--- 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_gamma-jason results: [] --- # scenario-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_gamma-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: 1.8080 - Accuracy: 0.8266 - F1: 0.8031 ## 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: 123621 - 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 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 6.283 | 0.27 | 5000 | 6.2020 | 0.2676 | 0.1018 | | 4.4008 | 0.53 | 10000 | 4.4005 | 0.5229 | 0.3509 | | 3.3814 | 0.8 | 15000 | 3.4464 | 0.6349 | 0.5009 | | 2.6796 | 1.07 | 20000 | 2.9367 | 0.6972 | 0.6000 | | 2.4097 | 1.34 | 25000 | 2.6464 | 0.7279 | 0.6536 | | 2.2303 | 1.6 | 30000 | 2.4685 | 0.7522 | 0.6898 | | 2.0826 | 1.87 | 35000 | 2.3359 | 0.7657 | 0.7120 | | 1.6889 | 2.14 | 40000 | 2.3083 | 0.7710 | 0.7094 | | 1.6854 | 2.41 | 45000 | 2.2360 | 0.7767 | 0.7203 | | 1.621 | 2.67 | 50000 | 2.1137 | 0.7867 | 0.7391 | | 1.6051 | 2.94 | 55000 | 2.0718 | 0.7929 | 0.7467 | | 1.3071 | 3.21 | 60000 | 2.1140 | 0.7929 | 0.7508 | | 1.3165 | 3.47 | 65000 | 2.0525 | 0.7962 | 0.7574 | | 1.3278 | 3.74 | 70000 | 2.0554 | 0.7993 | 0.7620 | | 1.203 | 4.01 | 75000 | 2.0619 | 0.7989 | 0.7606 | | 1.1003 | 4.28 | 80000 | 2.0386 | 0.8011 | 0.7667 | | 1.0856 | 4.54 | 85000 | 2.0191 | 0.8024 | 0.7723 | | 1.0941 | 4.81 | 90000 | 2.0063 | 0.8019 | 0.7722 | | 0.9191 | 5.08 | 95000 | 2.0552 | 0.8051 | 0.7680 | | 0.9443 | 5.34 | 100000 | 2.0511 | 0.8023 | 0.7745 | | 0.9377 | 5.61 | 105000 | 2.0379 | 0.8059 | 0.7772 | | 0.9659 | 5.88 | 110000 | 2.0115 | 0.8058 | 0.7760 | | 0.7971 | 6.15 | 115000 | 2.0532 | 0.8083 | 0.7798 | | 0.8237 | 6.41 | 120000 | 2.0635 | 0.8070 | 0.7798 | | 0.8419 | 6.68 | 125000 | 2.0257 | 0.8079 | 0.7756 | | 0.8498 | 6.95 | 130000 | 2.0144 | 0.8117 | 0.7858 | | 0.7382 | 7.22 | 135000 | 2.0307 | 0.8101 | 0.7828 | | 0.7385 | 7.48 | 140000 | 2.0336 | 0.8117 | 0.7879 | | 0.7622 | 7.75 | 145000 | 1.9982 | 0.8126 | 0.7849 | | 0.6757 | 8.02 | 150000 | 2.0168 | 0.8147 | 0.7929 | | 0.667 | 8.28 | 155000 | 2.0176 | 0.8130 | 0.7882 | | 0.6735 | 8.55 | 160000 | 2.0328 | 0.8121 | 0.7888 | | 0.6927 | 8.82 | 165000 | 1.9887 | 0.8127 | 0.7877 | | 0.6113 | 9.09 | 170000 | 2.0148 | 0.8145 | 0.7885 | | 0.6098 | 9.35 | 175000 | 2.0184 | 0.8139 | 0.7898 | | 0.6308 | 9.62 | 180000 | 1.9917 | 0.8120 | 0.7870 | | 0.6361 | 9.89 | 185000 | 1.9818 | 0.8134 | 0.7877 | | 0.5727 | 10.15 | 190000 | 2.0203 | 0.8126 | 0.7888 | | 0.59 | 10.42 | 195000 | 1.9819 | 0.8143 | 0.7930 | | 0.5947 | 10.69 | 200000 | 2.0151 | 0.8143 | 0.7906 | | 0.602 | 10.96 | 205000 | 1.9809 | 0.8165 | 0.7923 | | 0.5482 | 11.22 | 210000 | 1.9816 | 0.8160 | 0.7935 | | 0.5669 | 11.49 | 215000 | 1.9793 | 0.8160 | 0.7904 | | 0.5724 | 11.76 | 220000 | 1.9677 | 0.8153 | 0.7905 | | 0.5295 | 12.03 | 225000 | 1.9569 | 0.8171 | 0.7924 | | 0.5277 | 12.29 | 230000 | 1.9549 | 0.8178 | 0.7959 | | 0.5324 | 12.56 | 235000 | 1.9477 | 0.8175 | 0.7929 | | 0.5374 | 12.83 | 240000 | 1.9587 | 0.8176 | 0.7960 | | 0.4818 | 13.09 | 245000 | 1.9764 | 0.8168 | 0.7935 | | 0.5064 | 13.36 | 250000 | 1.9439 | 0.8180 | 0.7945 | | 0.507 | 13.63 | 255000 | 1.9332 | 0.8160 | 0.7941 | | 0.5081 | 13.9 | 260000 | 1.9293 | 0.8180 | 0.7990 | | 0.4791 | 14.16 | 265000 | 1.9500 | 0.8183 | 0.7953 | | 0.4949 | 14.43 | 270000 | 1.9520 | 0.8181 | 0.7952 | | 0.4746 | 14.7 | 275000 | 1.9375 | 0.8197 | 0.7966 | | 0.4918 | 14.96 | 280000 | 1.9161 | 0.8210 | 0.7949 | | 0.4758 | 15.23 | 285000 | 1.9281 | 0.8184 | 0.7939 | | 0.4605 | 15.5 | 290000 | 1.9164 | 0.8194 | 0.7934 | | 0.4637 | 15.77 | 295000 | 1.9372 | 0.8192 | 0.7986 | | 0.4387 | 16.03 | 300000 | 1.9123 | 0.8213 | 0.8005 | | 0.4405 | 16.3 | 305000 | 1.9115 | 0.8191 | 0.7966 | | 0.4455 | 16.57 | 310000 | 1.8867 | 0.8212 | 0.7981 | | 0.4562 | 16.84 | 315000 | 1.9136 | 0.8199 | 0.7967 | | 0.4316 | 17.1 | 320000 | 1.8907 | 0.8218 | 0.7986 | | 0.4281 | 17.37 | 325000 | 1.8942 | 0.8222 | 0.7990 | | 0.4296 | 17.64 | 330000 | 1.9041 | 0.8215 | 0.7998 | | 0.4327 | 17.9 | 335000 | 1.8844 | 0.8239 | 0.7999 | | 0.4157 | 18.17 | 340000 | 1.8902 | 0.8219 | 0.8001 | | 0.4184 | 18.44 | 345000 | 1.8874 | 0.8227 | 0.7991 | | 0.4224 | 18.71 | 350000 | 1.8701 | 0.8224 | 0.7991 | | 0.4264 | 18.97 | 355000 | 1.8816 | 0.8217 | 0.7974 | | 0.4044 | 19.24 | 360000 | 1.8879 | 0.8212 | 0.7974 | | 0.4119 | 19.51 | 365000 | 1.8577 | 0.8229 | 0.7991 | | 0.4046 | 19.77 | 370000 | 1.8675 | 0.8235 | 0.8003 | | 0.4011 | 20.04 | 375000 | 1.8604 | 0.8231 | 0.7997 | | 0.4036 | 20.31 | 380000 | 1.8500 | 0.8240 | 0.8000 | | 0.3887 | 20.58 | 385000 | 1.8624 | 0.8231 | 0.7999 | | 0.4057 | 20.84 | 390000 | 1.8588 | 0.8222 | 0.7972 | | 0.3883 | 21.11 | 395000 | 1.8524 | 0.8233 | 0.7990 | | 0.3881 | 21.38 | 400000 | 1.8481 | 0.8245 | 0.8024 | | 0.3956 | 21.65 | 405000 | 1.8503 | 0.8245 | 0.8005 | | 0.3828 | 21.91 | 410000 | 1.8538 | 0.8240 | 0.7999 | | 0.3776 | 22.18 | 415000 | 1.8495 | 0.8241 | 0.7999 | | 0.3896 | 22.45 | 420000 | 1.8513 | 0.8226 | 0.7991 | | 0.3759 | 22.71 | 425000 | 1.8518 | 0.8251 | 0.8007 | | 0.3769 | 22.98 | 430000 | 1.8388 | 0.8242 | 0.8019 | | 0.3675 | 23.25 | 435000 | 1.8307 | 0.8245 | 0.8002 | | 0.3704 | 23.52 | 440000 | 1.8402 | 0.8227 | 0.7992 | | 0.3698 | 23.78 | 445000 | 1.8409 | 0.8238 | 0.7991 | | 0.3672 | 24.05 | 450000 | 1.8180 | 0.8248 | 0.7979 | | 0.3709 | 24.32 | 455000 | 1.8300 | 0.8235 | 0.8003 | | 0.361 | 24.58 | 460000 | 1.8265 | 0.8252 | 0.8012 | | 0.3649 | 24.85 | 465000 | 1.8288 | 0.8250 | 0.8012 | | 0.3534 | 25.12 | 470000 | 1.8216 | 0.8253 | 0.8025 | | 0.3535 | 25.39 | 475000 | 1.8240 | 0.8261 | 0.8017 | | 0.3578 | 25.65 | 480000 | 1.8216 | 0.8259 | 0.8011 | | 0.3569 | 25.92 | 485000 | 1.8257 | 0.8253 | 0.8025 | | 0.3515 | 26.19 | 490000 | 1.8191 | 0.8263 | 0.8026 | | 0.3551 | 26.46 | 495000 | 1.8209 | 0.8264 | 0.8036 | | 0.3577 | 26.72 | 500000 | 1.8199 | 0.8254 | 0.8011 | | 0.3548 | 26.99 | 505000 | 1.8190 | 0.8252 | 0.8006 | | 0.3498 | 27.26 | 510000 | 1.8072 | 0.8257 | 0.8023 | | 0.3419 | 27.52 | 515000 | 1.8131 | 0.8259 | 0.8019 | | 0.3452 | 27.79 | 520000 | 1.8140 | 0.8253 | 0.8023 | | 0.3364 | 28.06 | 525000 | 1.8145 | 0.8254 | 0.8017 | | 0.346 | 28.33 | 530000 | 1.8087 | 0.8256 | 0.8019 | | 0.3391 | 28.59 | 535000 | 1.8142 | 0.8259 | 0.8025 | | 0.3535 | 28.86 | 540000 | 1.8044 | 0.8270 | 0.8029 | | 0.333 | 29.13 | 545000 | 1.8150 | 0.8264 | 0.8026 | | 0.3397 | 29.39 | 550000 | 1.8099 | 0.8266 | 0.8032 | | 0.3429 | 29.66 | 555000 | 1.8090 | 0.8259 | 0.8017 | | 0.3422 | 29.93 | 560000 | 1.8080 | 0.8266 | 0.8031 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3