--- license: apache-2.0 base_model: google-bert/bert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: 20240327180321_happy_vaswani results: [] --- # 20240327180321_happy_vaswani This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0288 - Precision: 0.9791 - Recall: 0.9836 - F1: 0.9813 - Accuracy: 0.9908 ## 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: 0.0005 - train_batch_size: 32 - eval_batch_size: 32 - seed: 69 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 350 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0627 | 0.09 | 300 | 0.0535 | 0.9603 | 0.9629 | 0.9616 | 0.9809 | | 0.0571 | 0.17 | 600 | 0.0485 | 0.9625 | 0.9685 | 0.9655 | 0.9827 | | 0.0523 | 0.26 | 900 | 0.0451 | 0.9639 | 0.9721 | 0.9680 | 0.9840 | | 0.0498 | 0.35 | 1200 | 0.0452 | 0.9659 | 0.9700 | 0.9680 | 0.9841 | | 0.0498 | 0.44 | 1500 | 0.0440 | 0.9675 | 0.9717 | 0.9696 | 0.9849 | | 0.0487 | 0.52 | 1800 | 0.0429 | 0.9674 | 0.9714 | 0.9694 | 0.9848 | | 0.0485 | 0.61 | 2100 | 0.0431 | 0.9668 | 0.9733 | 0.9700 | 0.9850 | | 0.0468 | 0.7 | 2400 | 0.0410 | 0.9672 | 0.9745 | 0.9709 | 0.9855 | | 0.0469 | 0.78 | 2700 | 0.0412 | 0.9671 | 0.9754 | 0.9713 | 0.9857 | | 0.0473 | 0.87 | 3000 | 0.0419 | 0.9678 | 0.9731 | 0.9704 | 0.9853 | | 0.0455 | 0.96 | 3300 | 0.0415 | 0.9674 | 0.9756 | 0.9715 | 0.9857 | | 0.0417 | 1.04 | 3600 | 0.0404 | 0.9674 | 0.9763 | 0.9718 | 0.9859 | | 0.0428 | 1.13 | 3900 | 0.0410 | 0.9683 | 0.9755 | 0.9719 | 0.9860 | | 0.0421 | 1.22 | 4200 | 0.0400 | 0.9691 | 0.9750 | 0.9721 | 0.9861 | | 0.0412 | 1.31 | 4500 | 0.0403 | 0.9681 | 0.9763 | 0.9722 | 0.9861 | | 0.0411 | 1.39 | 4800 | 0.0384 | 0.9706 | 0.9764 | 0.9735 | 0.9869 | | 0.0401 | 1.48 | 5100 | 0.0381 | 0.9697 | 0.9772 | 0.9734 | 0.9867 | | 0.0399 | 1.57 | 5400 | 0.0373 | 0.9711 | 0.9759 | 0.9735 | 0.9869 | | 0.0398 | 1.65 | 5700 | 0.0367 | 0.9703 | 0.9780 | 0.9742 | 0.9871 | | 0.0393 | 1.74 | 6000 | 0.0374 | 0.9687 | 0.9783 | 0.9735 | 0.9869 | | 0.039 | 1.83 | 6300 | 0.0359 | 0.9709 | 0.9781 | 0.9745 | 0.9873 | | 0.0386 | 1.92 | 6600 | 0.0361 | 0.9711 | 0.9780 | 0.9746 | 0.9873 | | 0.0376 | 2.0 | 6900 | 0.0362 | 0.9717 | 0.9784 | 0.9750 | 0.9876 | | 0.0346 | 2.09 | 7200 | 0.0359 | 0.9712 | 0.9790 | 0.9751 | 0.9876 | | 0.0344 | 2.18 | 7500 | 0.0345 | 0.9730 | 0.9785 | 0.9757 | 0.9880 | | 0.0335 | 2.26 | 7800 | 0.0340 | 0.9725 | 0.9789 | 0.9757 | 0.9880 | | 0.0337 | 2.35 | 8100 | 0.0344 | 0.9722 | 0.9795 | 0.9758 | 0.9880 | | 0.0336 | 2.44 | 8400 | 0.0344 | 0.9721 | 0.9806 | 0.9763 | 0.9883 | | 0.033 | 2.53 | 8700 | 0.0342 | 0.9734 | 0.9792 | 0.9763 | 0.9883 | | 0.0331 | 2.61 | 9000 | 0.0345 | 0.9736 | 0.9792 | 0.9764 | 0.9883 | | 0.0329 | 2.7 | 9300 | 0.0331 | 0.9727 | 0.9808 | 0.9767 | 0.9884 | | 0.032 | 2.79 | 9600 | 0.0332 | 0.9731 | 0.9808 | 0.9769 | 0.9886 | | 0.0323 | 2.87 | 9900 | 0.0321 | 0.9740 | 0.9808 | 0.9774 | 0.9888 | | 0.0314 | 2.96 | 10200 | 0.0322 | 0.9748 | 0.9805 | 0.9776 | 0.9889 | | 0.0275 | 3.05 | 10500 | 0.0327 | 0.9750 | 0.9800 | 0.9775 | 0.9888 | | 0.0275 | 3.13 | 10800 | 0.0330 | 0.9736 | 0.9810 | 0.9773 | 0.9888 | | 0.0272 | 3.22 | 11100 | 0.0321 | 0.9753 | 0.9816 | 0.9784 | 0.9893 | | 0.0272 | 3.31 | 11400 | 0.0319 | 0.9749 | 0.9810 | 0.9779 | 0.9891 | | 0.0269 | 3.4 | 11700 | 0.0305 | 0.9758 | 0.9810 | 0.9784 | 0.9893 | | 0.027 | 3.48 | 12000 | 0.0303 | 0.9762 | 0.9814 | 0.9788 | 0.9895 | | 0.0267 | 3.57 | 12300 | 0.0300 | 0.9764 | 0.9819 | 0.9792 | 0.9897 | | 0.0263 | 3.66 | 12600 | 0.0297 | 0.9766 | 0.9818 | 0.9792 | 0.9898 | | 0.0261 | 3.74 | 12900 | 0.0296 | 0.9766 | 0.9824 | 0.9795 | 0.9899 | | 0.0255 | 3.83 | 13200 | 0.0294 | 0.9775 | 0.9827 | 0.9801 | 0.9902 | | 0.0254 | 3.92 | 13500 | 0.0289 | 0.9774 | 0.9828 | 0.9801 | 0.9902 | | 0.0234 | 4.01 | 13800 | 0.0302 | 0.9775 | 0.9826 | 0.9801 | 0.9901 | | 0.0207 | 4.09 | 14100 | 0.0303 | 0.9773 | 0.9823 | 0.9798 | 0.9900 | | 0.0205 | 4.18 | 14400 | 0.0299 | 0.9779 | 0.9825 | 0.9802 | 0.9903 | | 0.0205 | 4.27 | 14700 | 0.0296 | 0.9781 | 0.9828 | 0.9804 | 0.9903 | | 0.0205 | 4.35 | 15000 | 0.0291 | 0.9785 | 0.9831 | 0.9808 | 0.9906 | | 0.0201 | 4.44 | 15300 | 0.0294 | 0.9781 | 0.9830 | 0.9805 | 0.9904 | | 0.0198 | 4.53 | 15600 | 0.0290 | 0.9784 | 0.9831 | 0.9807 | 0.9905 | | 0.0199 | 4.62 | 15900 | 0.0293 | 0.9781 | 0.9835 | 0.9808 | 0.9905 | | 0.0199 | 4.7 | 16200 | 0.0291 | 0.9789 | 0.9835 | 0.9812 | 0.9907 | | 0.0195 | 4.79 | 16500 | 0.0293 | 0.9788 | 0.9835 | 0.9811 | 0.9907 | | 0.0196 | 4.88 | 16800 | 0.0290 | 0.9787 | 0.9835 | 0.9811 | 0.9907 | | 0.0196 | 4.96 | 17100 | 0.0288 | 0.9791 | 0.9836 | 0.9813 | 0.9908 | ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.0a0+6a974be - Datasets 2.18.0 - Tokenizers 0.15.2