--- 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_betta-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: 0.8045612773846911 - name: F1 type: f1 value: 0.7727857971515983 --- # scenario-NON-KD-SCR-D2_data-AmazonScience_massive_all_1_1_betta-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: 1.9142 - Accuracy: 0.8046 - F1: 0.7728 ## 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: 222 - 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 | |:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| | 1.5859 | 0.27 | 5000 | 1.5238 | 0.5916 | 0.4568 | | 1.0824 | 0.53 | 10000 | 1.0656 | 0.7161 | 0.6369 | | 0.9043 | 0.8 | 15000 | 0.9531 | 0.7483 | 0.6851 | | 0.6415 | 1.07 | 20000 | 0.8855 | 0.7695 | 0.7228 | | 0.621 | 1.34 | 25000 | 0.8840 | 0.7753 | 0.7240 | | 0.5937 | 1.6 | 30000 | 0.7962 | 0.7901 | 0.7516 | | 0.5686 | 1.87 | 35000 | 0.7952 | 0.7967 | 0.7561 | | 0.3671 | 2.14 | 40000 | 0.8418 | 0.7987 | 0.7574 | | 0.3902 | 2.41 | 45000 | 0.8381 | 0.7957 | 0.7598 | | 0.396 | 2.67 | 50000 | 0.8075 | 0.8048 | 0.7694 | | 0.393 | 2.94 | 55000 | 0.7902 | 0.8080 | 0.7732 | | 0.2474 | 3.21 | 60000 | 0.9032 | 0.8080 | 0.7735 | | 0.2644 | 3.47 | 65000 | 0.9130 | 0.8025 | 0.7691 | | 0.2718 | 3.74 | 70000 | 0.8801 | 0.8054 | 0.7697 | | 0.2297 | 4.01 | 75000 | 0.9409 | 0.8096 | 0.7761 | | 0.17 | 4.28 | 80000 | 1.0125 | 0.8032 | 0.7736 | | 0.1986 | 4.54 | 85000 | 0.9995 | 0.8068 | 0.7782 | | 0.2025 | 4.81 | 90000 | 0.9642 | 0.8104 | 0.7824 | | 0.1189 | 5.08 | 95000 | 1.1317 | 0.8028 | 0.7769 | | 0.1335 | 5.34 | 100000 | 1.1220 | 0.8055 | 0.7786 | | 0.152 | 5.61 | 105000 | 1.0980 | 0.8086 | 0.7790 | | 0.1583 | 5.88 | 110000 | 1.0602 | 0.8060 | 0.7772 | | 0.0974 | 6.15 | 115000 | 1.2524 | 0.8045 | 0.7746 | | 0.1184 | 6.41 | 120000 | 1.2211 | 0.8090 | 0.7829 | | 0.1281 | 6.68 | 125000 | 1.1444 | 0.8077 | 0.7812 | | 0.135 | 6.95 | 130000 | 1.1688 | 0.8062 | 0.7769 | | 0.0922 | 7.22 | 135000 | 1.3128 | 0.8067 | 0.7724 | | 0.0953 | 7.48 | 140000 | 1.3277 | 0.8065 | 0.7819 | | 0.1066 | 7.75 | 145000 | 1.3270 | 0.8050 | 0.7756 | | 0.0774 | 8.02 | 150000 | 1.3254 | 0.8050 | 0.7788 | | 0.0764 | 8.28 | 155000 | 1.4299 | 0.8011 | 0.7738 | | 0.0785 | 8.55 | 160000 | 1.4007 | 0.8034 | 0.7711 | | 0.084 | 8.82 | 165000 | 1.3885 | 0.8045 | 0.7766 | | 0.0563 | 9.09 | 170000 | 1.4746 | 0.8035 | 0.7752 | | 0.0645 | 9.35 | 175000 | 1.4897 | 0.8045 | 0.7692 | | 0.0759 | 9.62 | 180000 | 1.4882 | 0.8059 | 0.7769 | | 0.0773 | 9.89 | 185000 | 1.4405 | 0.8045 | 0.7815 | | 0.0546 | 10.15 | 190000 | 1.5088 | 0.8031 | 0.7734 | | 0.0659 | 10.42 | 195000 | 1.5111 | 0.8002 | 0.7692 | | 0.0626 | 10.69 | 200000 | 1.5119 | 0.8051 | 0.7776 | | 0.0673 | 10.96 | 205000 | 1.5103 | 0.8043 | 0.7712 | | 0.05 | 11.22 | 210000 | 1.5920 | 0.8018 | 0.7690 | | 0.0523 | 11.49 | 215000 | 1.6002 | 0.8009 | 0.7671 | | 0.0542 | 11.76 | 220000 | 1.5411 | 0.8041 | 0.7716 | | 0.0383 | 12.03 | 225000 | 1.6058 | 0.8027 | 0.7704 | | 0.0407 | 12.29 | 230000 | 1.6273 | 0.8061 | 0.7775 | | 0.0455 | 12.56 | 235000 | 1.6502 | 0.7989 | 0.7748 | | 0.0558 | 12.83 | 240000 | 1.5998 | 0.8011 | 0.7711 | | 0.0294 | 13.09 | 245000 | 1.6627 | 0.7994 | 0.7732 | | 0.0425 | 13.36 | 250000 | 1.7116 | 0.8003 | 0.7742 | | 0.0422 | 13.63 | 255000 | 1.6802 | 0.8032 | 0.7779 | | 0.0468 | 13.9 | 260000 | 1.6578 | 0.8011 | 0.7714 | | 0.033 | 14.16 | 265000 | 1.7403 | 0.8017 | 0.7721 | | 0.0349 | 14.43 | 270000 | 1.6947 | 0.8021 | 0.7709 | | 0.0394 | 14.7 | 275000 | 1.7328 | 0.8007 | 0.7711 | | 0.0423 | 14.96 | 280000 | 1.6948 | 0.8006 | 0.7721 | | 0.0329 | 15.23 | 285000 | 1.7625 | 0.7996 | 0.7701 | | 0.0297 | 15.5 | 290000 | 1.7449 | 0.8007 | 0.7717 | | 0.0398 | 15.77 | 295000 | 1.7366 | 0.7981 | 0.7654 | | 0.024 | 16.03 | 300000 | 1.7560 | 0.7997 | 0.7703 | | 0.0276 | 16.3 | 305000 | 1.7644 | 0.8004 | 0.7675 | | 0.0361 | 16.57 | 310000 | 1.7596 | 0.8028 | 0.7729 | | 0.0268 | 16.84 | 315000 | 1.7816 | 0.8038 | 0.7736 | | 0.0197 | 17.1 | 320000 | 1.7892 | 0.8022 | 0.7733 | | 0.028 | 17.37 | 325000 | 1.8219 | 0.8027 | 0.7753 | | 0.0287 | 17.64 | 330000 | 1.8050 | 0.8014 | 0.7765 | | 0.0272 | 17.9 | 335000 | 1.8047 | 0.8000 | 0.7725 | | 0.0213 | 18.17 | 340000 | 1.8086 | 0.8011 | 0.7710 | | 0.0254 | 18.44 | 345000 | 1.8148 | 0.8005 | 0.7715 | | 0.0243 | 18.71 | 350000 | 1.8234 | 0.7995 | 0.7675 | | 0.0246 | 18.97 | 355000 | 1.7890 | 0.7980 | 0.7664 | | 0.0214 | 19.24 | 360000 | 1.8467 | 0.7983 | 0.7677 | | 0.0233 | 19.51 | 365000 | 1.8218 | 0.8013 | 0.7690 | | 0.0216 | 19.77 | 370000 | 1.8382 | 0.8019 | 0.7737 | | 0.0153 | 20.04 | 375000 | 1.8232 | 0.8023 | 0.7723 | | 0.0193 | 20.31 | 380000 | 1.8526 | 0.8012 | 0.7724 | | 0.0173 | 20.58 | 385000 | 1.8398 | 0.8034 | 0.7732 | | 0.0205 | 20.84 | 390000 | 1.8113 | 0.8014 | 0.7690 | | 0.0168 | 21.11 | 395000 | 1.8381 | 0.7991 | 0.7680 | | 0.0175 | 21.38 | 400000 | 1.8405 | 0.8019 | 0.7717 | | 0.0186 | 21.65 | 405000 | 1.9043 | 0.8002 | 0.7700 | | 0.0184 | 21.91 | 410000 | 1.8670 | 0.8018 | 0.7704 | | 0.0114 | 22.18 | 415000 | 1.8526 | 0.8026 | 0.7710 | | 0.0135 | 22.45 | 420000 | 1.8631 | 0.8020 | 0.7692 | | 0.019 | 22.71 | 425000 | 1.8668 | 0.8006 | 0.7709 | | 0.0143 | 22.98 | 430000 | 1.8666 | 0.8028 | 0.7729 | | 0.0114 | 23.25 | 435000 | 1.8901 | 0.8014 | 0.7720 | | 0.016 | 23.52 | 440000 | 1.8869 | 0.7994 | 0.7678 | | 0.0132 | 23.78 | 445000 | 1.8972 | 0.8025 | 0.7710 | | 0.0129 | 24.05 | 450000 | 1.9345 | 0.7991 | 0.7667 | | 0.0088 | 24.32 | 455000 | 1.9026 | 0.7997 | 0.7694 | | 0.0136 | 24.58 | 460000 | 1.8971 | 0.8011 | 0.7681 | | 0.0126 | 24.85 | 465000 | 1.9017 | 0.8017 | 0.7721 | | 0.0104 | 25.12 | 470000 | 1.9358 | 0.8028 | 0.7724 | | 0.0067 | 25.39 | 475000 | 1.9320 | 0.8019 | 0.7677 | | 0.0084 | 25.65 | 480000 | 1.9150 | 0.8032 | 0.7729 | | 0.0116 | 25.92 | 485000 | 1.9124 | 0.8020 | 0.7710 | | 0.0076 | 26.19 | 490000 | 1.9507 | 0.8030 | 0.7712 | | 0.012 | 26.46 | 495000 | 1.9480 | 0.8009 | 0.7707 | | 0.0112 | 26.72 | 500000 | 1.8995 | 0.8039 | 0.7726 | | 0.0092 | 26.99 | 505000 | 1.8921 | 0.8021 | 0.7711 | | 0.007 | 27.26 | 510000 | 1.9380 | 0.8019 | 0.7703 | | 0.0087 | 27.52 | 515000 | 1.9166 | 0.8014 | 0.7709 | | 0.0074 | 27.79 | 520000 | 1.9080 | 0.8035 | 0.7743 | | 0.0076 | 28.06 | 525000 | 1.9084 | 0.8029 | 0.7710 | | 0.0086 | 28.33 | 530000 | 1.9215 | 0.8042 | 0.7729 | | 0.0074 | 28.59 | 535000 | 1.9156 | 0.8032 | 0.7723 | | 0.0077 | 28.86 | 540000 | 1.9076 | 0.8033 | 0.7717 | | 0.0066 | 29.13 | 545000 | 1.9227 | 0.8038 | 0.7733 | | 0.0071 | 29.39 | 550000 | 1.9142 | 0.8042 | 0.7729 | | 0.008 | 29.66 | 555000 | 1.9119 | 0.8042 | 0.7725 | | 0.0066 | 29.93 | 560000 | 1.9142 | 0.8046 | 0.7728 | ### Framework versions - Transformers 4.33.3 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.13.3