6944 | 0.5938 | | No log | 7.0 | 7 | 0.6944 | 0.5938 | | No log | 8.0 | 8 | 0.6943 | 0.5938 | | No log | 9.0 | 9 | 0.6943 | 0.5938 | | 0.7032 | 10.0 | 10 | 0.6943 | 0.5938 | | 0.7032 | 11.0 | 11 | 0.6942 | 0.5938 | | 0.7032 | 12.0 | 12 | 0.6942 | 0.5938 | | 0.7032 | 13.0 | 13 | 0.6941 | 0.5938 | | 0.7032 | 14.0 | 14 | 0.6940 | 0.5938 | | 0.7032 | 15.0 | 15 | 0.6940 | 0.5938 | | 0.7032 | 16.0 | 16 | 0.6939 | 0.5938 | | 0.7032 | 17.0 | 17 | 0.6938 | 0.5938 | | 0.7032 | 18.0 | 18 | 0.6937 | 0.5938 | | 0.7032 | 19.0 | 19 | 0.6936 | 0.5938 | | 0.709 | 20.0 | 20 | 0.6935 | 0.5938 | | 0.709 | 21.0 | 21 | 0.6934 | 0.5938 | | 0.709 | 22.0 | 22 | 0.6933 | 0.5938 | | 0.709 | 23.0 | 23 | 0.6932 | 0.5938 | | 0.709 | 24.0 | 24 | 0.6931 | 0.5938 | | 0.709 | 25.0 | 25 | 0.6930 | 0.5938 | | 0.709 | 26.0 | 26 | 0.6928 | 0.5938 | | 0.709 | 27.0 | 27 | 0.6927 | 0.5938 | | 0.709 | 28.0 | 28 | 0.6926 | 0.5938 | | 0.709 | 29.0 | 29 | 0.6924 | 0.5938 | | 0.6984 | 30.0 | 30 | 0.6923 | 0.5938 | | 0.6984 | 31.0 | 31 | 0.6921 | 0.5938 | | 0.6984 | 32.0 | 32 | 0.6920 | 0.5938 | | 0.6984 | 33.0 | 33 | 0.6918 | 0.5938 | | 0.6984 | 34.0 | 34 | 0.6916 | 0.5938 | | 0.6984 | 35.0 | 35 | 0.6915 | 0.5938 | | 0.6984 | 36.0 | 36 | 0.6913 | 0.5938 | | 0.6984 | 37.0 | 37 | 0.6911 | 0.5938 | | 0.6984 | 38.0 | 38 | 0.6909 | 0.5938 | | 0.6984 | 39.0 | 39 | 0.6907 | 0.5938 | | 0.6833 | 40.0 | 40 | 0.6905 | 0.5938 | | 0.6833 | 41.0 | 41 | 0.6903 | 0.5938 | | 0.6833 | 42.0 | 42 | 0.6901 | 0.5938 | | 0.6833 | 43.0 | 43 | 0.6899 | 0.5938 | | 0.6833 | 44.0 | 44 | 0.6897 | 0.5938 | | 0.6833 | 45.0 | 45 | 0.6895 | 0.5938 | | 0.6833 | 46.0 | 46 | 0.6893 | 0.5938 | | 0.6833 | 47.0 | 47 | 0.6890 | 0.5938 | | 0.6833 | 48.0 | 48 | 0.6888 | 0.5938 | | 0.6833 | 49.0 | 49 | 0.6885 | 0.5938 | | 0.6831 | 50.0 | 50 | 0.6882 | 0.5938 | | 0.6831 | 51.0 | 51 | 0.6879 | 0.5938 | | 0.6831 | 52.0 | 52 | 0.6876 | 0.5938 | | 0.6831 | 53.0 | 53 | 0.6873 | 0.5938 | | 0.6831 | 54.0 | 54 | 0.6870 | 0.5938 | | 0.6831 | 55.0 | 55 | 0.6867 | 0.625 | | 0.6831 | 56.0 | 56 | 0.6863 | 0.625 | | 0.6831 | 57.0 | 57 | 0.6860 | 0.625 | | 0.6831 | 58.0 | 58 | 0.6856 | 0.625 | | 0.6831 | 59.0 | 59 | 0.6852 | 0.625 | | 0.669 | 60.0 | 60 | 0.6848 | 0.625 | | 0.669 | 61.0 | 61 | 0.6844 | 0.625 | | 0.669 | 62.0 | 62 | 0.6839 | 0.625 | | 0.669 | 63.0 | 63 | 0.6835 | 0.625 | | 0.669 | 64.0 | 64 | 0.6830 | 0.625 | | 0.669 | 65.0 | 65 | 0.6824 | 0.625 | | 0.669 | 66.0 | 66 | 0.6819 | 0.625 | | 0.669 | 67.0 | 67 | 0.6814 | 0.625 | | 0.669 | 68.0 | 68 | 0.6808 | 0.625 | | 0.669 | 69.0 | 69 | 0.6802 | 0.625 | | 0.6556 | 70.0 | 70 | 0.6796 | 0.625 | | 0.6556 | 71.0 | 71 | 0.6789 | 0.625 | | 0.6556 | 72.0 | 72 | 0.6782 | 0.625 | | 0.6556 | 73.0 | 73 | 0.6774 | 0.625 | | 0.6556 | 74.0 | 74 | 0.6766 | 0.6562 | | 0.6556 | 75.0 | 75 | 0.6757 | 0.6562 | | 0.6556 | 76.0 | 76 | 0.6747 | 0.6562 | | 0.6556 | 77.0 | 77 | 0.6736 | 0.6562 | | 0.6556 | 78.0 | 78 | 0.6725 | 0.6562 | | 0.6556 | 79.0 | 79 | 0.6713 | 0.6562 | | 0.6248 | 80.0 | 80 | 0.6700 | 0.6562 | | 0.6248 | 81.0 | 81 | 0.6687 | 0.6562 | | 0.6248 | 82.0 | 82 | 0.6673 | 0.6562 | | 0.6248 | 83.0 | 83 | 0.6660 | 0.6562 | | 0.6248 | 84.0 | 84 | 0.6647 | 0.6562 | | 0.6248 | 85.0 | 85 | 0.6635 | 0.6562 | | 0.6248 | 86.0 | 86 | 0.6622 | 0.6562 | | 0.6248 | 87.0 | 87 | 0.6603 | 0.5938 | | 0.6248 | 88.0 | 88 | 0.6586 | 0.5938 | | 0.6248 | 89.0 | 89 | 0.6574 | 0.5938 | | 0.6013 | 90.0 | 90 | 0.6565 | 0.5938 | | 0.6013 | 91.0 | 91 | 0.6557 | 0.5938 | | 0.6013 | 92.0 | 92 | 0.6548 | 0.5938 | | 0.6013 | 93.0 | 93 | 0.6541 | 0.625 | | 0.6013 | 94.0 | 94 | 0.6538 | 0.625 | | 0.6013 | 95.0 | 95 | 0.6537 | 0.625 | | 0.6013 | 96.0 | 96 | 0.6531 | 0.625 | | 0.6013 | 97.0 | 97 | 0.6522 | 0.625 | | 0.6013 | 98.0 | 98 | 0.6518 | 0.625 | | 0.6013 | 99.0 | 99 | 0.6515 | 0.6562 | | 0.5622 | 100.0 | 100 | 0.6502 | 0.6562 | | 0.5622 | 101.0 | 101 | 0.6481 | 0.6562 | | 0.5622 | 102.0 | 102 | 0.6457 | 0.6562 | | 0.5622 | 103.0 | 103 | 0.6434 | 0.6562 | | 0.5622 | 104.0 | 104 | 0.6411 | 0.6562 | | 0.5622 | 105.0 | 105 | 0.6384 | 0.7188 | | 0.5622 | 106.0 | 106 | 0.6362 | 0.6875 | | 0.5622 | 107.0 | 107 | 0.6338 | 0.6875 | | 0.5622 | 108.0 | 108 | 0.6311 | 0.6875 | | 0.5622 | 109.0 | 109 | 0.6281 | 0.6562 | | 0.5022 | 110.0 | 110 | 0.6236 | 0.6875 | | 0.5022 | 111.0 | 111 | 0.6193 | 0.6875 | | 0.5022 | 112.0 | 112 | 0.6141 | 0.6562 | | 0.5022 | 113.0 | 113 | 0.6088 | 0.6875 | | 0.5022 | 114.0 | 114 | 0.6046 | 0.6875 | | 0.5022 | 115.0 | 115 | 0.6024 | 0.6875 | | 0.5022 | 116.0 | 116 | 0.6014 | 0.6875 | | 0.5022 | 117.0 | 117 | 0.6004 | 0.6875 | | 0.5022 | 118.0 | 118 | 0.5993 | 0.6875 | | 0.5022 | 119.0 | 119 | 0.5982 | 0.6875 | | 0.4576 | 120.0 | 120 | 0.5969 | 0.6875 | | 0.4576 | 121.0 | 121 | 0.5957 | 0.6875 | | 0.4576 | 122.0 | 122 | 0.5944 | 0.7188 | | 0.4576 | 123.0 | 123 | 0.5929 | 0.7188 | | 0.4576 | 124.0 | 124 | 0.5916 | 0.7188 | | 0.4576 | 125.0 | 125 | 0.5903 | 0.7188 | | 0.4576 | 126.0 | 126 | 0.5887 | 0.7188 | | 0.4576 | 127.0 | 127 | 0.5873 | 0.7188 | | 0.4576 | 128.0 | 128 | 0.5857 | 0.75 | | 0.4576 | 129.0 | 129 | 0.5837 | 0.75 | | 0.4105 | 130.0 | 130 | 0.5819 | 0.75 | | 0.4105 | 131.0 | 131 | 0.5797 | 0.75 | | 0.4105 | 132.0 | 132 | 0.5781 | 0.75 | | 0.4105 | 133.0 | 133 | 0.5770 | 0.75 | | 0.4105 | 134.0 | 134 | 0.5756 | 0.75 | | 0.4105 | 135.0 | 135 | 0.5734 | 0.75 | | 0.4105 | 136.0 | 136 | 0.5714 | 0.75 | | 0.4105 | 137.0 | 137 | 0.5694 | 0.75 | | 0.4105 | 138.0 | 138 | 0.5673 | 0.75 | | 0.4105 | 139.0 | 139 | 0.5651 | 0.75 | | 0.3744 | 140.0 | 140 | 0.5628 | 0.75 | | 0.3744 | 141.0 | 141 | 0.5605 | 0.7812 | | 0.3744 | 142.0 | 142 | 0.5581 | 0.7812 | | 0.3744 | 143.0 | 143 | 0.5555 | 0.7812 | | 0.3744 | 144.0 | 144 | 0.5532 | 0.7812 | | 0.3744 | 145.0 | 145 | 0.5510 | 0.7812 | | 0.3744 | 146.0 | 146 | 0.5489 | 0.7812 | | 0.3744 | 147.0 | 147 | 0.5470 | 0.7812 | | 0.3744 | 148.0 | 148 | 0.5453 | 0.7812 | | 0.3744 | 149.0 | 149 | 0.5435 | 0.7812 | | 0.3294 | 150.0 | 150 | 0.5420 | 0.8125 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3