--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: detect-femicide-news-xlmr-nl-fft-freeze2 results: [] --- # detect-femicide-news-xlmr-nl-fft-freeze2 This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4119 - Accuracy: 0.8571 - Precision Neg: 0.85 - Precision Pos: 0.875 - Recall Neg: 0.9444 - Recall Pos: 0.7 - F1 Score Neg: 0.8947 - F1 Score Pos: 0.7778 ## 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: 24 - eval_batch_size: 8 - seed: 1996 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Neg | Precision Pos | Recall Neg | Recall Pos | F1 Score Neg | F1 Score Pos | |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:-------------:|:----------:|:----------:|:------------:|:------------:| | 1.3215 | 1.0 | 23 | 1.0782 | 0.75 | 0.7391 | 0.8 | 0.9444 | 0.4 | 0.8293 | 0.5333 | | 1.0955 | 2.0 | 46 | 0.9057 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.9344 | 3.0 | 69 | 0.7420 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.8303 | 4.0 | 92 | 0.5952 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.6728 | 5.0 | 115 | 0.5078 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.649 | 6.0 | 138 | 0.4546 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.6008 | 7.0 | 161 | 0.4454 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.5439 | 8.0 | 184 | 0.4495 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.5557 | 9.0 | 207 | 0.4479 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.5637 | 10.0 | 230 | 0.4470 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.5709 | 11.0 | 253 | 0.4500 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.5496 | 12.0 | 276 | 0.4456 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5586 | 13.0 | 299 | 0.4484 | 0.8214 | 0.8095 | 0.8571 | 0.9444 | 0.6 | 0.8718 | 0.7059 | | 0.562 | 14.0 | 322 | 0.4435 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5555 | 15.0 | 345 | 0.4427 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5449 | 16.0 | 368 | 0.4404 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.555 | 17.0 | 391 | 0.4384 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5541 | 18.0 | 414 | 0.4383 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5463 | 19.0 | 437 | 0.4379 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5548 | 20.0 | 460 | 0.4357 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5365 | 21.0 | 483 | 0.4342 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5473 | 22.0 | 506 | 0.4308 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5467 | 23.0 | 529 | 0.4309 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.543 | 24.0 | 552 | 0.4312 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.543 | 25.0 | 575 | 0.4289 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5309 | 26.0 | 598 | 0.4290 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5406 | 27.0 | 621 | 0.4246 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5295 | 28.0 | 644 | 0.4248 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.535 | 29.0 | 667 | 0.4247 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5401 | 30.0 | 690 | 0.4265 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5285 | 31.0 | 713 | 0.4262 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5492 | 32.0 | 736 | 0.4247 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5473 | 33.0 | 759 | 0.4224 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.547 | 34.0 | 782 | 0.4250 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5394 | 35.0 | 805 | 0.4280 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5361 | 36.0 | 828 | 0.4247 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5294 | 37.0 | 851 | 0.4238 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5302 | 38.0 | 874 | 0.4236 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5384 | 39.0 | 897 | 0.4215 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5396 | 40.0 | 920 | 0.4209 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5305 | 41.0 | 943 | 0.4192 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5241 | 42.0 | 966 | 0.4204 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5433 | 43.0 | 989 | 0.4190 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5246 | 44.0 | 1012 | 0.4169 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.525 | 45.0 | 1035 | 0.4177 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5306 | 46.0 | 1058 | 0.4169 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5228 | 47.0 | 1081 | 0.4167 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5094 | 48.0 | 1104 | 0.4176 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5207 | 49.0 | 1127 | 0.4170 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5087 | 50.0 | 1150 | 0.4169 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5229 | 51.0 | 1173 | 0.4163 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5221 | 52.0 | 1196 | 0.4160 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5147 | 53.0 | 1219 | 0.4166 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.524 | 54.0 | 1242 | 0.4157 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5171 | 55.0 | 1265 | 0.4149 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5116 | 56.0 | 1288 | 0.4138 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5373 | 57.0 | 1311 | 0.4139 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5274 | 58.0 | 1334 | 0.4131 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5375 | 59.0 | 1357 | 0.4133 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.528 | 60.0 | 1380 | 0.4136 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5282 | 61.0 | 1403 | 0.4147 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.528 | 62.0 | 1426 | 0.4142 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5357 | 63.0 | 1449 | 0.4132 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5177 | 64.0 | 1472 | 0.4132 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5358 | 65.0 | 1495 | 0.4133 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5224 | 66.0 | 1518 | 0.4124 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5121 | 67.0 | 1541 | 0.4125 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5394 | 68.0 | 1564 | 0.4137 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.52 | 69.0 | 1587 | 0.4140 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5103 | 70.0 | 1610 | 0.4131 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5224 | 71.0 | 1633 | 0.4134 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5351 | 72.0 | 1656 | 0.4129 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5181 | 73.0 | 1679 | 0.4138 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.532 | 74.0 | 1702 | 0.4139 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5216 | 75.0 | 1725 | 0.4142 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5083 | 76.0 | 1748 | 0.4138 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.531 | 77.0 | 1771 | 0.4132 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5245 | 78.0 | 1794 | 0.4125 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5191 | 79.0 | 1817 | 0.4127 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.516 | 80.0 | 1840 | 0.4126 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5098 | 81.0 | 1863 | 0.4128 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5173 | 82.0 | 1886 | 0.4127 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5119 | 83.0 | 1909 | 0.4129 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5296 | 84.0 | 1932 | 0.4125 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5105 | 85.0 | 1955 | 0.4131 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5108 | 86.0 | 1978 | 0.4124 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5156 | 87.0 | 2001 | 0.4125 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5143 | 88.0 | 2024 | 0.4124 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5099 | 89.0 | 2047 | 0.4122 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5163 | 90.0 | 2070 | 0.4120 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5224 | 91.0 | 2093 | 0.4118 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.4936 | 92.0 | 2116 | 0.4120 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5236 | 93.0 | 2139 | 0.4118 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5261 | 94.0 | 2162 | 0.4118 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5134 | 95.0 | 2185 | 0.4119 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5064 | 96.0 | 2208 | 0.4118 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5072 | 97.0 | 2231 | 0.4118 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5264 | 98.0 | 2254 | 0.4118 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.5344 | 99.0 | 2277 | 0.4118 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | | 0.522 | 100.0 | 2300 | 0.4119 | 0.8571 | 0.85 | 0.875 | 0.9444 | 0.7 | 0.8947 | 0.7778 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2+cu113 - Datasets 1.18.3 - Tokenizers 0.11.0