--- base_model: vinai/phobert-base-v2 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: PhoBert_Lexical_lc results: [] --- # PhoBert_Lexical_lc 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: 0.6002 - Accuracy: 0.8324 - F1: 0.8697 ## 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 | 0.1927 | 200 | 0.7068 | 0.6753 | 0.7632 | | No log | 0.3854 | 400 | 0.7343 | 0.6772 | 0.7646 | | No log | 0.5780 | 600 | 0.5764 | 0.7641 | 0.8248 | | No log | 0.7707 | 800 | 0.7871 | 0.6153 | 0.7178 | | No log | 0.9634 | 1000 | 0.5685 | 0.7548 | 0.8186 | | 0.358 | 1.1561 | 1200 | 0.6231 | 0.7569 | 0.8203 | | 0.358 | 1.3487 | 1400 | 0.5796 | 0.7737 | 0.8314 | | 0.358 | 1.5414 | 1600 | 0.5651 | 0.7758 | 0.8327 | | 0.358 | 1.7341 | 1800 | 0.6171 | 0.7502 | 0.8157 | | 0.358 | 1.9268 | 2000 | 0.5711 | 0.7645 | 0.8254 | | 0.2472 | 2.1195 | 2200 | 0.6046 | 0.7615 | 0.8235 | | 0.2472 | 2.3121 | 2400 | 0.8503 | 0.6871 | 0.7718 | | 0.2472 | 2.5048 | 2600 | 0.7907 | 0.7136 | 0.7908 | | 0.2472 | 2.6975 | 2800 | 0.6425 | 0.7575 | 0.8209 | | 0.2472 | 2.8902 | 3000 | 0.5584 | 0.8067 | 0.8530 | | 0.2065 | 3.0829 | 3200 | 0.6602 | 0.7627 | 0.8244 | | 0.2065 | 3.2755 | 3400 | 0.7031 | 0.7570 | 0.8206 | | 0.2065 | 3.4682 | 3600 | 0.6166 | 0.7832 | 0.8382 | | 0.2065 | 3.6609 | 3800 | 0.7400 | 0.7279 | 0.8008 | | 0.2065 | 3.8536 | 4000 | 0.5337 | 0.8066 | 0.8531 | | 0.1757 | 4.0462 | 4200 | 0.7663 | 0.7600 | 0.8227 | | 0.1757 | 4.2389 | 4400 | 0.6286 | 0.7849 | 0.8392 | | 0.1757 | 4.4316 | 4600 | 0.6379 | 0.8031 | 0.8511 | | 0.1757 | 4.6243 | 4800 | 0.6865 | 0.7751 | 0.8328 | | 0.1757 | 4.8170 | 5000 | 0.5512 | 0.8216 | 0.8629 | | 0.1511 | 5.0096 | 5200 | 0.6118 | 0.8058 | 0.8529 | | 0.1511 | 5.2023 | 5400 | 0.8038 | 0.7545 | 0.8191 | | 0.1511 | 5.3950 | 5600 | 0.6799 | 0.8170 | 0.8600 | | 0.1511 | 5.5877 | 5800 | 0.8013 | 0.7679 | 0.8282 | | 0.1511 | 5.7803 | 6000 | 0.7806 | 0.7809 | 0.8365 | | 0.1511 | 5.9730 | 6200 | 0.7302 | 0.7738 | 0.8320 | | 0.129 | 6.1657 | 6400 | 0.6002 | 0.8324 | 0.8697 | | 0.129 | 6.3584 | 6600 | 0.7237 | 0.8069 | 0.8534 | | 0.129 | 6.5511 | 6800 | 0.7118 | 0.8072 | 0.8536 | | 0.129 | 6.7437 | 7000 | 0.7674 | 0.7933 | 0.8447 | | 0.129 | 6.9364 | 7200 | 0.7735 | 0.7737 | 0.8319 | | 0.1133 | 7.1291 | 7400 | 0.6940 | 0.8152 | 0.8588 | | 0.1133 | 7.3218 | 7600 | 0.8333 | 0.7880 | 0.8413 | | 0.1133 | 7.5145 | 7800 | 0.7050 | 0.8016 | 0.8502 | | 0.1133 | 7.7071 | 8000 | 0.8503 | 0.7763 | 0.8336 | | 0.1133 | 7.8998 | 8200 | 0.8677 | 0.7734 | 0.8318 | | 0.0964 | 8.0925 | 8400 | 0.7368 | 0.7994 | 0.8488 | | 0.0964 | 8.2852 | 8600 | 0.7291 | 0.8161 | 0.8594 | | 0.0964 | 8.4778 | 8800 | 0.8928 | 0.7948 | 0.8457 | | 0.0964 | 8.6705 | 9000 | 0.9070 | 0.7799 | 0.8360 | | 0.0964 | 8.8632 | 9200 | 0.8584 | 0.7961 | 0.8465 | | 0.085 | 9.0559 | 9400 | 0.8249 | 0.8081 | 0.8543 | | 0.085 | 9.2486 | 9600 | 0.8202 | 0.7929 | 0.8446 | | 0.085 | 9.4412 | 9800 | 0.9296 | 0.7757 | 0.8332 | | 0.085 | 9.6339 | 10000 | 0.9153 | 0.7931 | 0.8447 | | 0.085 | 9.8266 | 10200 | 0.9087 | 0.7868 | 0.8405 | | 0.0749 | 10.0193 | 10400 | 0.8043 | 0.8054 | 0.8526 | | 0.0749 | 10.2119 | 10600 | 0.9692 | 0.7916 | 0.8436 | | 0.0749 | 10.4046 | 10800 | 0.8181 | 0.8190 | 0.8614 | | 0.0749 | 10.5973 | 11000 | 0.8767 | 0.8010 | 0.8498 | | 0.0749 | 10.7900 | 11200 | 0.9470 | 0.7944 | 0.8455 | | 0.0749 | 10.9827 | 11400 | 0.9699 | 0.7796 | 0.8358 | | 0.0668 | 11.1753 | 11600 | 0.9448 | 0.7862 | 0.8402 | | 0.0668 | 11.3680 | 11800 | 0.9925 | 0.7982 | 0.8480 | | 0.0668 | 11.5607 | 12000 | 1.0677 | 0.7826 | 0.8378 | | 0.0668 | 11.7534 | 12200 | 0.8985 | 0.7994 | 0.8487 | | 0.0668 | 11.9461 | 12400 | 0.9710 | 0.7969 | 0.8471 | | 0.0601 | 12.1387 | 12600 | 1.0032 | 0.7924 | 0.8442 | | 0.0601 | 12.3314 | 12800 | 1.0084 | 0.7911 | 0.8432 | | 0.0601 | 12.5241 | 13000 | 1.1361 | 0.7666 | 0.8272 | | 0.0601 | 12.7168 | 13200 | 0.9933 | 0.7935 | 0.8449 | | 0.0601 | 12.9094 | 13400 | 1.0405 | 0.7888 | 0.8419 | | 0.0528 | 13.1021 | 13600 | 1.0769 | 0.7822 | 0.8375 | | 0.0528 | 13.2948 | 13800 | 1.0596 | 0.7906 | 0.8431 | | 0.0528 | 13.4875 | 14000 | 1.0612 | 0.7848 | 0.8393 | | 0.0528 | 13.6802 | 14200 | 1.0330 | 0.7909 | 0.8434 | | 0.0528 | 13.8728 | 14400 | 1.0386 | 0.7967 | 0.8471 | | 0.0477 | 14.0655 | 14600 | 0.9948 | 0.7956 | 0.8464 | | 0.0477 | 14.2582 | 14800 | 1.0767 | 0.7897 | 0.8425 | | 0.0477 | 14.4509 | 15000 | 1.0176 | 0.7938 | 0.8451 | | 0.0477 | 14.6435 | 15200 | 1.0246 | 0.7945 | 0.8456 | | 0.0477 | 14.8362 | 15400 | 1.0230 | 0.7969 | 0.8472 | ### Framework versions - Transformers 4.44.0 - Pytorch 2.1.2 - Datasets 2.20.0 - Tokenizers 0.19.1