--- license: apache-2.0 base_model: albert-base-v2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-yelp_polarity-16-100 results: [] --- # best_model-yelp_polarity-16-100 This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1862 - 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 | 1.0619 | 0.8438 | | No log | 2.0 | 2 | 1.0610 | 0.8438 | | No log | 3.0 | 3 | 1.0591 | 0.8438 | | No log | 4.0 | 4 | 1.0563 | 0.8438 | | No log | 5.0 | 5 | 1.0524 | 0.8438 | | No log | 6.0 | 6 | 1.0473 | 0.8438 | | No log | 7.0 | 7 | 1.0408 | 0.8438 | | No log | 8.0 | 8 | 1.0325 | 0.8438 | | No log | 9.0 | 9 | 1.0221 | 0.8438 | | 0.5215 | 10.0 | 10 | 1.0093 | 0.8438 | | 0.5215 | 11.0 | 11 | 0.9939 | 0.8438 | | 0.5215 | 12.0 | 12 | 0.9775 | 0.8438 | | 0.5215 | 13.0 | 13 | 0.9630 | 0.8438 | | 0.5215 | 14.0 | 14 | 0.9517 | 0.8438 | | 0.5215 | 15.0 | 15 | 0.9431 | 0.8125 | | 0.5215 | 16.0 | 16 | 0.9352 | 0.7812 | | 0.5215 | 17.0 | 17 | 0.9263 | 0.7812 | | 0.5215 | 18.0 | 18 | 0.9195 | 0.7812 | | 0.5215 | 19.0 | 19 | 0.9178 | 0.7812 | | 0.3945 | 20.0 | 20 | 0.9230 | 0.8125 | | 0.3945 | 21.0 | 21 | 0.9374 | 0.8125 | | 0.3945 | 22.0 | 22 | 0.9628 | 0.8125 | | 0.3945 | 23.0 | 23 | 1.0035 | 0.8438 | | 0.3945 | 24.0 | 24 | 1.0608 | 0.8125 | | 0.3945 | 25.0 | 25 | 1.1258 | 0.8125 | | 0.3945 | 26.0 | 26 | 1.1859 | 0.8125 | | 0.3945 | 27.0 | 27 | 1.2311 | 0.8125 | | 0.3945 | 28.0 | 28 | 1.2580 | 0.8125 | | 0.3945 | 29.0 | 29 | 1.2702 | 0.8125 | | 0.2334 | 30.0 | 30 | 1.2750 | 0.8125 | | 0.2334 | 31.0 | 31 | 1.2763 | 0.8125 | | 0.2334 | 32.0 | 32 | 1.2763 | 0.8125 | | 0.2334 | 33.0 | 33 | 1.2757 | 0.8125 | | 0.2334 | 34.0 | 34 | 1.2733 | 0.8125 | | 0.2334 | 35.0 | 35 | 1.2687 | 0.8125 | | 0.2334 | 36.0 | 36 | 1.2612 | 0.8125 | | 0.2334 | 37.0 | 37 | 1.2508 | 0.8125 | | 0.2334 | 38.0 | 38 | 1.2376 | 0.8125 | | 0.2334 | 39.0 | 39 | 1.2213 | 0.8125 | | 0.024 | 40.0 | 40 | 1.2024 | 0.8125 | | 0.024 | 41.0 | 41 | 1.1803 | 0.8125 | | 0.024 | 42.0 | 42 | 1.1548 | 0.8125 | | 0.024 | 43.0 | 43 | 1.1254 | 0.8125 | | 0.024 | 44.0 | 44 | 1.0929 | 0.8125 | | 0.024 | 45.0 | 45 | 1.0591 | 0.8125 | | 0.024 | 46.0 | 46 | 1.0257 | 0.8125 | | 0.024 | 47.0 | 47 | 0.9942 | 0.8125 | | 0.024 | 48.0 | 48 | 0.9662 | 0.8125 | | 0.024 | 49.0 | 49 | 0.9436 | 0.8125 | | 0.0008 | 50.0 | 50 | 0.9266 | 0.8125 | | 0.0008 | 51.0 | 51 | 0.9148 | 0.8125 | | 0.0008 | 52.0 | 52 | 0.9073 | 0.8125 | | 0.0008 | 53.0 | 53 | 0.9039 | 0.8125 | | 0.0008 | 54.0 | 54 | 0.9049 | 0.8125 | | 0.0008 | 55.0 | 55 | 0.9087 | 0.8125 | | 0.0008 | 56.0 | 56 | 0.9152 | 0.8125 | | 0.0008 | 57.0 | 57 | 0.9238 | 0.8125 | | 0.0008 | 58.0 | 58 | 0.9340 | 0.8125 | | 0.0008 | 59.0 | 59 | 0.9450 | 0.8125 | | 0.0006 | 60.0 | 60 | 0.9566 | 0.8438 | | 0.0006 | 61.0 | 61 | 0.9682 | 0.8438 | | 0.0006 | 62.0 | 62 | 0.9797 | 0.8438 | | 0.0006 | 63.0 | 63 | 0.9912 | 0.8438 | | 0.0006 | 64.0 | 64 | 1.0028 | 0.8438 | | 0.0006 | 65.0 | 65 | 1.0141 | 0.8438 | | 0.0006 | 66.0 | 66 | 1.0251 | 0.8438 | | 0.0006 | 67.0 | 67 | 1.0358 | 0.8438 | | 0.0006 | 68.0 | 68 | 1.0460 | 0.8438 | | 0.0006 | 69.0 | 69 | 1.0558 | 0.8438 | | 0.0005 | 70.0 | 70 | 1.0646 | 0.8438 | | 0.0005 | 71.0 | 71 | 1.0730 | 0.8438 | | 0.0005 | 72.0 | 72 | 1.0808 | 0.8438 | | 0.0005 | 73.0 | 73 | 1.0882 | 0.8438 | | 0.0005 | 74.0 | 74 | 1.0951 | 0.8438 | | 0.0005 | 75.0 | 75 | 1.1013 | 0.8125 | | 0.0005 | 76.0 | 76 | 1.1070 | 0.8125 | | 0.0005 | 77.0 | 77 | 1.1122 | 0.8125 | | 0.0005 | 78.0 | 78 | 1.1170 | 0.8125 | | 0.0005 | 79.0 | 79 | 1.1214 | 0.8125 | | 0.0004 | 80.0 | 80 | 1.1255 | 0.8125 | | 0.0004 | 81.0 | 81 | 1.1292 | 0.8125 | | 0.0004 | 82.0 | 82 | 1.1324 | 0.8125 | | 0.0004 | 83.0 | 83 | 1.1354 | 0.8125 | | 0.0004 | 84.0 | 84 | 1.1383 | 0.8125 | | 0.0004 | 85.0 | 85 | 1.1411 | 0.8125 | | 0.0004 | 86.0 | 86 | 1.1437 | 0.8125 | | 0.0004 | 87.0 | 87 | 1.1462 | 0.8125 | | 0.0004 | 88.0 | 88 | 1.1484 | 0.8125 | | 0.0004 | 89.0 | 89 | 1.1506 | 0.8125 | | 0.0004 | 90.0 | 90 | 1.1527 | 0.8125 | | 0.0004 | 91.0 | 91 | 1.1546 | 0.8125 | | 0.0004 | 92.0 | 92 | 1.1563 | 0.8125 | | 0.0004 | 93.0 | 93 | 1.1579 | 0.8125 | | 0.0004 | 94.0 | 94 | 1.1596 | 0.8125 | | 0.0004 | 95.0 | 95 | 1.1611 | 0.8125 | | 0.0004 | 96.0 | 96 | 1.1624 | 0.8125 | | 0.0004 | 97.0 | 97 | 1.1636 | 0.8125 | | 0.0004 | 98.0 | 98 | 1.1648 | 0.8125 | | 0.0004 | 99.0 | 99 | 1.1658 | 0.8125 | | 0.0003 | 100.0 | 100 | 1.1668 | 0.8125 | | 0.0003 | 101.0 | 101 | 1.1678 | 0.8125 | | 0.0003 | 102.0 | 102 | 1.1689 | 0.8125 | | 0.0003 | 103.0 | 103 | 1.1697 | 0.8125 | | 0.0003 | 104.0 | 104 | 1.1706 | 0.8125 | | 0.0003 | 105.0 | 105 | 1.1715 | 0.8125 | | 0.0003 | 106.0 | 106 | 1.1722 | 0.8125 | | 0.0003 | 107.0 | 107 | 1.1728 | 0.8125 | | 0.0003 | 108.0 | 108 | 1.1734 | 0.8125 | | 0.0003 | 109.0 | 109 | 1.1739 | 0.8125 | | 0.0003 | 110.0 | 110 | 1.1745 | 0.8125 | | 0.0003 | 111.0 | 111 | 1.1749 | 0.8125 | | 0.0003 | 112.0 | 112 | 1.1754 | 0.8125 | | 0.0003 | 113.0 | 113 | 1.1759 | 0.8125 | | 0.0003 | 114.0 | 114 | 1.1764 | 0.8125 | | 0.0003 | 115.0 | 115 | 1.1768 | 0.8125 | | 0.0003 | 116.0 | 116 | 1.1772 | 0.8125 | | 0.0003 | 117.0 | 117 | 1.1774 | 0.8125 | | 0.0003 | 118.0 | 118 | 1.1776 | 0.8125 | | 0.0003 | 119.0 | 119 | 1.1776 | 0.8125 | | 0.0003 | 120.0 | 120 | 1.1778 | 0.8125 | | 0.0003 | 121.0 | 121 | 1.1780 | 0.8125 | | 0.0003 | 122.0 | 122 | 1.1781 | 0.8125 | | 0.0003 | 123.0 | 123 | 1.1783 | 0.8125 | | 0.0003 | 124.0 | 124 | 1.1784 | 0.8125 | | 0.0003 | 125.0 | 125 | 1.1787 | 0.8125 | | 0.0003 | 126.0 | 126 | 1.1790 | 0.8125 | | 0.0003 | 127.0 | 127 | 1.1794 | 0.8125 | | 0.0003 | 128.0 | 128 | 1.1797 | 0.8125 | | 0.0003 | 129.0 | 129 | 1.1800 | 0.8125 | | 0.0003 | 130.0 | 130 | 1.1803 | 0.8125 | | 0.0003 | 131.0 | 131 | 1.1807 | 0.8125 | | 0.0003 | 132.0 | 132 | 1.1809 | 0.8125 | | 0.0003 | 133.0 | 133 | 1.1812 | 0.8125 | | 0.0003 | 134.0 | 134 | 1.1815 | 0.8125 | | 0.0003 | 135.0 | 135 | 1.1818 | 0.8125 | | 0.0003 | 136.0 | 136 | 1.1823 | 0.8125 | | 0.0003 | 137.0 | 137 | 1.1828 | 0.8125 | | 0.0003 | 138.0 | 138 | 1.1832 | 0.8125 | | 0.0003 | 139.0 | 139 | 1.1835 | 0.8125 | | 0.0002 | 140.0 | 140 | 1.1837 | 0.8125 | | 0.0002 | 141.0 | 141 | 1.1838 | 0.8125 | | 0.0002 | 142.0 | 142 | 1.1840 | 0.8125 | | 0.0002 | 143.0 | 143 | 1.1841 | 0.8125 | | 0.0002 | 144.0 | 144 | 1.1844 | 0.8125 | | 0.0002 | 145.0 | 145 | 1.1845 | 0.8125 | | 0.0002 | 146.0 | 146 | 1.1848 | 0.8125 | | 0.0002 | 147.0 | 147 | 1.1851 | 0.8125 | | 0.0002 | 148.0 | 148 | 1.1855 | 0.8125 | | 0.0002 | 149.0 | 149 | 1.1859 | 0.8125 | | 0.0002 | 150.0 | 150 | 1.1862 | 0.8125 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3