--- license: mit base_model: FacebookAI/xlm-roberta-base tags: - generated_from_trainer datasets: - massive metrics: - accuracy - f1 model-index: - name: scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_alpha-jason results: [] --- # scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_alpha-jason This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 1.8097 - Accuracy: 0.8259 - F1: 0.8016 ## 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: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 1123 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 5.8591 | 0.27 | 5000 | 5.6952 | 0.3264 | 0.1501 | | 4.1699 | 0.53 | 10000 | 4.1191 | 0.5584 | 0.4058 | | 3.363 | 0.8 | 15000 | 3.3295 | 0.6517 | 0.5362 | | 2.5996 | 1.07 | 20000 | 2.8831 | 0.7073 | 0.6101 | | 2.4092 | 1.34 | 25000 | 2.6156 | 0.7336 | 0.6540 | | 2.2034 | 1.6 | 30000 | 2.4512 | 0.7535 | 0.6792 | | 2.0892 | 1.87 | 35000 | 2.3290 | 0.7658 | 0.6941 | | 1.6869 | 2.14 | 40000 | 2.2489 | 0.7767 | 0.7205 | | 1.6442 | 2.41 | 45000 | 2.2293 | 0.7780 | 0.7261 | | 1.633 | 2.67 | 50000 | 2.1483 | 0.7854 | 0.7392 | | 1.6348 | 2.94 | 55000 | 2.0885 | 0.7906 | 0.7468 | | 1.2977 | 3.21 | 60000 | 2.1314 | 0.7903 | 0.7490 | | 1.3298 | 3.47 | 65000 | 2.0696 | 0.7975 | 0.7590 | | 1.3 | 3.74 | 70000 | 2.0638 | 0.7961 | 0.7611 | | 1.2101 | 4.01 | 75000 | 2.0296 | 0.8022 | 0.7627 | | 1.0841 | 4.28 | 80000 | 2.0720 | 0.8008 | 0.7656 | | 1.0928 | 4.54 | 85000 | 2.0490 | 0.8031 | 0.7684 | | 1.0846 | 4.81 | 90000 | 1.9852 | 0.8068 | 0.7751 | | 0.9008 | 5.08 | 95000 | 2.0298 | 0.8076 | 0.7749 | | 0.9178 | 5.34 | 100000 | 2.0931 | 0.8025 | 0.7735 | | 0.9507 | 5.61 | 105000 | 2.0079 | 0.8066 | 0.7790 | | 0.9577 | 5.88 | 110000 | 1.9660 | 0.8103 | 0.7780 | | 0.7877 | 6.15 | 115000 | 2.0676 | 0.8072 | 0.7772 | | 0.7916 | 6.41 | 120000 | 2.0080 | 0.8089 | 0.7832 | | 0.8493 | 6.68 | 125000 | 2.0347 | 0.8078 | 0.7780 | | 0.8544 | 6.95 | 130000 | 2.0131 | 0.8093 | 0.7806 | | 0.7207 | 7.22 | 135000 | 2.0612 | 0.8089 | 0.7827 | | 0.7387 | 7.48 | 140000 | 2.0334 | 0.8100 | 0.7829 | | 0.7341 | 7.75 | 145000 | 2.0446 | 0.8096 | 0.7826 | | 0.6886 | 8.02 | 150000 | 2.0384 | 0.8114 | 0.7853 | | 0.6826 | 8.28 | 155000 | 2.0159 | 0.8103 | 0.7850 | | 0.6944 | 8.55 | 160000 | 1.9987 | 0.8136 | 0.7879 | | 0.6858 | 8.82 | 165000 | 2.0162 | 0.8124 | 0.7905 | | 0.6204 | 9.09 | 170000 | 2.0336 | 0.8128 | 0.7875 | | 0.6063 | 9.35 | 175000 | 2.0218 | 0.8125 | 0.7879 | | 0.6253 | 9.62 | 180000 | 2.0256 | 0.8130 | 0.7874 | | 0.6354 | 9.89 | 185000 | 1.9910 | 0.8149 | 0.7889 | | 0.5804 | 10.15 | 190000 | 2.0027 | 0.8139 | 0.7898 | | 0.5932 | 10.42 | 195000 | 1.9711 | 0.8157 | 0.7919 | | 0.5965 | 10.69 | 200000 | 1.9713 | 0.8158 | 0.7930 | | 0.6028 | 10.96 | 205000 | 2.0039 | 0.8135 | 0.7884 | | 0.5417 | 11.22 | 210000 | 1.9622 | 0.8164 | 0.7926 | | 0.5556 | 11.49 | 215000 | 1.9953 | 0.8157 | 0.7937 | | 0.5552 | 11.76 | 220000 | 1.9741 | 0.8166 | 0.7928 | | 0.5146 | 12.03 | 225000 | 1.9948 | 0.8146 | 0.7892 | | 0.5328 | 12.29 | 230000 | 1.9546 | 0.8175 | 0.7969 | | 0.5224 | 12.56 | 235000 | 1.9565 | 0.8171 | 0.7927 | | 0.5491 | 12.83 | 240000 | 1.9538 | 0.8178 | 0.7932 | | 0.5001 | 13.09 | 245000 | 1.9559 | 0.8184 | 0.7944 | | 0.4904 | 13.36 | 250000 | 1.9734 | 0.8165 | 0.7947 | | 0.5091 | 13.63 | 255000 | 1.9647 | 0.8177 | 0.7936 | | 0.5157 | 13.9 | 260000 | 1.9391 | 0.8194 | 0.7953 | | 0.4824 | 14.16 | 265000 | 1.9494 | 0.8189 | 0.7967 | | 0.4757 | 14.43 | 270000 | 1.9423 | 0.8174 | 0.7920 | | 0.4859 | 14.7 | 275000 | 1.9255 | 0.8193 | 0.7949 | | 0.4878 | 14.96 | 280000 | 1.9229 | 0.8197 | 0.7957 | | 0.4629 | 15.23 | 285000 | 1.9201 | 0.8191 | 0.7950 | | 0.4634 | 15.5 | 290000 | 1.9189 | 0.8209 | 0.7990 | | 0.4593 | 15.77 | 295000 | 1.9161 | 0.8200 | 0.7991 | | 0.4484 | 16.03 | 300000 | 1.8980 | 0.8210 | 0.7952 | | 0.4473 | 16.3 | 305000 | 1.9098 | 0.8204 | 0.7983 | | 0.4531 | 16.57 | 310000 | 1.8917 | 0.8210 | 0.7964 | | 0.4493 | 16.84 | 315000 | 1.8937 | 0.8205 | 0.7979 | | 0.4288 | 17.1 | 320000 | 1.8914 | 0.8200 | 0.7989 | | 0.4291 | 17.37 | 325000 | 1.8920 | 0.8216 | 0.7988 | | 0.4215 | 17.64 | 330000 | 1.8951 | 0.8224 | 0.7987 | | 0.4351 | 17.9 | 335000 | 1.8831 | 0.8220 | 0.7964 | | 0.4164 | 18.17 | 340000 | 1.8704 | 0.8223 | 0.7971 | | 0.4205 | 18.44 | 345000 | 1.8835 | 0.8227 | 0.7985 | | 0.4239 | 18.71 | 350000 | 1.8768 | 0.8227 | 0.7985 | | 0.4269 | 18.97 | 355000 | 1.8723 | 0.8226 | 0.7988 | | 0.4051 | 19.24 | 360000 | 1.8555 | 0.8235 | 0.8016 | | 0.4122 | 19.51 | 365000 | 1.8716 | 0.8234 | 0.7997 | | 0.3921 | 19.77 | 370000 | 1.8650 | 0.8231 | 0.7978 | | 0.3973 | 20.04 | 375000 | 1.8550 | 0.8236 | 0.7983 | | 0.4 | 20.31 | 380000 | 1.8512 | 0.8225 | 0.7966 | | 0.4027 | 20.58 | 385000 | 1.8653 | 0.8236 | 0.7982 | | 0.3932 | 20.84 | 390000 | 1.8594 | 0.8243 | 0.7974 | | 0.392 | 21.11 | 395000 | 1.8373 | 0.8247 | 0.8006 | | 0.3887 | 21.38 | 400000 | 1.8420 | 0.8252 | 0.8012 | | 0.3887 | 21.65 | 405000 | 1.8425 | 0.8241 | 0.7984 | | 0.38 | 21.91 | 410000 | 1.8413 | 0.8244 | 0.8017 | | 0.3793 | 22.18 | 415000 | 1.8325 | 0.8240 | 0.7978 | | 0.3806 | 22.45 | 420000 | 1.8338 | 0.8249 | 0.7990 | | 0.3726 | 22.71 | 425000 | 1.8488 | 0.8231 | 0.7990 | | 0.3771 | 22.98 | 430000 | 1.8441 | 0.8243 | 0.7998 | | 0.3728 | 23.25 | 435000 | 1.8380 | 0.8238 | 0.8005 | | 0.3677 | 23.52 | 440000 | 1.8289 | 0.8246 | 0.7999 | | 0.368 | 23.78 | 445000 | 1.8334 | 0.8256 | 0.8012 | | 0.3659 | 24.05 | 450000 | 1.8188 | 0.8261 | 0.8010 | | 0.3706 | 24.32 | 455000 | 1.8239 | 0.8250 | 0.7992 | | 0.3649 | 24.58 | 460000 | 1.8236 | 0.8258 | 0.8013 | | 0.3537 | 24.85 | 465000 | 1.8327 | 0.8254 | 0.7991 | | 0.3548 | 25.12 | 470000 | 1.8175 | 0.8258 | 0.8020 | | 0.3483 | 25.39 | 475000 | 1.8225 | 0.8255 | 0.8008 | | 0.3516 | 25.65 | 480000 | 1.8200 | 0.8254 | 0.8004 | | 0.3588 | 25.92 | 485000 | 1.8265 | 0.8256 | 0.8001 | | 0.3492 | 26.19 | 490000 | 1.8052 | 0.8270 | 0.8015 | | 0.3497 | 26.46 | 495000 | 1.8165 | 0.8268 | 0.8022 | | 0.3467 | 26.72 | 500000 | 1.8172 | 0.8265 | 0.8026 | | 0.3463 | 26.99 | 505000 | 1.8084 | 0.8266 | 0.8023 | | 0.3448 | 27.26 | 510000 | 1.8105 | 0.8267 | 0.8021 | | 0.3414 | 27.52 | 515000 | 1.8109 | 0.8267 | 0.8014 | | 0.3439 | 27.79 | 520000 | 1.8146 | 0.8254 | 0.8005 | | 0.3374 | 28.06 | 525000 | 1.8081 | 0.8264 | 0.8033 | | 0.3412 | 28.33 | 530000 | 1.8125 | 0.8264 | 0.8022 | | 0.3396 | 28.59 | 535000 | 1.8141 | 0.8264 | 0.8022 | | 0.3451 | 28.86 | 540000 | 1.8072 | 0.8258 | 0.8005 | | 0.337 | 29.13 | 545000 | 1.8056 | 0.8265 | 0.8028 | | 0.3335 | 29.39 | 550000 | 1.8083 | 0.8263 | 0.8010 | | 0.3402 | 29.66 | 555000 | 1.8107 | 0.8260 | 0.8013 | | 0.3409 | 29.93 | 560000 | 1.8097 | 0.8259 | 0.8016 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3