--- license: apache-2.0 base_model: albert/albert-base-v2 tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: classify-phishing_real_1 results: [] --- # classify-phishing_real_1 This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1185 - Accuracy: 0.9645 - F1: 0.9645 - Precision: 0.9645 - Recall: 0.9645 - Accuracy Label 0: 0.9708 - Accuracy Label 1: 0.9559 ## 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: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Accuracy Label 0 | Accuracy Label 1 | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----------------:|:----------------:| | 0.4991 | 0.1030 | 100 | 0.4748 | 0.7925 | 0.7819 | 0.8136 | 0.7925 | 0.9508 | 0.5747 | | 0.3087 | 0.2060 | 200 | 0.3052 | 0.8799 | 0.8793 | 0.8799 | 0.8799 | 0.9189 | 0.8262 | | 0.2974 | 0.3090 | 300 | 0.2390 | 0.9093 | 0.9094 | 0.9095 | 0.9093 | 0.9181 | 0.8972 | | 0.2644 | 0.4119 | 400 | 0.3068 | 0.8663 | 0.8670 | 0.8887 | 0.8663 | 0.7899 | 0.9715 | | 0.223 | 0.5149 | 500 | 0.2122 | 0.9154 | 0.9158 | 0.9195 | 0.9154 | 0.8905 | 0.9495 | | 0.215 | 0.6179 | 600 | 0.2011 | 0.9229 | 0.9222 | 0.9252 | 0.9229 | 0.9714 | 0.8561 | | 0.1419 | 0.7209 | 700 | 0.1836 | 0.9305 | 0.9300 | 0.9318 | 0.9305 | 0.9690 | 0.8775 | | 0.1511 | 0.8239 | 800 | 0.1828 | 0.9305 | 0.9308 | 0.9327 | 0.9305 | 0.9145 | 0.9526 | | 0.173 | 0.9269 | 900 | 0.1544 | 0.9430 | 0.9428 | 0.9433 | 0.9430 | 0.9666 | 0.9107 | | 0.0986 | 1.0299 | 1000 | 0.1513 | 0.9429 | 0.9430 | 0.9435 | 0.9429 | 0.9384 | 0.9491 | | 0.1403 | 1.1329 | 1100 | 0.1515 | 0.9426 | 0.9429 | 0.9444 | 0.9426 | 0.9278 | 0.9631 | | 0.1133 | 1.2358 | 1200 | 0.1394 | 0.9475 | 0.9475 | 0.9475 | 0.9475 | 0.9531 | 0.9397 | | 0.1117 | 1.3388 | 1300 | 0.1525 | 0.9457 | 0.9459 | 0.9467 | 0.9457 | 0.9371 | 0.9576 | | 0.1277 | 1.4418 | 1400 | 0.1311 | 0.9490 | 0.9491 | 0.9492 | 0.9490 | 0.9501 | 0.9475 | | 0.0886 | 1.5448 | 1500 | 0.1375 | 0.9503 | 0.9503 | 0.9503 | 0.9503 | 0.9628 | 0.9331 | | 0.1273 | 1.6478 | 1600 | 0.1297 | 0.9533 | 0.9533 | 0.9535 | 0.9533 | 0.9536 | 0.9529 | | 0.1102 | 1.7508 | 1700 | 0.1136 | 0.9578 | 0.9578 | 0.9578 | 0.9578 | 0.9637 | 0.9498 | | 0.0793 | 1.8538 | 1800 | 0.1269 | 0.9562 | 0.9561 | 0.9563 | 0.9562 | 0.9718 | 0.9348 | | 0.0995 | 1.9567 | 1900 | 0.1129 | 0.9591 | 0.9590 | 0.9591 | 0.9591 | 0.9702 | 0.9437 | | 0.0846 | 2.0597 | 2000 | 0.1362 | 0.9533 | 0.9534 | 0.9543 | 0.9533 | 0.9422 | 0.9685 | | 0.096 | 2.1627 | 2100 | 0.1383 | 0.9563 | 0.9564 | 0.9572 | 0.9563 | 0.9467 | 0.9696 | | 0.0797 | 2.2657 | 2200 | 0.1137 | 0.9620 | 0.9619 | 0.9619 | 0.9620 | 0.9711 | 0.9494 | | 0.0602 | 2.3687 | 2300 | 0.1211 | 0.9609 | 0.9609 | 0.9609 | 0.9609 | 0.9664 | 0.9532 | | 0.0951 | 2.4717 | 2400 | 0.1194 | 0.9614 | 0.9615 | 0.9615 | 0.9614 | 0.9628 | 0.9596 | | 0.0343 | 2.5747 | 2500 | 0.1237 | 0.9629 | 0.9629 | 0.9630 | 0.9629 | 0.9624 | 0.9634 | | 0.0512 | 2.6777 | 2600 | 0.1263 | 0.9625 | 0.9625 | 0.9625 | 0.9625 | 0.9738 | 0.9471 | | 0.0532 | 2.7806 | 2700 | 0.1229 | 0.9633 | 0.9633 | 0.9633 | 0.9633 | 0.9706 | 0.9533 | | 0.0673 | 2.8836 | 2800 | 0.1206 | 0.9644 | 0.9644 | 0.9644 | 0.9644 | 0.9679 | 0.9596 | | 0.0209 | 2.9866 | 2900 | 0.1185 | 0.9645 | 0.9645 | 0.9645 | 0.9645 | 0.9709 | 0.9556 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.2.1 - Datasets 2.20.0 - Tokenizers 0.19.1