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--- |
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license: mit |
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base_model: xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- massive |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: scenario-NON-KD-SCR-D2_data-AmazonScience_massive_all_1_1111 |
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results: |
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- task: |
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name: Text Classification |
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type: text-classification |
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dataset: |
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name: massive |
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type: massive |
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config: all_1.1 |
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split: validation |
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args: all_1.1 |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8041450679178176 |
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- name: F1 |
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type: f1 |
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value: 0.7741242752130463 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# scenario-NON-KD-SCR-D2_data-AmazonScience_massive_all_1_1111 |
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This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the massive dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.9383 |
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- Accuracy: 0.8041 |
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- F1: 0.7741 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 111 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:------:|:---------------:|:--------:|:------:| |
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| 1.625 | 0.27 | 5000 | 1.5556 | 0.5832 | 0.4573 | |
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| 1.1017 | 0.53 | 10000 | 1.0835 | 0.7113 | 0.6333 | |
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| 0.9149 | 0.8 | 15000 | 0.9682 | 0.7414 | 0.6802 | |
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| 0.652 | 1.07 | 20000 | 0.8972 | 0.7697 | 0.7151 | |
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| 0.6198 | 1.34 | 25000 | 0.8545 | 0.7780 | 0.7314 | |
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| 0.6266 | 1.6 | 30000 | 0.8015 | 0.7901 | 0.7412 | |
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| 0.5837 | 1.87 | 35000 | 0.7959 | 0.7950 | 0.7590 | |
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| 0.3694 | 2.14 | 40000 | 0.8423 | 0.7980 | 0.7569 | |
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| 0.3908 | 2.41 | 45000 | 0.8553 | 0.7916 | 0.7578 | |
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| 0.4034 | 2.67 | 50000 | 0.7928 | 0.8039 | 0.7667 | |
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| 0.391 | 2.94 | 55000 | 0.8013 | 0.8061 | 0.7698 | |
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| 0.2482 | 3.21 | 60000 | 0.8858 | 0.8043 | 0.7719 | |
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| 0.2658 | 3.47 | 65000 | 0.9176 | 0.8043 | 0.7679 | |
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| 0.2786 | 3.74 | 70000 | 0.8828 | 0.8052 | 0.7720 | |
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| 0.2445 | 4.01 | 75000 | 0.9493 | 0.8075 | 0.7717 | |
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| 0.1873 | 4.28 | 80000 | 1.0095 | 0.8028 | 0.7741 | |
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| 0.2023 | 4.54 | 85000 | 0.9853 | 0.8041 | 0.7703 | |
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| 0.2141 | 4.81 | 90000 | 0.9830 | 0.8081 | 0.7797 | |
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| 0.1302 | 5.08 | 95000 | 1.0565 | 0.8081 | 0.7790 | |
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| 0.1478 | 5.34 | 100000 | 1.0957 | 0.8061 | 0.7770 | |
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| 0.1544 | 5.61 | 105000 | 1.0758 | 0.8036 | 0.7636 | |
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| 0.1529 | 5.88 | 110000 | 1.1086 | 0.8057 | 0.7671 | |
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| 0.1056 | 6.15 | 115000 | 1.2581 | 0.8024 | 0.7727 | |
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| 0.1119 | 6.41 | 120000 | 1.2179 | 0.8046 | 0.7757 | |
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| 0.1191 | 6.68 | 125000 | 1.2204 | 0.8039 | 0.7752 | |
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| 0.1276 | 6.95 | 130000 | 1.1614 | 0.8056 | 0.7735 | |
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| 0.087 | 7.22 | 135000 | 1.3199 | 0.8037 | 0.7744 | |
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| 0.0974 | 7.48 | 140000 | 1.3204 | 0.8062 | 0.7765 | |
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| 0.0977 | 7.75 | 145000 | 1.3529 | 0.7993 | 0.7702 | |
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| 0.0741 | 8.02 | 150000 | 1.3510 | 0.8030 | 0.7783 | |
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| 0.0736 | 8.28 | 155000 | 1.4104 | 0.8029 | 0.7730 | |
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| 0.0836 | 8.55 | 160000 | 1.4256 | 0.8000 | 0.7687 | |
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| 0.0874 | 8.82 | 165000 | 1.4120 | 0.8024 | 0.7680 | |
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| 0.06 | 9.09 | 170000 | 1.4139 | 0.8029 | 0.7740 | |
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| 0.0722 | 9.35 | 175000 | 1.4836 | 0.8010 | 0.7712 | |
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| 0.0729 | 9.62 | 180000 | 1.4753 | 0.8003 | 0.7724 | |
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| 0.0733 | 9.89 | 185000 | 1.4762 | 0.8015 | 0.7747 | |
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| 0.0537 | 10.15 | 190000 | 1.5196 | 0.8022 | 0.7735 | |
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| 0.0579 | 10.42 | 195000 | 1.5303 | 0.8022 | 0.7741 | |
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| 0.0633 | 10.69 | 200000 | 1.5843 | 0.8023 | 0.7730 | |
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| 0.0702 | 10.96 | 205000 | 1.5198 | 0.8042 | 0.7779 | |
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| 0.0473 | 11.22 | 210000 | 1.6088 | 0.8008 | 0.7716 | |
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| 0.0506 | 11.49 | 215000 | 1.6281 | 0.8001 | 0.7740 | |
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| 0.0601 | 11.76 | 220000 | 1.5632 | 0.8032 | 0.7758 | |
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| 0.0369 | 12.03 | 225000 | 1.6079 | 0.8012 | 0.7699 | |
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| 0.0492 | 12.29 | 230000 | 1.6162 | 0.8004 | 0.7726 | |
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| 0.0473 | 12.56 | 235000 | 1.6604 | 0.8004 | 0.7725 | |
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| 0.0449 | 12.83 | 240000 | 1.5631 | 0.8023 | 0.7763 | |
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| 0.0334 | 13.09 | 245000 | 1.6218 | 0.8049 | 0.7734 | |
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| 0.0426 | 13.36 | 250000 | 1.6875 | 0.8004 | 0.7736 | |
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| 0.0483 | 13.63 | 255000 | 1.6627 | 0.8028 | 0.7736 | |
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| 0.0514 | 13.9 | 260000 | 1.6705 | 0.8014 | 0.7705 | |
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| 0.0357 | 14.16 | 265000 | 1.7121 | 0.8008 | 0.7759 | |
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| 0.0313 | 14.43 | 270000 | 1.7074 | 0.7993 | 0.7714 | |
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| 0.0405 | 14.7 | 275000 | 1.6907 | 0.7973 | 0.7619 | |
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| 0.0473 | 14.96 | 280000 | 1.7018 | 0.8006 | 0.7707 | |
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| 0.0364 | 15.23 | 285000 | 1.7487 | 0.8009 | 0.7725 | |
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| 0.0369 | 15.5 | 290000 | 1.7177 | 0.7996 | 0.7635 | |
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| 0.0407 | 15.77 | 295000 | 1.7514 | 0.7981 | 0.7676 | |
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| 0.0215 | 16.03 | 300000 | 1.8013 | 0.8003 | 0.7738 | |
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| 0.0251 | 16.3 | 305000 | 1.7813 | 0.8001 | 0.7700 | |
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| 0.0306 | 16.57 | 310000 | 1.7511 | 0.8029 | 0.7748 | |
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| 0.0328 | 16.84 | 315000 | 1.7910 | 0.8015 | 0.7750 | |
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| 0.0191 | 17.1 | 320000 | 1.8131 | 0.8002 | 0.7663 | |
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| 0.0231 | 17.37 | 325000 | 1.7831 | 0.8027 | 0.7771 | |
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| 0.0274 | 17.64 | 330000 | 1.7864 | 0.8025 | 0.7743 | |
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| 0.0355 | 17.9 | 335000 | 1.8057 | 0.8004 | 0.7693 | |
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| 0.019 | 18.17 | 340000 | 1.8307 | 0.8001 | 0.7704 | |
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| 0.0255 | 18.44 | 345000 | 1.8017 | 0.7999 | 0.7681 | |
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| 0.033 | 18.71 | 350000 | 1.8074 | 0.7983 | 0.7701 | |
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| 0.0329 | 18.97 | 355000 | 1.8416 | 0.7988 | 0.7690 | |
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| 0.0216 | 19.24 | 360000 | 1.8396 | 0.8003 | 0.7719 | |
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| 0.0234 | 19.51 | 365000 | 1.8631 | 0.7999 | 0.7707 | |
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| 0.0228 | 19.77 | 370000 | 1.8195 | 0.8031 | 0.7751 | |
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| 0.0148 | 20.04 | 375000 | 1.8301 | 0.8026 | 0.7731 | |
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| 0.0203 | 20.31 | 380000 | 1.8525 | 0.8009 | 0.7709 | |
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| 0.0183 | 20.58 | 385000 | 1.8466 | 0.7978 | 0.7678 | |
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| 0.0171 | 20.84 | 390000 | 1.8859 | 0.8016 | 0.7751 | |
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| 0.0156 | 21.11 | 395000 | 1.8790 | 0.8000 | 0.7698 | |
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| 0.0169 | 21.38 | 400000 | 1.8781 | 0.8015 | 0.7733 | |
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| 0.0193 | 21.65 | 405000 | 1.8454 | 0.8016 | 0.7723 | |
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| 0.0157 | 21.91 | 410000 | 1.8695 | 0.8008 | 0.7710 | |
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| 0.0111 | 22.18 | 415000 | 1.8899 | 0.8010 | 0.7718 | |
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| 0.0178 | 22.45 | 420000 | 1.8696 | 0.7990 | 0.7692 | |
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| 0.0183 | 22.71 | 425000 | 1.8613 | 0.8006 | 0.7722 | |
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| 0.0202 | 22.98 | 430000 | 1.8738 | 0.7991 | 0.7685 | |
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| 0.0127 | 23.25 | 435000 | 1.8803 | 0.8039 | 0.7753 | |
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| 0.0139 | 23.52 | 440000 | 1.9212 | 0.7983 | 0.7669 | |
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| 0.0149 | 23.78 | 445000 | 1.8538 | 0.8016 | 0.7716 | |
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| 0.0094 | 24.05 | 450000 | 1.9183 | 0.8010 | 0.7729 | |
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| 0.0125 | 24.32 | 455000 | 1.9316 | 0.7997 | 0.7709 | |
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| 0.015 | 24.58 | 460000 | 1.8689 | 0.8011 | 0.7713 | |
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| 0.013 | 24.85 | 465000 | 1.9028 | 0.8015 | 0.7734 | |
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| 0.0114 | 25.12 | 470000 | 1.9559 | 0.8003 | 0.7705 | |
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| 0.0105 | 25.39 | 475000 | 1.9195 | 0.8013 | 0.7705 | |
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| 0.0138 | 25.65 | 480000 | 1.8951 | 0.8032 | 0.7739 | |
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| 0.0125 | 25.92 | 485000 | 1.9088 | 0.8024 | 0.7735 | |
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| 0.0087 | 26.19 | 490000 | 1.9183 | 0.8001 | 0.7687 | |
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| 0.0089 | 26.46 | 495000 | 1.9353 | 0.8027 | 0.7754 | |
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| 0.0088 | 26.72 | 500000 | 1.8883 | 0.8005 | 0.7692 | |
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| 0.0114 | 26.99 | 505000 | 1.9141 | 0.8035 | 0.7741 | |
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| 0.0094 | 27.26 | 510000 | 1.9412 | 0.8032 | 0.7730 | |
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| 0.007 | 27.52 | 515000 | 1.9465 | 0.8029 | 0.7735 | |
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| 0.0077 | 27.79 | 520000 | 1.9341 | 0.8046 | 0.7765 | |
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| 0.0065 | 28.06 | 525000 | 1.9372 | 0.8038 | 0.7739 | |
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| 0.0092 | 28.33 | 530000 | 1.9510 | 0.8030 | 0.7740 | |
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| 0.0103 | 28.59 | 535000 | 1.9216 | 0.8033 | 0.7724 | |
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| 0.0082 | 28.86 | 540000 | 1.9275 | 0.8031 | 0.7726 | |
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| 0.0076 | 29.13 | 545000 | 1.9477 | 0.8036 | 0.7735 | |
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| 0.0063 | 29.39 | 550000 | 1.9361 | 0.8033 | 0.7722 | |
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| 0.0073 | 29.66 | 555000 | 1.9427 | 0.8038 | 0.7737 | |
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| 0.006 | 29.93 | 560000 | 1.9383 | 0.8041 | 0.7741 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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