--- library_name: transformers license: agpl-3.0 base_model: vinai/phobert-base-v2 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: PhoBert_Lexical_15-10 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # PhoBert_Lexical_15-10 This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0211 - Accuracy: 0.9945 - F1: 0.9945 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:| | No log | 0.1996 | 200 | 0.3681 | 0.8393 | 0.8397 | | No log | 0.3992 | 400 | 0.3333 | 0.8558 | 0.8563 | | No log | 0.5988 | 600 | 0.3128 | 0.8681 | 0.8684 | | No log | 0.7984 | 800 | 0.3062 | 0.8698 | 0.8703 | | No log | 0.9980 | 1000 | 0.2766 | 0.8822 | 0.8827 | | 0.3857 | 1.1976 | 1200 | 0.2619 | 0.8896 | 0.8900 | | 0.3857 | 1.3972 | 1400 | 0.2524 | 0.8939 | 0.8943 | | 0.3857 | 1.5968 | 1600 | 0.2386 | 0.8997 | 0.9000 | | 0.3857 | 1.7964 | 1800 | 0.2422 | 0.9013 | 0.9013 | | 0.3857 | 1.9960 | 2000 | 0.2229 | 0.9073 | 0.9077 | | 0.2899 | 2.1956 | 2200 | 0.2121 | 0.9135 | 0.9137 | | 0.2899 | 2.3952 | 2400 | 0.2311 | 0.9028 | 0.9032 | | 0.2899 | 2.5948 | 2600 | 0.2070 | 0.9163 | 0.9166 | | 0.2899 | 2.7944 | 2800 | 0.1931 | 0.9224 | 0.9226 | | 0.2899 | 2.9940 | 3000 | 0.1894 | 0.9236 | 0.9238 | | 0.248 | 3.1936 | 3200 | 0.1797 | 0.9285 | 0.9287 | | 0.248 | 3.3932 | 3400 | 0.1714 | 0.9330 | 0.9331 | | 0.248 | 3.5928 | 3600 | 0.1628 | 0.9369 | 0.9371 | | 0.248 | 3.7924 | 3800 | 0.1642 | 0.9353 | 0.9355 | | 0.248 | 3.9920 | 4000 | 0.1541 | 0.9403 | 0.9405 | | 0.2167 | 4.1916 | 4200 | 0.1430 | 0.9467 | 0.9468 | | 0.2167 | 4.3912 | 4400 | 0.1510 | 0.9425 | 0.9427 | | 0.2167 | 4.5908 | 4600 | 0.1509 | 0.9433 | 0.9435 | | 0.2167 | 4.7904 | 4800 | 0.1323 | 0.9514 | 0.9515 | | 0.2167 | 4.9900 | 5000 | 0.1248 | 0.9559 | 0.9559 | | 0.1901 | 5.1896 | 5200 | 0.1159 | 0.9577 | 0.9577 | | 0.1901 | 5.3892 | 5400 | 0.1123 | 0.9595 | 0.9595 | | 0.1901 | 5.5888 | 5600 | 0.1128 | 0.9590 | 0.9591 | | 0.1901 | 5.7884 | 5800 | 0.1016 | 0.9636 | 0.9636 | | 0.1901 | 5.9880 | 6000 | 0.1080 | 0.9628 | 0.9629 | | 0.1659 | 6.1876 | 6200 | 0.0910 | 0.9679 | 0.9679 | | 0.1659 | 6.3872 | 6400 | 0.0939 | 0.9674 | 0.9675 | | 0.1659 | 6.5868 | 6600 | 0.0948 | 0.9665 | 0.9665 | | 0.1659 | 6.7864 | 6800 | 0.0846 | 0.9711 | 0.9711 | | 0.1659 | 6.9860 | 7000 | 0.0866 | 0.9691 | 0.9691 | | 0.1451 | 7.1856 | 7200 | 0.0778 | 0.9733 | 0.9734 | | 0.1451 | 7.3852 | 7400 | 0.0823 | 0.9707 | 0.9708 | | 0.1451 | 7.5848 | 7600 | 0.0702 | 0.9771 | 0.9772 | | 0.1451 | 7.7844 | 7800 | 0.0684 | 0.9776 | 0.9776 | | 0.1451 | 7.9840 | 8000 | 0.0695 | 0.9765 | 0.9766 | | 0.128 | 8.1836 | 8200 | 0.0635 | 0.9793 | 0.9793 | | 0.128 | 8.3832 | 8400 | 0.0586 | 0.9805 | 0.9805 | | 0.128 | 8.5828 | 8600 | 0.0698 | 0.9747 | 0.9748 | | 0.128 | 8.7824 | 8800 | 0.0570 | 0.9815 | 0.9815 | | 0.128 | 8.9820 | 9000 | 0.0514 | 0.9849 | 0.9849 | | 0.1113 | 9.1816 | 9200 | 0.0514 | 0.9836 | 0.9836 | | 0.1113 | 9.3812 | 9400 | 0.0470 | 0.9862 | 0.9862 | | 0.1113 | 9.5808 | 9600 | 0.0480 | 0.9850 | 0.9850 | | 0.1113 | 9.7804 | 9800 | 0.0482 | 0.9849 | 0.9849 | | 0.1113 | 9.9800 | 10000 | 0.0423 | 0.9872 | 0.9872 | | 0.0969 | 10.1796 | 10200 | 0.0410 | 0.9871 | 0.9871 | | 0.0969 | 10.3792 | 10400 | 0.0374 | 0.9887 | 0.9887 | | 0.0969 | 10.5788 | 10600 | 0.0372 | 0.9884 | 0.9884 | | 0.0969 | 10.7784 | 10800 | 0.0350 | 0.9903 | 0.9903 | | 0.0969 | 10.9780 | 11000 | 0.0386 | 0.9878 | 0.9878 | | 0.0864 | 11.1776 | 11200 | 0.0327 | 0.9908 | 0.9908 | | 0.0864 | 11.3772 | 11400 | 0.0305 | 0.9915 | 0.9916 | | 0.0864 | 11.5768 | 11600 | 0.0304 | 0.9916 | 0.9916 | | 0.0864 | 11.7764 | 11800 | 0.0336 | 0.9895 | 0.9895 | | 0.0864 | 11.9760 | 12000 | 0.0276 | 0.9925 | 0.9925 | | 0.0734 | 12.1756 | 12200 | 0.0265 | 0.9929 | 0.9929 | | 0.0734 | 12.3752 | 12400 | 0.0275 | 0.9924 | 0.9924 | | 0.0734 | 12.5749 | 12600 | 0.0265 | 0.9928 | 0.9928 | | 0.0734 | 12.7745 | 12800 | 0.0256 | 0.9927 | 0.9928 | | 0.0734 | 12.9741 | 13000 | 0.0238 | 0.9940 | 0.9940 | | 0.0665 | 13.1737 | 13200 | 0.0251 | 0.9929 | 0.9929 | | 0.0665 | 13.3733 | 13400 | 0.0242 | 0.9934 | 0.9934 | | 0.0665 | 13.5729 | 13600 | 0.0228 | 0.9942 | 0.9942 | | 0.0665 | 13.7725 | 13800 | 0.0232 | 0.9938 | 0.9938 | | 0.0665 | 13.9721 | 14000 | 0.0228 | 0.9940 | 0.9940 | | 0.0623 | 14.1717 | 14200 | 0.0215 | 0.9944 | 0.9944 | | 0.0623 | 14.3713 | 14400 | 0.0217 | 0.9943 | 0.9943 | | 0.0623 | 14.5709 | 14600 | 0.0213 | 0.9944 | 0.9944 | | 0.0623 | 14.7705 | 14800 | 0.0213 | 0.9944 | 0.9944 | | 0.0623 | 14.9701 | 15000 | 0.0211 | 0.9945 | 0.9945 | ### Framework versions - Transformers 4.45.2 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.20.1