--- license: mit base_model: xlm-roberta-base tags: - generated_from_trainer datasets: - massive metrics: - accuracy - f1 model-index: - name: scenario-NON-KD-SCR-D2_data-AmazonScience_massive_all_1_1_gamma-jason results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: all_1.1 split: validation args: all_1.1 metrics: - name: Accuracy type: accuracy value: 0.8066896212493851 - name: F1 type: f1 value: 0.7754136602803074 --- # scenario-NON-KD-SCR-D2_data-AmazonScience_massive_all_1_1_gamma-jason This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 1.9069 - Accuracy: 0.8067 - F1: 0.7754 ## 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: 333 - 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 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 1.4977 | 0.27 | 5000 | 1.4424 | 0.6121 | 0.4883 | | 1.0666 | 0.53 | 10000 | 1.0799 | 0.7100 | 0.6376 | | 0.8956 | 0.8 | 15000 | 0.9316 | 0.7553 | 0.6882 | | 0.6187 | 1.07 | 20000 | 0.9192 | 0.7669 | 0.7139 | | 0.6149 | 1.34 | 25000 | 0.8627 | 0.7810 | 0.7321 | | 0.5816 | 1.6 | 30000 | 0.8322 | 0.7860 | 0.7348 | | 0.5658 | 1.87 | 35000 | 0.7785 | 0.7965 | 0.7507 | | 0.3827 | 2.14 | 40000 | 0.8172 | 0.8005 | 0.7622 | | 0.3917 | 2.41 | 45000 | 0.8092 | 0.8041 | 0.7669 | | 0.3967 | 2.67 | 50000 | 0.7971 | 0.8059 | 0.7698 | | 0.412 | 2.94 | 55000 | 0.8060 | 0.8060 | 0.7676 | | 0.2572 | 3.21 | 60000 | 0.9054 | 0.8044 | 0.7731 | | 0.2717 | 3.47 | 65000 | 0.8967 | 0.8041 | 0.7704 | | 0.2788 | 3.74 | 70000 | 0.8620 | 0.8081 | 0.7698 | | 0.2396 | 4.01 | 75000 | 0.9314 | 0.8108 | 0.7740 | | 0.1779 | 4.28 | 80000 | 0.9958 | 0.8078 | 0.7708 | | 0.1993 | 4.54 | 85000 | 1.0189 | 0.8017 | 0.7677 | | 0.2035 | 4.81 | 90000 | 0.9530 | 0.8098 | 0.7813 | | 0.1212 | 5.08 | 95000 | 1.1017 | 0.8065 | 0.7820 | | 0.1408 | 5.34 | 100000 | 1.0842 | 0.8046 | 0.7709 | | 0.148 | 5.61 | 105000 | 1.0908 | 0.8044 | 0.7735 | | 0.1627 | 5.88 | 110000 | 1.0639 | 0.8059 | 0.7752 | | 0.1025 | 6.15 | 115000 | 1.2316 | 0.8069 | 0.7792 | | 0.1115 | 6.41 | 120000 | 1.2271 | 0.8058 | 0.7751 | | 0.1184 | 6.68 | 125000 | 1.1966 | 0.8072 | 0.7791 | | 0.1254 | 6.95 | 130000 | 1.2027 | 0.8040 | 0.7712 | | 0.0945 | 7.22 | 135000 | 1.3307 | 0.8014 | 0.7731 | | 0.1011 | 7.48 | 140000 | 1.2414 | 0.8099 | 0.7808 | | 0.0979 | 7.75 | 145000 | 1.2843 | 0.8047 | 0.7750 | | 0.0805 | 8.02 | 150000 | 1.3527 | 0.8065 | 0.7790 | | 0.0796 | 8.28 | 155000 | 1.3635 | 0.8072 | 0.7779 | | 0.0847 | 8.55 | 160000 | 1.3899 | 0.8046 | 0.7757 | | 0.0888 | 8.82 | 165000 | 1.3633 | 0.8050 | 0.7754 | | 0.0584 | 9.09 | 170000 | 1.4016 | 0.8053 | 0.7788 | | 0.0666 | 9.35 | 175000 | 1.4972 | 0.8008 | 0.7724 | | 0.0708 | 9.62 | 180000 | 1.4603 | 0.8055 | 0.7718 | | 0.0816 | 9.89 | 185000 | 1.4165 | 0.8063 | 0.7777 | | 0.0557 | 10.15 | 190000 | 1.4906 | 0.8019 | 0.7708 | | 0.0619 | 10.42 | 195000 | 1.5149 | 0.8021 | 0.7674 | | 0.0674 | 10.69 | 200000 | 1.5291 | 0.8076 | 0.7809 | | 0.0657 | 10.96 | 205000 | 1.4904 | 0.8079 | 0.7805 | | 0.0421 | 11.22 | 210000 | 1.5688 | 0.8052 | 0.7712 | | 0.0478 | 11.49 | 215000 | 1.6017 | 0.8025 | 0.7685 | | 0.0589 | 11.76 | 220000 | 1.5721 | 0.8029 | 0.7699 | | 0.0448 | 12.03 | 225000 | 1.6069 | 0.8046 | 0.7750 | | 0.0506 | 12.29 | 230000 | 1.5687 | 0.8068 | 0.7749 | | 0.0464 | 12.56 | 235000 | 1.6193 | 0.8027 | 0.7695 | | 0.0503 | 12.83 | 240000 | 1.6064 | 0.8033 | 0.7737 | | 0.0345 | 13.09 | 245000 | 1.6466 | 0.8045 | 0.7700 | | 0.0423 | 13.36 | 250000 | 1.6533 | 0.8004 | 0.7652 | | 0.0398 | 13.63 | 255000 | 1.7020 | 0.8022 | 0.7721 | | 0.0521 | 13.9 | 260000 | 1.6553 | 0.8029 | 0.7720 | | 0.0377 | 14.16 | 265000 | 1.7035 | 0.8000 | 0.7636 | | 0.0405 | 14.43 | 270000 | 1.7346 | 0.8005 | 0.7696 | | 0.0409 | 14.7 | 275000 | 1.6876 | 0.8032 | 0.7729 | | 0.0376 | 14.96 | 280000 | 1.6858 | 0.8042 | 0.7740 | | 0.029 | 15.23 | 285000 | 1.7151 | 0.8045 | 0.7756 | | 0.0355 | 15.5 | 290000 | 1.6751 | 0.8020 | 0.7747 | | 0.0378 | 15.77 | 295000 | 1.7474 | 0.8032 | 0.7700 | | 0.0255 | 16.03 | 300000 | 1.7624 | 0.8037 | 0.7725 | | 0.0269 | 16.3 | 305000 | 1.7465 | 0.8034 | 0.7742 | | 0.0289 | 16.57 | 310000 | 1.7289 | 0.8035 | 0.7772 | | 0.0296 | 16.84 | 315000 | 1.7882 | 0.8010 | 0.7695 | | 0.0265 | 17.1 | 320000 | 1.7604 | 0.8038 | 0.7733 | | 0.0246 | 17.37 | 325000 | 1.8082 | 0.8008 | 0.7682 | | 0.0254 | 17.64 | 330000 | 1.7370 | 0.8036 | 0.7718 | | 0.0279 | 17.9 | 335000 | 1.7997 | 0.8026 | 0.7734 | | 0.023 | 18.17 | 340000 | 1.7900 | 0.7988 | 0.7656 | | 0.0261 | 18.44 | 345000 | 1.7862 | 0.8032 | 0.7683 | | 0.0262 | 18.71 | 350000 | 1.8031 | 0.7999 | 0.7693 | | 0.0257 | 18.97 | 355000 | 1.8355 | 0.7991 | 0.7679 | | 0.0175 | 19.24 | 360000 | 1.8461 | 0.8015 | 0.7724 | | 0.0239 | 19.51 | 365000 | 1.8358 | 0.7995 | 0.7669 | | 0.0264 | 19.77 | 370000 | 1.8008 | 0.8021 | 0.7712 | | 0.0136 | 20.04 | 375000 | 1.8389 | 0.7994 | 0.7651 | | 0.0189 | 20.31 | 380000 | 1.8344 | 0.8006 | 0.7672 | | 0.019 | 20.58 | 385000 | 1.8654 | 0.8026 | 0.7714 | | 0.0228 | 20.84 | 390000 | 1.8569 | 0.8015 | 0.7701 | | 0.0134 | 21.11 | 395000 | 1.8587 | 0.8026 | 0.7708 | | 0.0166 | 21.38 | 400000 | 1.8480 | 0.8050 | 0.7763 | | 0.0191 | 21.65 | 405000 | 1.8835 | 0.8025 | 0.7675 | | 0.0188 | 21.91 | 410000 | 1.8918 | 0.8031 | 0.7736 | | 0.0162 | 22.18 | 415000 | 1.8598 | 0.8015 | 0.7725 | | 0.017 | 22.45 | 420000 | 1.8991 | 0.8001 | 0.7644 | | 0.0147 | 22.71 | 425000 | 1.8656 | 0.7992 | 0.7690 | | 0.0188 | 22.98 | 430000 | 1.8882 | 0.8018 | 0.7687 | | 0.0116 | 23.25 | 435000 | 1.8724 | 0.8017 | 0.7677 | | 0.0147 | 23.52 | 440000 | 1.8837 | 0.8032 | 0.7721 | | 0.0164 | 23.78 | 445000 | 1.8735 | 0.8054 | 0.7724 | | 0.0103 | 24.05 | 450000 | 1.8956 | 0.8047 | 0.7731 | | 0.0096 | 24.32 | 455000 | 1.9296 | 0.8029 | 0.7677 | | 0.0127 | 24.58 | 460000 | 1.8898 | 0.8019 | 0.7725 | | 0.0104 | 24.85 | 465000 | 1.9208 | 0.8017 | 0.7679 | | 0.0075 | 25.12 | 470000 | 1.9089 | 0.8034 | 0.7706 | | 0.0108 | 25.39 | 475000 | 1.9093 | 0.8023 | 0.7664 | | 0.0116 | 25.65 | 480000 | 1.9088 | 0.8044 | 0.7722 | | 0.0155 | 25.92 | 485000 | 1.8528 | 0.8063 | 0.7754 | | 0.0082 | 26.19 | 490000 | 1.9082 | 0.8072 | 0.7739 | | 0.0117 | 26.46 | 495000 | 1.9002 | 0.8047 | 0.7727 | | 0.0072 | 26.72 | 500000 | 1.8967 | 0.8058 | 0.7735 | | 0.0082 | 26.99 | 505000 | 1.9000 | 0.8046 | 0.7747 | | 0.0088 | 27.26 | 510000 | 1.8946 | 0.8053 | 0.7744 | | 0.0074 | 27.52 | 515000 | 1.8956 | 0.8057 | 0.7717 | | 0.0099 | 27.79 | 520000 | 1.8367 | 0.8057 | 0.7725 | | 0.0072 | 28.06 | 525000 | 1.8863 | 0.8083 | 0.7768 | | 0.0094 | 28.33 | 530000 | 1.8948 | 0.8070 | 0.7754 | | 0.0084 | 28.59 | 535000 | 1.8845 | 0.8072 | 0.7756 | | 0.0069 | 28.86 | 540000 | 1.8865 | 0.8075 | 0.7751 | | 0.0055 | 29.13 | 545000 | 1.8999 | 0.8062 | 0.7735 | | 0.0051 | 29.39 | 550000 | 1.9113 | 0.8066 | 0.7751 | | 0.006 | 29.66 | 555000 | 1.9058 | 0.8066 | 0.7751 | | 0.0085 | 29.93 | 560000 | 1.9069 | 0.8067 | 0.7754 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3