--- 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_delta-jason results: [] --- # scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_delta-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: 1.7914 - Accuracy: 0.8274 - F1: 0.8029 ## 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: 1254 - 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 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 5.7233 | 0.27 | 5000 | 5.5374 | 0.3569 | 0.1678 | | 3.9329 | 0.53 | 10000 | 3.9116 | 0.5835 | 0.4241 | | 3.1699 | 0.8 | 15000 | 3.1794 | 0.6720 | 0.5600 | | 2.5004 | 1.07 | 20000 | 2.7930 | 0.7147 | 0.6299 | | 2.289 | 1.34 | 25000 | 2.5799 | 0.7385 | 0.6658 | | 2.1906 | 1.6 | 30000 | 2.4334 | 0.7534 | 0.6852 | | 2.0248 | 1.87 | 35000 | 2.3295 | 0.7659 | 0.6957 | | 1.7159 | 2.14 | 40000 | 2.2350 | 0.7731 | 0.7166 | | 1.669 | 2.41 | 45000 | 2.2103 | 0.7811 | 0.7321 | | 1.6191 | 2.67 | 50000 | 2.1297 | 0.7874 | 0.7426 | | 1.587 | 2.94 | 55000 | 2.0855 | 0.7890 | 0.7448 | | 1.3031 | 3.21 | 60000 | 2.1223 | 0.7920 | 0.7492 | | 1.3 | 3.47 | 65000 | 2.0614 | 0.7952 | 0.7563 | | 1.337 | 3.74 | 70000 | 2.0343 | 0.8002 | 0.7618 | | 1.2406 | 4.01 | 75000 | 2.0565 | 0.7999 | 0.7616 | | 1.1146 | 4.28 | 80000 | 2.0439 | 0.8006 | 0.7686 | | 1.0767 | 4.54 | 85000 | 2.0461 | 0.8001 | 0.7663 | | 1.1244 | 4.81 | 90000 | 2.0096 | 0.8032 | 0.7699 | | 0.91 | 5.08 | 95000 | 2.0492 | 0.8064 | 0.7710 | | 0.9446 | 5.34 | 100000 | 2.0278 | 0.8059 | 0.7742 | | 0.9581 | 5.61 | 105000 | 2.0434 | 0.8034 | 0.7750 | | 0.9525 | 5.88 | 110000 | 2.0068 | 0.8073 | 0.7794 | | 0.809 | 6.15 | 115000 | 2.0286 | 0.8072 | 0.7773 | | 0.8597 | 6.41 | 120000 | 2.0080 | 0.8104 | 0.7819 | | 0.8219 | 6.68 | 125000 | 2.0049 | 0.8093 | 0.7816 | | 0.8507 | 6.95 | 130000 | 2.0095 | 0.8094 | 0.7866 | | 0.7371 | 7.22 | 135000 | 2.0533 | 0.8105 | 0.7863 | | 0.754 | 7.48 | 140000 | 1.9987 | 0.8110 | 0.7827 | | 0.7829 | 7.75 | 145000 | 2.0120 | 0.8079 | 0.7825 | | 0.6798 | 8.02 | 150000 | 1.9928 | 0.8144 | 0.7883 | | 0.6619 | 8.28 | 155000 | 1.9941 | 0.8127 | 0.7861 | | 0.6869 | 8.55 | 160000 | 2.0368 | 0.8119 | 0.7889 | | 0.695 | 8.82 | 165000 | 2.0073 | 0.8133 | 0.7869 | | 0.6395 | 9.09 | 170000 | 2.0095 | 0.8110 | 0.7879 | | 0.6274 | 9.35 | 175000 | 1.9915 | 0.8156 | 0.7924 | | 0.6186 | 9.62 | 180000 | 2.0114 | 0.8158 | 0.7912 | | 0.643 | 9.89 | 185000 | 1.9917 | 0.8143 | 0.7936 | | 0.5898 | 10.15 | 190000 | 2.0036 | 0.8156 | 0.7905 | | 0.5948 | 10.42 | 195000 | 1.9868 | 0.8168 | 0.7904 | | 0.6093 | 10.69 | 200000 | 1.9822 | 0.8153 | 0.7892 | | 0.5942 | 10.96 | 205000 | 1.9939 | 0.8144 | 0.7912 | | 0.5497 | 11.22 | 210000 | 1.9786 | 0.8169 | 0.7961 | | 0.5516 | 11.49 | 215000 | 1.9650 | 0.8168 | 0.7913 | | 0.5591 | 11.76 | 220000 | 1.9793 | 0.8175 | 0.7927 | | 0.5103 | 12.03 | 225000 | 1.9715 | 0.8183 | 0.7942 | | 0.5165 | 12.29 | 230000 | 1.9620 | 0.8172 | 0.7936 | | 0.5248 | 12.56 | 235000 | 1.9760 | 0.8179 | 0.7950 | | 0.5289 | 12.83 | 240000 | 1.9459 | 0.8190 | 0.7952 | | 0.4995 | 13.09 | 245000 | 1.9564 | 0.8185 | 0.7959 | | 0.4906 | 13.36 | 250000 | 1.9484 | 0.8186 | 0.7940 | | 0.5011 | 13.63 | 255000 | 1.9320 | 0.8188 | 0.7923 | | 0.4996 | 13.9 | 260000 | 1.9477 | 0.8164 | 0.7929 | | 0.4844 | 14.16 | 265000 | 1.9110 | 0.8207 | 0.7942 | | 0.4814 | 14.43 | 270000 | 1.9303 | 0.8190 | 0.7927 | | 0.4953 | 14.7 | 275000 | 1.9211 | 0.8208 | 0.7951 | | 0.4897 | 14.96 | 280000 | 1.9206 | 0.8209 | 0.7940 | | 0.4473 | 15.23 | 285000 | 1.9059 | 0.8214 | 0.7959 | | 0.4615 | 15.5 | 290000 | 1.9021 | 0.8229 | 0.7985 | | 0.4687 | 15.77 | 295000 | 1.9177 | 0.8204 | 0.7960 | | 0.4425 | 16.03 | 300000 | 1.9065 | 0.8225 | 0.7994 | | 0.451 | 16.3 | 305000 | 1.8924 | 0.8219 | 0.7972 | | 0.458 | 16.57 | 310000 | 1.9036 | 0.8210 | 0.7953 | | 0.4514 | 16.84 | 315000 | 1.8810 | 0.8224 | 0.7960 | | 0.4263 | 17.1 | 320000 | 1.8826 | 0.8241 | 0.8003 | | 0.4355 | 17.37 | 325000 | 1.8685 | 0.8236 | 0.7991 | | 0.4234 | 17.64 | 330000 | 1.8634 | 0.8249 | 0.7994 | | 0.4346 | 17.9 | 335000 | 1.8640 | 0.8239 | 0.8001 | | 0.4077 | 18.17 | 340000 | 1.8656 | 0.8245 | 0.8006 | | 0.4156 | 18.44 | 345000 | 1.8666 | 0.8229 | 0.7990 | | 0.4185 | 18.71 | 350000 | 1.8495 | 0.8235 | 0.8005 | | 0.4211 | 18.97 | 355000 | 1.8784 | 0.8233 | 0.7982 | | 0.3981 | 19.24 | 360000 | 1.8562 | 0.8235 | 0.7993 | | 0.4139 | 19.51 | 365000 | 1.8417 | 0.8243 | 0.7986 | | 0.4052 | 19.77 | 370000 | 1.8533 | 0.8249 | 0.7998 | | 0.3915 | 20.04 | 375000 | 1.8413 | 0.8255 | 0.8020 | | 0.4015 | 20.31 | 380000 | 1.8540 | 0.8232 | 0.7991 | | 0.3923 | 20.58 | 385000 | 1.8592 | 0.8245 | 0.7995 | | 0.3984 | 20.84 | 390000 | 1.8613 | 0.8257 | 0.8026 | | 0.3886 | 21.11 | 395000 | 1.8350 | 0.8248 | 0.7985 | | 0.3888 | 21.38 | 400000 | 1.8343 | 0.8238 | 0.7984 | | 0.3878 | 21.65 | 405000 | 1.8207 | 0.8263 | 0.8013 | | 0.3901 | 21.91 | 410000 | 1.8394 | 0.8266 | 0.8034 | | 0.3765 | 22.18 | 415000 | 1.8250 | 0.8257 | 0.8017 | | 0.3793 | 22.45 | 420000 | 1.8159 | 0.8262 | 0.7997 | | 0.3825 | 22.71 | 425000 | 1.8220 | 0.8244 | 0.8009 | | 0.383 | 22.98 | 430000 | 1.8325 | 0.8265 | 0.8013 | | 0.3737 | 23.25 | 435000 | 1.8248 | 0.8259 | 0.8024 | | 0.3741 | 23.52 | 440000 | 1.8139 | 0.8258 | 0.8014 | | 0.3676 | 23.78 | 445000 | 1.8299 | 0.8264 | 0.8007 | | 0.3611 | 24.05 | 450000 | 1.8136 | 0.8261 | 0.8018 | | 0.3642 | 24.32 | 455000 | 1.8196 | 0.8263 | 0.8017 | | 0.3654 | 24.58 | 460000 | 1.8241 | 0.8249 | 0.8012 | | 0.3706 | 24.85 | 465000 | 1.8103 | 0.8255 | 0.8012 | | 0.3585 | 25.12 | 470000 | 1.8137 | 0.8263 | 0.8029 | | 0.3664 | 25.39 | 475000 | 1.8094 | 0.8260 | 0.8024 | | 0.3544 | 25.65 | 480000 | 1.8000 | 0.8279 | 0.8041 | | 0.3491 | 25.92 | 485000 | 1.8039 | 0.8264 | 0.8028 | | 0.3523 | 26.19 | 490000 | 1.7989 | 0.8279 | 0.8037 | | 0.3483 | 26.46 | 495000 | 1.8045 | 0.8276 | 0.8027 | | 0.3482 | 26.72 | 500000 | 1.8058 | 0.8264 | 0.8022 | | 0.3601 | 26.99 | 505000 | 1.8035 | 0.8269 | 0.8017 | | 0.3461 | 27.26 | 510000 | 1.7959 | 0.8273 | 0.8041 | | 0.3448 | 27.52 | 515000 | 1.8078 | 0.8271 | 0.8030 | | 0.3454 | 27.79 | 520000 | 1.7968 | 0.8273 | 0.8035 | | 0.3377 | 28.06 | 525000 | 1.7924 | 0.8270 | 0.8019 | | 0.3497 | 28.33 | 530000 | 1.7950 | 0.8277 | 0.8041 | | 0.3461 | 28.59 | 535000 | 1.7954 | 0.8282 | 0.8049 | | 0.3448 | 28.86 | 540000 | 1.7968 | 0.8270 | 0.8031 | | 0.3413 | 29.13 | 545000 | 1.7914 | 0.8279 | 0.8038 | | 0.3367 | 29.39 | 550000 | 1.7976 | 0.8274 | 0.8027 | | 0.3432 | 29.66 | 555000 | 1.7976 | 0.8271 | 0.8037 | | 0.3429 | 29.93 | 560000 | 1.7914 | 0.8274 | 0.8029 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3