--- license: mit base_model: roberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-base-sst-2-32-13 results: [] --- # roberta-base-sst-2-32-13 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9957 - 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 | 2 | 0.6951 | 0.5 | | No log | 2.0 | 4 | 0.6951 | 0.5 | | No log | 3.0 | 6 | 0.6951 | 0.5 | | No log | 4.0 | 8 | 0.6951 | 0.5 | | 0.6937 | 5.0 | 10 | 0.6950 | 0.5 | | 0.6937 | 6.0 | 12 | 0.6950 | 0.5 | | 0.6937 | 7.0 | 14 | 0.6950 | 0.5 | | 0.6937 | 8.0 | 16 | 0.6950 | 0.5 | | 0.6937 | 9.0 | 18 | 0.6949 | 0.5 | | 0.6953 | 10.0 | 20 | 0.6949 | 0.5 | | 0.6953 | 11.0 | 22 | 0.6949 | 0.5 | | 0.6953 | 12.0 | 24 | 0.6948 | 0.5 | | 0.6953 | 13.0 | 26 | 0.6948 | 0.5 | | 0.6953 | 14.0 | 28 | 0.6947 | 0.5 | | 0.6975 | 15.0 | 30 | 0.6947 | 0.5 | | 0.6975 | 16.0 | 32 | 0.6946 | 0.5 | | 0.6975 | 17.0 | 34 | 0.6946 | 0.5 | | 0.6975 | 18.0 | 36 | 0.6945 | 0.5 | | 0.6975 | 19.0 | 38 | 0.6944 | 0.5 | | 0.6888 | 20.0 | 40 | 0.6944 | 0.5 | | 0.6888 | 21.0 | 42 | 0.6943 | 0.5 | | 0.6888 | 22.0 | 44 | 0.6942 | 0.5 | | 0.6888 | 23.0 | 46 | 0.6942 | 0.5 | | 0.6888 | 24.0 | 48 | 0.6941 | 0.5 | | 0.6947 | 25.0 | 50 | 0.6940 | 0.5 | | 0.6947 | 26.0 | 52 | 0.6940 | 0.5 | | 0.6947 | 27.0 | 54 | 0.6939 | 0.5 | | 0.6947 | 28.0 | 56 | 0.6938 | 0.5 | | 0.6947 | 29.0 | 58 | 0.6937 | 0.5 | | 0.69 | 30.0 | 60 | 0.6937 | 0.5 | | 0.69 | 31.0 | 62 | 0.6936 | 0.5 | | 0.69 | 32.0 | 64 | 0.6936 | 0.5 | | 0.69 | 33.0 | 66 | 0.6935 | 0.5 | | 0.69 | 34.0 | 68 | 0.6934 | 0.5 | | 0.6901 | 35.0 | 70 | 0.6933 | 0.5 | | 0.6901 | 36.0 | 72 | 0.6932 | 0.5 | | 0.6901 | 37.0 | 74 | 0.6931 | 0.5 | | 0.6901 | 38.0 | 76 | 0.6930 | 0.5 | | 0.6901 | 39.0 | 78 | 0.6929 | 0.5 | | 0.6895 | 40.0 | 80 | 0.6928 | 0.5 | | 0.6895 | 41.0 | 82 | 0.6927 | 0.5 | | 0.6895 | 42.0 | 84 | 0.6926 | 0.5 | | 0.6895 | 43.0 | 86 | 0.6925 | 0.5 | | 0.6895 | 44.0 | 88 | 0.6924 | 0.5 | | 0.6874 | 45.0 | 90 | 0.6922 | 0.5 | | 0.6874 | 46.0 | 92 | 0.6921 | 0.5 | | 0.6874 | 47.0 | 94 | 0.6919 | 0.5 | | 0.6874 | 48.0 | 96 | 0.6917 | 0.5 | | 0.6874 | 49.0 | 98 | 0.6915 | 0.5 | | 0.6865 | 50.0 | 100 | 0.6913 | 0.5 | | 0.6865 | 51.0 | 102 | 0.6911 | 0.5 | | 0.6865 | 52.0 | 104 | 0.6908 | 0.5 | | 0.6865 | 53.0 | 106 | 0.6904 | 0.4844 | | 0.6865 | 54.0 | 108 | 0.6901 | 0.4688 | | 0.6818 | 55.0 | 110 | 0.6897 | 0.4688 | | 0.6818 | 56.0 | 112 | 0.6892 | 0.4531 | | 0.6818 | 57.0 | 114 | 0.6887 | 0.5625 | | 0.6818 | 58.0 | 116 | 0.6880 | 0.6094 | | 0.6818 | 59.0 | 118 | 0.6872 | 0.6406 | | 0.6697 | 60.0 | 120 | 0.6863 | 0.6406 | | 0.6697 | 61.0 | 122 | 0.6852 | 0.6875 | | 0.6697 | 62.0 | 124 | 0.6838 | 0.7656 | | 0.6697 | 63.0 | 126 | 0.6820 | 0.7812 | | 0.6697 | 64.0 | 128 | 0.6798 | 0.7656 | | 0.6559 | 65.0 | 130 | 0.6769 | 0.7656 | | 0.6559 | 66.0 | 132 | 0.6730 | 0.7188 | | 0.6559 | 67.0 | 134 | 0.6675 | 0.7344 | | 0.6559 | 68.0 | 136 | 0.6598 | 0.7188 | | 0.6559 | 69.0 | 138 | 0.6489 | 0.7188 | | 0.6085 | 70.0 | 140 | 0.6343 | 0.7188 | | 0.6085 | 71.0 | 142 | 0.6161 | 0.7656 | | 0.6085 | 72.0 | 144 | 0.5928 | 0.8125 | | 0.6085 | 73.0 | 146 | 0.5652 | 0.8438 | | 0.6085 | 74.0 | 148 | 0.5367 | 0.8594 | | 0.474 | 75.0 | 150 | 0.5083 | 0.8438 | | 0.474 | 76.0 | 152 | 0.4779 | 0.8438 | | 0.474 | 77.0 | 154 | 0.4473 | 0.8594 | | 0.474 | 78.0 | 156 | 0.4179 | 0.8594 | | 0.474 | 79.0 | 158 | 0.3930 | 0.875 | | 0.2428 | 80.0 | 160 | 0.3782 | 0.8594 | | 0.2428 | 81.0 | 162 | 0.3734 | 0.8438 | | 0.2428 | 82.0 | 164 | 0.3731 | 0.8594 | | 0.2428 | 83.0 | 166 | 0.3816 | 0.875 | | 0.2428 | 84.0 | 168 | 0.4042 | 0.8438 | | 0.0805 | 85.0 | 170 | 0.4405 | 0.8438 | | 0.0805 | 86.0 | 172 | 0.4840 | 0.8281 | | 0.0805 | 87.0 | 174 | 0.5432 | 0.8125 | | 0.0805 | 88.0 | 176 | 0.6025 | 0.8125 | | 0.0805 | 89.0 | 178 | 0.6412 | 0.8125 | | 0.0222 | 90.0 | 180 | 0.6653 | 0.8125 | | 0.0222 | 91.0 | 182 | 0.6845 | 0.8125 | | 0.0222 | 92.0 | 184 | 0.6954 | 0.8125 | | 0.0222 | 93.0 | 186 | 0.7007 | 0.8281 | | 0.0222 | 94.0 | 188 | 0.7029 | 0.8438 | | 0.0093 | 95.0 | 190 | 0.7083 | 0.8438 | | 0.0093 | 96.0 | 192 | 0.7172 | 0.8594 | | 0.0093 | 97.0 | 194 | 0.7250 | 0.8594 | | 0.0093 | 98.0 | 196 | 0.7286 | 0.8594 | | 0.0093 | 99.0 | 198 | 0.7361 | 0.8594 | | 0.0058 | 100.0 | 200 | 0.7447 | 0.8594 | | 0.0058 | 101.0 | 202 | 0.7544 | 0.8594 | | 0.0058 | 102.0 | 204 | 0.7632 | 0.8594 | | 0.0058 | 103.0 | 206 | 0.7724 | 0.8594 | | 0.0058 | 104.0 | 208 | 0.7842 | 0.8594 | | 0.0041 | 105.0 | 210 | 0.7955 | 0.8594 | | 0.0041 | 106.0 | 212 | 0.8061 | 0.8594 | | 0.0041 | 107.0 | 214 | 0.8164 | 0.8594 | | 0.0041 | 108.0 | 216 | 0.8262 | 0.8594 | | 0.0041 | 109.0 | 218 | 0.8348 | 0.8594 | | 0.0032 | 110.0 | 220 | 0.8438 | 0.8594 | | 0.0032 | 111.0 | 222 | 0.8514 | 0.8594 | | 0.0032 | 112.0 | 224 | 0.8582 | 0.8594 | | 0.0032 | 113.0 | 226 | 0.8650 | 0.8594 | | 0.0032 | 114.0 | 228 | 0.8718 | 0.8438 | | 0.0028 | 115.0 | 230 | 0.8777 | 0.8438 | | 0.0028 | 116.0 | 232 | 0.8829 | 0.8438 | | 0.0028 | 117.0 | 234 | 0.8884 | 0.8438 | | 0.0028 | 118.0 | 236 | 0.8938 | 0.8438 | | 0.0028 | 119.0 | 238 | 0.8986 | 0.8438 | | 0.0024 | 120.0 | 240 | 0.9023 | 0.8438 | | 0.0024 | 121.0 | 242 | 0.9055 | 0.8438 | | 0.0024 | 122.0 | 244 | 0.9087 | 0.8438 | | 0.0024 | 123.0 | 246 | 0.9121 | 0.8438 | | 0.0024 | 124.0 | 248 | 0.9165 | 0.8438 | | 0.0021 | 125.0 | 250 | 0.9209 | 0.8438 | | 0.0021 | 126.0 | 252 | 0.9258 | 0.8438 | | 0.0021 | 127.0 | 254 | 0.9303 | 0.8438 | | 0.0021 | 128.0 | 256 | 0.9338 | 0.8438 | | 0.0021 | 129.0 | 258 | 0.9365 | 0.8438 | | 0.0019 | 130.0 | 260 | 0.9395 | 0.8438 | | 0.0019 | 131.0 | 262 | 0.9426 | 0.8438 | | 0.0019 | 132.0 | 264 | 0.9448 | 0.8438 | | 0.0019 | 133.0 | 266 | 0.9463 | 0.8438 | | 0.0019 | 134.0 | 268 | 0.9480 | 0.8438 | | 0.0017 | 135.0 | 270 | 0.9506 | 0.8438 | | 0.0017 | 136.0 | 272 | 0.9535 | 0.8438 | | 0.0017 | 137.0 | 274 | 0.9561 | 0.8438 | | 0.0017 | 138.0 | 276 | 0.9579 | 0.8438 | | 0.0017 | 139.0 | 278 | 0.9596 | 0.8438 | | 0.0015 | 140.0 | 280 | 0.9618 | 0.8438 | | 0.0015 | 141.0 | 282 | 0.9650 | 0.8438 | | 0.0015 | 142.0 | 284 | 0.9682 | 0.8438 | | 0.0015 | 143.0 | 286 | 0.9712 | 0.8438 | | 0.0015 | 144.0 | 288 | 0.9741 | 0.8438 | | 0.0014 | 145.0 | 290 | 0.9769 | 0.8438 | | 0.0014 | 146.0 | 292 | 0.9801 | 0.8438 | | 0.0014 | 147.0 | 294 | 0.9835 | 0.8438 | | 0.0014 | 148.0 | 296 | 0.9872 | 0.8438 | | 0.0014 | 149.0 | 298 | 0.9911 | 0.8438 | | 0.0013 | 150.0 | 300 | 0.9957 | 0.8438 | ### Framework versions - Transformers 4.32.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.4.0 - Tokenizers 0.13.3