Initial Commit
Browse files- README.md +113 -113
- eval_results_ml.json +1 -1
- pytorch_model.bin +1 -1
- training_args.bin +2 -2
README.md
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metrics:
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- name: Accuracy
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type: accuracy
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- name: F1
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type: f1
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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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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.
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- Accuracy: 0.
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- F1: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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| No log | 0.28 | 100 |
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| No log | 0.56 | 200 | 2.
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| No log | 0.83 | 300 | 1.
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| No log | 1.11 | 400 | 1.
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### Framework versions
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.7360994569987366
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- name: F1
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type: f1
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value: 0.688120673898054
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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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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the massive dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.5243
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- Accuracy: 0.7361
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- F1: 0.6881
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
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| No log | 0.28 | 100 | 2.9524 | 0.3055 | 0.0759 |
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| No log | 0.56 | 200 | 2.0666 | 0.4969 | 0.2470 |
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| No log | 0.83 | 300 | 1.6852 | 0.5878 | 0.3626 |
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| No log | 1.11 | 400 | 1.4751 | 0.6376 | 0.4564 |
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| 1.9292 | 1.39 | 500 | 1.4633 | 0.6522 | 0.4887 |
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| 1.9292 | 1.67 | 600 | 1.4477 | 0.6604 | 0.5016 |
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| 1.9292 | 1.94 | 700 | 1.3844 | 0.6758 | 0.5746 |
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| 1.9292 | 2.22 | 800 | 1.3272 | 0.6937 | 0.5857 |
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| 1.9292 | 2.5 | 900 | 1.2445 | 0.7178 | 0.6192 |
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| 0.6258 | 2.78 | 1000 | 1.3348 | 0.7128 | 0.6198 |
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| 0.6258 | 3.06 | 1100 | 1.5354 | 0.6734 | 0.6006 |
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| 0.6258 | 3.33 | 1200 | 1.4149 | 0.7001 | 0.6258 |
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| 0.6258 | 3.61 | 1300 | 1.4474 | 0.7032 | 0.6405 |
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| 0.6258 | 3.89 | 1400 | 1.5031 | 0.7054 | 0.6395 |
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| 0.3592 | 4.17 | 1500 | 1.3733 | 0.7225 | 0.6669 |
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| 0.3592 | 4.44 | 1600 | 1.3757 | 0.7317 | 0.6555 |
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| 0.3592 | 4.72 | 1700 | 1.4694 | 0.7134 | 0.6539 |
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| 0.3592 | 5.0 | 1800 | 1.4733 | 0.7077 | 0.6461 |
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| 0.3592 | 5.28 | 1900 | 1.5077 | 0.7232 | 0.6654 |
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| 0.2316 | 5.56 | 2000 | 1.6396 | 0.7069 | 0.6482 |
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| 0.2316 | 5.83 | 2100 | 1.5588 | 0.7178 | 0.6599 |
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| 0.2316 | 6.11 | 2200 | 1.5611 | 0.7206 | 0.6507 |
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| 0.2316 | 6.39 | 2300 | 1.7029 | 0.7155 | 0.6597 |
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| 0.2316 | 6.67 | 2400 | 1.7865 | 0.7048 | 0.6407 |
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| 0.1763 | 6.94 | 2500 | 1.7791 | 0.7065 | 0.6555 |
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| 0.1763 | 7.22 | 2600 | 1.8013 | 0.7176 | 0.6572 |
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| 0.1763 | 7.5 | 2700 | 1.8034 | 0.7149 | 0.6578 |
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| 0.1763 | 7.78 | 2800 | 2.0082 | 0.6872 | 0.6234 |
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| 0.1763 | 8.06 | 2900 | 2.0108 | 0.7011 | 0.6388 |
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| 0.1136 | 8.33 | 3000 | 2.0779 | 0.6997 | 0.6513 |
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| 0.1136 | 8.61 | 3100 | 1.9805 | 0.7122 | 0.6555 |
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| 0.1136 | 8.89 | 3200 | 2.1014 | 0.7010 | 0.6485 |
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| 0.1136 | 9.17 | 3300 | 1.9710 | 0.7133 | 0.6537 |
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| 0.1136 | 9.44 | 3400 | 1.9677 | 0.7152 | 0.6564 |
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| 0.0964 | 9.72 | 3500 | 2.0902 | 0.7079 | 0.6535 |
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| 0.0964 | 10.0 | 3600 | 2.0776 | 0.7083 | 0.6529 |
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| 0.0964 | 10.28 | 3700 | 2.0649 | 0.7191 | 0.6647 |
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| 0.0964 | 10.56 | 3800 | 2.0690 | 0.7152 | 0.6551 |
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| 0.0964 | 10.83 | 3900 | 2.1585 | 0.7055 | 0.6513 |
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| 0.0721 | 11.11 | 4000 | 2.0158 | 0.7236 | 0.6660 |
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| 0.0721 | 11.39 | 4100 | 2.1559 | 0.7120 | 0.6616 |
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| 0.0721 | 11.67 | 4200 | 2.0517 | 0.7253 | 0.6694 |
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| 0.0721 | 11.94 | 4300 | 2.1721 | 0.7219 | 0.6662 |
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| 0.0721 | 12.22 | 4400 | 2.2949 | 0.7079 | 0.6680 |
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| 0.0448 | 12.5 | 4500 | 2.1676 | 0.7186 | 0.6685 |
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| 0.0448 | 12.78 | 4600 | 2.0882 | 0.7227 | 0.6636 |
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| 0.0448 | 13.06 | 4700 | 2.0149 | 0.7335 | 0.6736 |
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| 0.0448 | 13.33 | 4800 | 2.2128 | 0.7243 | 0.6667 |
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| 0.0448 | 13.61 | 4900 | 2.2664 | 0.7200 | 0.6577 |
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| 0.0371 | 13.89 | 5000 | 2.3489 | 0.7100 | 0.6656 |
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| 0.0371 | 14.17 | 5100 | 2.3454 | 0.7087 | 0.6531 |
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| 0.0371 | 14.44 | 5200 | 2.2062 | 0.7296 | 0.6767 |
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| 0.0371 | 14.72 | 5300 | 2.4544 | 0.7101 | 0.6661 |
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| 0.0371 | 15.0 | 5400 | 2.2581 | 0.7275 | 0.6683 |
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| 0.0227 | 15.28 | 5500 | 2.2904 | 0.7242 | 0.6697 |
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| 0.0227 | 15.56 | 5600 | 2.3484 | 0.7152 | 0.6495 |
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| 0.0227 | 15.83 | 5700 | 2.4505 | 0.7126 | 0.6599 |
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| 0.0227 | 16.11 | 5800 | 2.2985 | 0.7236 | 0.6673 |
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| 0.0227 | 16.39 | 5900 | 2.3929 | 0.7245 | 0.6751 |
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| 0.022 | 16.67 | 6000 | 2.4606 | 0.7200 | 0.6643 |
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| 0.022 | 16.94 | 6100 | 2.3481 | 0.7276 | 0.6689 |
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| 0.022 | 17.22 | 6200 | 2.3302 | 0.7273 | 0.6724 |
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| 0.022 | 17.5 | 6300 | 2.3566 | 0.7292 | 0.6787 |
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| 0.022 | 17.78 | 6400 | 2.3972 | 0.7281 | 0.6785 |
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| 0.0133 | 18.06 | 6500 | 2.5105 | 0.7205 | 0.6705 |
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| 0.0133 | 18.33 | 6600 | 2.3785 | 0.7295 | 0.6775 |
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| 0.0133 | 18.61 | 6700 | 2.4367 | 0.7220 | 0.6676 |
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| 0.0133 | 18.89 | 6800 | 2.4496 | 0.7255 | 0.6690 |
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| 0.0133 | 19.17 | 6900 | 2.4133 | 0.7279 | 0.6720 |
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| 0.0097 | 19.44 | 7000 | 2.5588 | 0.7140 | 0.6652 |
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| 0.0097 | 19.72 | 7100 | 2.4906 | 0.7210 | 0.6656 |
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| 0.0097 | 20.0 | 7200 | 2.5187 | 0.7199 | 0.6619 |
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| 0.0097 | 20.28 | 7300 | 2.4627 | 0.7254 | 0.6686 |
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| 0.0097 | 20.56 | 7400 | 2.5543 | 0.7187 | 0.6615 |
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| 0.0096 | 20.83 | 7500 | 2.4262 | 0.7259 | 0.6676 |
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| 0.0096 | 21.11 | 7600 | 2.4768 | 0.7256 | 0.6699 |
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| 0.0096 | 21.39 | 7700 | 2.5336 | 0.7220 | 0.6724 |
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| 0.0096 | 21.67 | 7800 | 2.5221 | 0.7240 | 0.6703 |
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| 0.0096 | 21.94 | 7900 | 2.5008 | 0.7269 | 0.6712 |
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| 0.0086 | 22.22 | 8000 | 2.4998 | 0.7278 | 0.6703 |
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| 0.0086 | 22.5 | 8100 | 2.4611 | 0.7319 | 0.6842 |
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| 0.0086 | 22.78 | 8200 | 2.5119 | 0.7313 | 0.6832 |
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| 0.0086 | 23.06 | 8300 | 2.4329 | 0.7300 | 0.6764 |
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| 0.0086 | 23.33 | 8400 | 2.4080 | 0.7317 | 0.6822 |
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| 0.007 | 23.61 | 8500 | 2.4054 | 0.7313 | 0.6802 |
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| 0.007 | 23.89 | 8600 | 2.4345 | 0.7334 | 0.6851 |
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| 0.007 | 24.17 | 8700 | 2.4735 | 0.7326 | 0.6865 |
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| 0.007 | 24.44 | 8800 | 2.4718 | 0.7313 | 0.6843 |
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| 0.007 | 24.72 | 8900 | 2.4391 | 0.7328 | 0.6818 |
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| 0.0029 | 25.0 | 9000 | 2.5152 | 0.7290 | 0.6869 |
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| 0.0029 | 25.28 | 9100 | 2.4609 | 0.7365 | 0.6908 |
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| 0.0029 | 25.56 | 9200 | 2.4717 | 0.7359 | 0.6932 |
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| 0.0029 | 25.83 | 9300 | 2.5283 | 0.7337 | 0.6881 |
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| 0.0029 | 26.11 | 9400 | 2.4831 | 0.7342 | 0.6866 |
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| 0.0026 | 26.39 | 9500 | 2.5291 | 0.7325 | 0.6861 |
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| 0.0026 | 26.67 | 9600 | 2.5201 | 0.7344 | 0.6855 |
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| 0.0026 | 26.94 | 9700 | 2.5496 | 0.7322 | 0.6857 |
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| 0.0026 | 27.22 | 9800 | 2.5302 | 0.7332 | 0.6853 |
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| 0.0026 | 27.5 | 9900 | 2.5388 | 0.7329 | 0.6871 |
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| 0.0025 | 27.78 | 10000 | 2.5210 | 0.7326 | 0.6845 |
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| 0.0025 | 28.06 | 10100 | 2.5482 | 0.7319 | 0.6841 |
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| 0.0025 | 28.33 | 10200 | 2.5628 | 0.7315 | 0.6853 |
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| 0.0025 | 28.61 | 10300 | 2.5439 | 0.7341 | 0.6870 |
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| 0.0025 | 28.89 | 10400 | 2.5241 | 0.7356 | 0.6875 |
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| 0.001 | 29.17 | 10500 | 2.5238 | 0.7354 | 0.6873 |
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| 0.001 | 29.44 | 10600 | 2.5186 | 0.7362 | 0.6880 |
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| 0.001 | 29.72 | 10700 | 2.5237 | 0.7360 | 0.6880 |
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| 0.001 | 30.0 | 10800 | 2.5243 | 0.7361 | 0.6881 |
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### Framework versions
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eval_results_ml.json
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