--- license: apache-2.0 base_model: albert-base-v2 tags: - generated_from_trainer metrics: - accuracy model-index: - name: best_model-yelp_polarity-16-42 results: [] --- # best_model-yelp_polarity-16-42 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: 0.6276 - 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 | 0.7035 | 0.8438 | | No log | 2.0 | 2 | 0.7063 | 0.8438 | | No log | 3.0 | 3 | 0.7119 | 0.8438 | | No log | 4.0 | 4 | 0.7198 | 0.8438 | | No log | 5.0 | 5 | 0.7298 | 0.8125 | | No log | 6.0 | 6 | 0.7415 | 0.8125 | | No log | 7.0 | 7 | 0.7542 | 0.8125 | | No log | 8.0 | 8 | 0.7678 | 0.8125 | | No log | 9.0 | 9 | 0.7815 | 0.8125 | | 0.4002 | 10.0 | 10 | 0.7947 | 0.8125 | | 0.4002 | 11.0 | 11 | 0.8066 | 0.8125 | | 0.4002 | 12.0 | 12 | 0.8164 | 0.8125 | | 0.4002 | 13.0 | 13 | 0.8228 | 0.8125 | | 0.4002 | 14.0 | 14 | 0.8259 | 0.8125 | | 0.4002 | 15.0 | 15 | 0.8260 | 0.8125 | | 0.4002 | 16.0 | 16 | 0.8231 | 0.8125 | | 0.4002 | 17.0 | 17 | 0.8172 | 0.8125 | | 0.4002 | 18.0 | 18 | 0.8084 | 0.8125 | | 0.4002 | 19.0 | 19 | 0.7968 | 0.8125 | | 0.3498 | 20.0 | 20 | 0.7826 | 0.8125 | | 0.3498 | 21.0 | 21 | 0.7660 | 0.8125 | | 0.3498 | 22.0 | 22 | 0.7474 | 0.8438 | | 0.3498 | 23.0 | 23 | 0.7272 | 0.8438 | | 0.3498 | 24.0 | 24 | 0.7053 | 0.8438 | | 0.3498 | 25.0 | 25 | 0.6813 | 0.8438 | | 0.3498 | 26.0 | 26 | 0.6547 | 0.8438 | | 0.3498 | 27.0 | 27 | 0.6255 | 0.8438 | | 0.3498 | 28.0 | 28 | 0.5952 | 0.8438 | | 0.3498 | 29.0 | 29 | 0.5656 | 0.8125 | | 0.2773 | 30.0 | 30 | 0.5407 | 0.8125 | | 0.2773 | 31.0 | 31 | 0.5221 | 0.8125 | | 0.2773 | 32.0 | 32 | 0.5096 | 0.8125 | | 0.2773 | 33.0 | 33 | 0.5026 | 0.8125 | | 0.2773 | 34.0 | 34 | 0.5080 | 0.8125 | | 0.2773 | 35.0 | 35 | 0.5248 | 0.8125 | | 0.2773 | 36.0 | 36 | 0.5517 | 0.8125 | | 0.2773 | 37.0 | 37 | 0.5838 | 0.8125 | | 0.2773 | 38.0 | 38 | 0.6122 | 0.8125 | | 0.2773 | 39.0 | 39 | 0.6332 | 0.8125 | | 0.1446 | 40.0 | 40 | 0.6455 | 0.8125 | | 0.1446 | 41.0 | 41 | 0.6491 | 0.8125 | | 0.1446 | 42.0 | 42 | 0.6449 | 0.8125 | | 0.1446 | 43.0 | 43 | 0.6330 | 0.8125 | | 0.1446 | 44.0 | 44 | 0.6121 | 0.8125 | | 0.1446 | 45.0 | 45 | 0.5814 | 0.8125 | | 0.1446 | 46.0 | 46 | 0.5390 | 0.8125 | | 0.1446 | 47.0 | 47 | 0.4913 | 0.8125 | | 0.1446 | 48.0 | 48 | 0.4598 | 0.8125 | | 0.1446 | 49.0 | 49 | 0.4469 | 0.8438 | | 0.066 | 50.0 | 50 | 0.4535 | 0.8438 | | 0.066 | 51.0 | 51 | 0.4775 | 0.8125 | | 0.066 | 52.0 | 52 | 0.5153 | 0.8125 | | 0.066 | 53.0 | 53 | 0.5618 | 0.8125 | | 0.066 | 54.0 | 54 | 0.6090 | 0.8125 | | 0.066 | 55.0 | 55 | 0.6490 | 0.8125 | | 0.066 | 56.0 | 56 | 0.6785 | 0.8125 | | 0.066 | 57.0 | 57 | 0.6962 | 0.8125 | | 0.066 | 58.0 | 58 | 0.7045 | 0.8125 | | 0.066 | 59.0 | 59 | 0.7056 | 0.8125 | | 0.0171 | 60.0 | 60 | 0.7001 | 0.8125 | | 0.0171 | 61.0 | 61 | 0.6878 | 0.8125 | | 0.0171 | 62.0 | 62 | 0.6688 | 0.8125 | | 0.0171 | 63.0 | 63 | 0.6427 | 0.8125 | | 0.0171 | 64.0 | 64 | 0.6110 | 0.8125 | | 0.0171 | 65.0 | 65 | 0.5764 | 0.8125 | | 0.0171 | 66.0 | 66 | 0.5422 | 0.8125 | | 0.0171 | 67.0 | 67 | 0.5147 | 0.8125 | | 0.0171 | 68.0 | 68 | 0.4976 | 0.8125 | | 0.0171 | 69.0 | 69 | 0.4883 | 0.8125 | | 0.0058 | 70.0 | 70 | 0.4876 | 0.8438 | | 0.0058 | 71.0 | 71 | 0.4932 | 0.8438 | | 0.0058 | 72.0 | 72 | 0.5018 | 0.8438 | | 0.0058 | 73.0 | 73 | 0.5127 | 0.8125 | | 0.0058 | 74.0 | 74 | 0.5251 | 0.8125 | | 0.0058 | 75.0 | 75 | 0.5385 | 0.8125 | | 0.0058 | 76.0 | 76 | 0.5517 | 0.8125 | | 0.0058 | 77.0 | 77 | 0.5644 | 0.8125 | | 0.0058 | 78.0 | 78 | 0.5758 | 0.8125 | | 0.0058 | 79.0 | 79 | 0.5858 | 0.8125 | | 0.0037 | 80.0 | 80 | 0.5941 | 0.8125 | | 0.0037 | 81.0 | 81 | 0.6009 | 0.8125 | | 0.0037 | 82.0 | 82 | 0.6064 | 0.8125 | | 0.0037 | 83.0 | 83 | 0.6102 | 0.8125 | | 0.0037 | 84.0 | 84 | 0.6119 | 0.8125 | | 0.0037 | 85.0 | 85 | 0.6123 | 0.8125 | | 0.0037 | 86.0 | 86 | 0.6108 | 0.8125 | | 0.0037 | 87.0 | 87 | 0.6081 | 0.8125 | | 0.0037 | 88.0 | 88 | 0.6040 | 0.8125 | | 0.0037 | 89.0 | 89 | 0.5987 | 0.8125 | | 0.0028 | 90.0 | 90 | 0.5923 | 0.8125 | | 0.0028 | 91.0 | 91 | 0.5853 | 0.8125 | | 0.0028 | 92.0 | 92 | 0.5779 | 0.8125 | | 0.0028 | 93.0 | 93 | 0.5703 | 0.8125 | | 0.0028 | 94.0 | 94 | 0.5627 | 0.8125 | | 0.0028 | 95.0 | 95 | 0.5552 | 0.8125 | | 0.0028 | 96.0 | 96 | 0.5481 | 0.8438 | | 0.0028 | 97.0 | 97 | 0.5417 | 0.8438 | | 0.0028 | 98.0 | 98 | 0.5365 | 0.8438 | | 0.0028 | 99.0 | 99 | 0.5318 | 0.8438 | | 0.0023 | 100.0 | 100 | 0.5280 | 0.8438 | | 0.0023 | 101.0 | 101 | 0.5249 | 0.8438 | | 0.0023 | 102.0 | 102 | 0.5220 | 0.8438 | | 0.0023 | 103.0 | 103 | 0.5198 | 0.8438 | | 0.0023 | 104.0 | 104 | 0.5180 | 0.8438 | | 0.0023 | 105.0 | 105 | 0.5169 | 0.8438 | | 0.0023 | 106.0 | 106 | 0.5167 | 0.8438 | | 0.0023 | 107.0 | 107 | 0.5172 | 0.8438 | | 0.0023 | 108.0 | 108 | 0.5184 | 0.8438 | | 0.0023 | 109.0 | 109 | 0.5203 | 0.8438 | | 0.0019 | 110.0 | 110 | 0.5224 | 0.8438 | | 0.0019 | 111.0 | 111 | 0.5249 | 0.8438 | | 0.0019 | 112.0 | 112 | 0.5278 | 0.8438 | | 0.0019 | 113.0 | 113 | 0.5309 | 0.8438 | | 0.0019 | 114.0 | 114 | 0.5343 | 0.8438 | | 0.0019 | 115.0 | 115 | 0.5381 | 0.8438 | | 0.0019 | 116.0 | 116 | 0.5422 | 0.8438 | | 0.0019 | 117.0 | 117 | 0.5467 | 0.8438 | | 0.0019 | 118.0 | 118 | 0.5514 | 0.8125 | | 0.0019 | 119.0 | 119 | 0.5561 | 0.8125 | | 0.0016 | 120.0 | 120 | 0.5609 | 0.8125 | | 0.0016 | 121.0 | 121 | 0.5655 | 0.8125 | | 0.0016 | 122.0 | 122 | 0.5703 | 0.8125 | | 0.0016 | 123.0 | 123 | 0.5750 | 0.8125 | | 0.0016 | 124.0 | 124 | 0.5796 | 0.8125 | | 0.0016 | 125.0 | 125 | 0.5838 | 0.8125 | | 0.0016 | 126.0 | 126 | 0.5877 | 0.8125 | | 0.0016 | 127.0 | 127 | 0.5915 | 0.8125 | | 0.0016 | 128.0 | 128 | 0.5950 | 0.8125 | | 0.0016 | 129.0 | 129 | 0.5978 | 0.8125 | | 0.0013 | 130.0 | 130 | 0.6002 | 0.8125 | | 0.0013 | 131.0 | 131 | 0.6024 | 0.8125 | | 0.0013 | 132.0 | 132 | 0.6045 | 0.8125 | | 0.0013 | 133.0 | 133 | 0.6065 | 0.8125 | | 0.0013 | 134.0 | 134 | 0.6082 | 0.8125 | | 0.0013 | 135.0 | 135 | 0.6097 | 0.8125 | | 0.0013 | 136.0 | 136 | 0.6113 | 0.8125 | | 0.0013 | 137.0 | 137 | 0.6125 | 0.8125 | | 0.0013 | 138.0 | 138 | 0.6136 | 0.8125 | | 0.0013 | 139.0 | 139 | 0.6148 | 0.8125 | | 0.0012 | 140.0 | 140 | 0.6158 | 0.8125 | | 0.0012 | 141.0 | 141 | 0.6165 | 0.8125 | | 0.0012 | 142.0 | 142 | 0.6172 | 0.8125 | | 0.0012 | 143.0 | 143 | 0.6180 | 0.8125 | | 0.0012 | 144.0 | 144 | 0.6190 | 0.8125 | | 0.0012 | 145.0 | 145 | 0.6201 | 0.8125 | | 0.0012 | 146.0 | 146 | 0.6215 | 0.8125 | | 0.0012 | 147.0 | 147 | 0.6227 | 0.8125 | | 0.0012 | 148.0 | 148 | 0.6239 | 0.8125 | | 0.0012 | 149.0 | 149 | 0.6256 | 0.8125 | | 0.001 | 150.0 | 150 | 0.6276 | 0.8125 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3