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--- |
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license: mit |
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base_model: FacebookAI/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-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_beta-jason |
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results: [] |
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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-KD-SCR-MSV-D2_data-AmazonScience_massive_all_1_1_beta-jason |
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This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/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: 1.8147 |
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- Accuracy: 0.8277 |
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- F1: 0.8039 |
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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: 4444 |
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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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| 6.0249 | 0.27 | 5000 | 5.8081 | 0.3147 | 0.1329 | |
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| 4.3373 | 0.53 | 10000 | 4.3107 | 0.5396 | 0.3628 | |
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| 3.4678 | 0.8 | 15000 | 3.4302 | 0.6411 | 0.5106 | |
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| 2.7173 | 1.07 | 20000 | 3.0007 | 0.6913 | 0.5899 | |
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| 2.4577 | 1.34 | 25000 | 2.7295 | 0.7203 | 0.6347 | |
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| 2.2593 | 1.6 | 30000 | 2.5136 | 0.7454 | 0.6683 | |
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| 2.1902 | 1.87 | 35000 | 2.3803 | 0.7600 | 0.7085 | |
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| 1.6969 | 2.14 | 40000 | 2.2853 | 0.7723 | 0.7110 | |
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| 1.6768 | 2.41 | 45000 | 2.2101 | 0.7813 | 0.7304 | |
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| 1.6905 | 2.67 | 50000 | 2.1416 | 0.7879 | 0.7439 | |
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| 1.6272 | 2.94 | 55000 | 2.0825 | 0.7935 | 0.7525 | |
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| 1.3056 | 3.21 | 60000 | 2.1342 | 0.7916 | 0.7485 | |
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| 1.3281 | 3.47 | 65000 | 2.0752 | 0.7961 | 0.7578 | |
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| 1.3173 | 3.74 | 70000 | 2.0450 | 0.7997 | 0.7627 | |
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| 1.25 | 4.01 | 75000 | 2.0475 | 0.8050 | 0.7647 | |
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| 1.1003 | 4.28 | 80000 | 2.0769 | 0.8008 | 0.7647 | |
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| 1.085 | 4.54 | 85000 | 2.0143 | 0.8055 | 0.7729 | |
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| 1.1138 | 4.81 | 90000 | 2.0097 | 0.8062 | 0.7741 | |
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| 0.8998 | 5.08 | 95000 | 2.0293 | 0.8042 | 0.7687 | |
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| 0.941 | 5.34 | 100000 | 2.0463 | 0.8035 | 0.7730 | |
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| 0.9522 | 5.61 | 105000 | 2.0034 | 0.8080 | 0.7740 | |
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| 0.9612 | 5.88 | 110000 | 1.9783 | 0.8129 | 0.7818 | |
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| 0.8171 | 6.15 | 115000 | 2.0325 | 0.8105 | 0.7782 | |
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| 0.8311 | 6.41 | 120000 | 2.0537 | 0.8083 | 0.7822 | |
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| 0.8211 | 6.68 | 125000 | 2.0400 | 0.8117 | 0.7812 | |
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| 0.8373 | 6.95 | 130000 | 2.0087 | 0.8089 | 0.7817 | |
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| 0.7282 | 7.22 | 135000 | 2.0047 | 0.8129 | 0.7851 | |
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| 0.7229 | 7.48 | 140000 | 2.0254 | 0.8092 | 0.7847 | |
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| 0.7496 | 7.75 | 145000 | 1.9874 | 0.8159 | 0.7890 | |
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| 0.712 | 8.02 | 150000 | 2.0405 | 0.8122 | 0.7851 | |
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| 0.671 | 8.28 | 155000 | 2.0360 | 0.8128 | 0.7891 | |
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| 0.6774 | 8.55 | 160000 | 2.0099 | 0.8136 | 0.7899 | |
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| 0.7075 | 8.82 | 165000 | 1.9979 | 0.8131 | 0.7852 | |
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| 0.6209 | 9.09 | 170000 | 1.9955 | 0.8152 | 0.7908 | |
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| 0.6128 | 9.35 | 175000 | 1.9814 | 0.8161 | 0.7927 | |
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| 0.6485 | 9.62 | 180000 | 1.9914 | 0.8159 | 0.7925 | |
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| 0.6385 | 9.89 | 185000 | 1.9978 | 0.8169 | 0.7939 | |
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| 0.5789 | 10.15 | 190000 | 2.0151 | 0.8157 | 0.7938 | |
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| 0.5933 | 10.42 | 195000 | 1.9624 | 0.8191 | 0.7952 | |
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| 0.602 | 10.69 | 200000 | 1.9850 | 0.8185 | 0.7931 | |
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| 0.5901 | 10.96 | 205000 | 1.9757 | 0.8175 | 0.7913 | |
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| 0.5686 | 11.22 | 210000 | 1.9776 | 0.8168 | 0.7928 | |
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| 0.5642 | 11.49 | 215000 | 2.0007 | 0.8155 | 0.7918 | |
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| 0.5734 | 11.76 | 220000 | 1.9678 | 0.8183 | 0.7939 | |
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| 0.5281 | 12.03 | 225000 | 1.9652 | 0.8172 | 0.7948 | |
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| 0.5051 | 12.29 | 230000 | 1.9726 | 0.8188 | 0.7933 | |
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| 0.5247 | 12.56 | 235000 | 1.9615 | 0.8188 | 0.7937 | |
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| 0.5239 | 12.83 | 240000 | 1.9451 | 0.8188 | 0.7964 | |
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| 0.4843 | 13.09 | 245000 | 1.9342 | 0.8203 | 0.7939 | |
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| 0.5033 | 13.36 | 250000 | 1.9629 | 0.8182 | 0.7964 | |
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| 0.5131 | 13.63 | 255000 | 1.9466 | 0.8181 | 0.7959 | |
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| 0.5116 | 13.9 | 260000 | 1.9256 | 0.8206 | 0.7966 | |
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| 0.4832 | 14.16 | 265000 | 1.9252 | 0.8206 | 0.7993 | |
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| 0.4746 | 14.43 | 270000 | 1.9285 | 0.8197 | 0.7956 | |
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| 0.483 | 14.7 | 275000 | 1.9409 | 0.8188 | 0.7933 | |
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| 0.4955 | 14.96 | 280000 | 1.9275 | 0.8211 | 0.7963 | |
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| 0.4609 | 15.23 | 285000 | 1.9160 | 0.8213 | 0.7963 | |
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| 0.477 | 15.5 | 290000 | 1.9189 | 0.8218 | 0.7997 | |
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| 0.4685 | 15.77 | 295000 | 1.9135 | 0.8216 | 0.7970 | |
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| 0.4449 | 16.03 | 300000 | 1.8993 | 0.8232 | 0.7964 | |
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| 0.444 | 16.3 | 305000 | 1.8900 | 0.8231 | 0.7979 | |
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| 0.4584 | 16.57 | 310000 | 1.9016 | 0.8220 | 0.7991 | |
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| 0.4401 | 16.84 | 315000 | 1.8982 | 0.8213 | 0.7951 | |
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| 0.4252 | 17.1 | 320000 | 1.8930 | 0.8228 | 0.7997 | |
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| 0.438 | 17.37 | 325000 | 1.8836 | 0.8222 | 0.7984 | |
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| 0.4391 | 17.64 | 330000 | 1.8935 | 0.8220 | 0.7991 | |
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| 0.4471 | 17.9 | 335000 | 1.8970 | 0.8212 | 0.7988 | |
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| 0.4159 | 18.17 | 340000 | 1.8877 | 0.8235 | 0.8001 | |
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| 0.4227 | 18.44 | 345000 | 1.8950 | 0.8226 | 0.7995 | |
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| 0.4178 | 18.71 | 350000 | 1.8888 | 0.8233 | 0.7992 | |
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| 0.4214 | 18.97 | 355000 | 1.8758 | 0.8222 | 0.7974 | |
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| 0.4113 | 19.24 | 360000 | 1.8703 | 0.8235 | 0.7975 | |
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| 0.4066 | 19.51 | 365000 | 1.8790 | 0.8243 | 0.7994 | |
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| 0.413 | 19.77 | 370000 | 1.8561 | 0.8248 | 0.8013 | |
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| 0.4099 | 20.04 | 375000 | 1.8576 | 0.8240 | 0.7993 | |
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| 0.4092 | 20.31 | 380000 | 1.8591 | 0.8252 | 0.8030 | |
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| 0.3952 | 20.58 | 385000 | 1.8563 | 0.8258 | 0.8023 | |
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| 0.4017 | 20.84 | 390000 | 1.8603 | 0.8253 | 0.8009 | |
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| 0.3964 | 21.11 | 395000 | 1.8472 | 0.8251 | 0.8028 | |
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| 0.3891 | 21.38 | 400000 | 1.8540 | 0.8251 | 0.8014 | |
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| 0.3881 | 21.65 | 405000 | 1.8558 | 0.8247 | 0.8010 | |
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| 0.3953 | 21.91 | 410000 | 1.8594 | 0.8244 | 0.8005 | |
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| 0.3872 | 22.18 | 415000 | 1.8576 | 0.8249 | 0.7995 | |
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| 0.3754 | 22.45 | 420000 | 1.8486 | 0.8255 | 0.8008 | |
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| 0.3798 | 22.71 | 425000 | 1.8511 | 0.8252 | 0.7998 | |
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| 0.3744 | 22.98 | 430000 | 1.8491 | 0.8242 | 0.7987 | |
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| 0.3691 | 23.25 | 435000 | 1.8343 | 0.8247 | 0.7995 | |
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| 0.3718 | 23.52 | 440000 | 1.8496 | 0.8247 | 0.7997 | |
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| 0.3761 | 23.78 | 445000 | 1.8433 | 0.8251 | 0.8016 | |
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| 0.3634 | 24.05 | 450000 | 1.8397 | 0.8247 | 0.8000 | |
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| 0.3704 | 24.32 | 455000 | 1.8334 | 0.8254 | 0.8007 | |
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| 0.3651 | 24.58 | 460000 | 1.8367 | 0.8255 | 0.8022 | |
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| 0.3649 | 24.85 | 465000 | 1.8321 | 0.8258 | 0.8022 | |
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| 0.3573 | 25.12 | 470000 | 1.8358 | 0.8255 | 0.8020 | |
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| 0.355 | 25.39 | 475000 | 1.8301 | 0.8259 | 0.8023 | |
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| 0.3595 | 25.65 | 480000 | 1.8257 | 0.8263 | 0.8042 | |
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| 0.3625 | 25.92 | 485000 | 1.8244 | 0.8265 | 0.8025 | |
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| 0.3508 | 26.19 | 490000 | 1.8303 | 0.8254 | 0.8021 | |
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| 0.3578 | 26.46 | 495000 | 1.8261 | 0.8264 | 0.8035 | |
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| 0.3506 | 26.72 | 500000 | 1.8200 | 0.8267 | 0.8029 | |
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| 0.3514 | 26.99 | 505000 | 1.8247 | 0.8266 | 0.8032 | |
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| 0.3496 | 27.26 | 510000 | 1.8255 | 0.8269 | 0.8046 | |
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| 0.3435 | 27.52 | 515000 | 1.8173 | 0.8262 | 0.8019 | |
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| 0.3502 | 27.79 | 520000 | 1.8137 | 0.8269 | 0.8035 | |
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| 0.3463 | 28.06 | 525000 | 1.8167 | 0.8265 | 0.8024 | |
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| 0.344 | 28.33 | 530000 | 1.8144 | 0.8275 | 0.8023 | |
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| 0.3469 | 28.59 | 535000 | 1.8133 | 0.8268 | 0.8021 | |
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| 0.3458 | 28.86 | 540000 | 1.8153 | 0.8254 | 0.8013 | |
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| 0.3387 | 29.13 | 545000 | 1.8111 | 0.8269 | 0.8019 | |
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| 0.3346 | 29.39 | 550000 | 1.8097 | 0.8271 | 0.8036 | |
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| 0.3393 | 29.66 | 555000 | 1.8179 | 0.8268 | 0.8033 | |
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| 0.3398 | 29.93 | 560000 | 1.8147 | 0.8277 | 0.8039 | |
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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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