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PhoBert_Lexical_Dataset51KBoDuoiWithNewLexical

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README.md CHANGED
@@ -13,14 +13,14 @@ model-index:
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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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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/phunganhsang123/huggingface/runs/z7hgo6zr)
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  # PhoBert_Lexical_Dataset51KBoDuoiWithNewLexical
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  This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 4.2529
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- - Accuracy: 0.3281
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- - F1: 0.3249
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  ## Model description
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@@ -51,65 +51,65 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-------:|:-----:|:---------------:|:--------:|:------:|
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- | No log | 0.2506 | 200 | 1.4462 | 0.4371 | 0.4398 |
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- | No log | 0.5013 | 400 | 1.5682 | 0.4338 | 0.4015 |
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- | No log | 0.7519 | 600 | 1.5830 | 0.4015 | 0.3987 |
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- | 0.346 | 1.0025 | 800 | 1.7651 | 0.4194 | 0.3893 |
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- | 0.346 | 1.2531 | 1000 | 1.8355 | 0.4033 | 0.3829 |
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- | 0.346 | 1.5038 | 1200 | 1.9005 | 0.3860 | 0.3873 |
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- | 0.346 | 1.7544 | 1400 | 1.8153 | 0.3991 | 0.3822 |
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- | 0.2583 | 2.0050 | 1600 | 1.9612 | 0.3948 | 0.3778 |
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- | 0.2583 | 2.2556 | 1800 | 2.0532 | 0.3905 | 0.3797 |
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- | 0.2583 | 2.5063 | 2000 | 2.0513 | 0.3817 | 0.3801 |
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- | 0.2583 | 2.7569 | 2200 | 2.2558 | 0.3861 | 0.3747 |
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- | 0.2148 | 3.0075 | 2400 | 2.1839 | 0.3743 | 0.3689 |
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- | 0.2148 | 3.2581 | 2600 | 2.1510 | 0.3928 | 0.3727 |
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- | 0.2148 | 3.5088 | 2800 | 2.1538 | 0.3744 | 0.3718 |
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- | 0.2148 | 3.7594 | 3000 | 2.2206 | 0.3857 | 0.3724 |
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- | 0.1824 | 4.0100 | 3200 | 2.3308 | 0.3840 | 0.3652 |
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- | 0.1824 | 4.2607 | 3400 | 2.5065 | 0.3648 | 0.3579 |
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- | 0.1824 | 4.5113 | 3600 | 2.5492 | 0.3674 | 0.3559 |
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- | 0.1824 | 4.7619 | 3800 | 2.5645 | 0.3767 | 0.3613 |
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- | 0.1518 | 5.0125 | 4000 | 2.5402 | 0.3807 | 0.3609 |
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- | 0.1518 | 5.2632 | 4200 | 2.6824 | 0.3644 | 0.3570 |
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- | 0.1518 | 5.5138 | 4400 | 2.8570 | 0.3578 | 0.3522 |
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- | 0.1518 | 5.7644 | 4600 | 2.5911 | 0.3591 | 0.3533 |
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- | 0.1308 | 6.0150 | 4800 | 2.9337 | 0.3560 | 0.3522 |
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- | 0.1308 | 6.2657 | 5000 | 2.9150 | 0.3506 | 0.3468 |
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- | 0.1308 | 6.5163 | 5200 | 3.0831 | 0.3583 | 0.3473 |
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- | 0.1308 | 6.7669 | 5400 | 2.9995 | 0.3507 | 0.3498 |
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- | 0.1114 | 7.0175 | 5600 | 3.0810 | 0.3443 | 0.3430 |
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- | 0.1114 | 7.2682 | 5800 | 2.9986 | 0.3493 | 0.3435 |
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- | 0.1114 | 7.5188 | 6000 | 3.1596 | 0.3520 | 0.3435 |
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- | 0.1114 | 7.7694 | 6200 | 3.2529 | 0.3461 | 0.3460 |
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- | 0.0941 | 8.0201 | 6400 | 3.3333 | 0.3419 | 0.3420 |
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- | 0.0941 | 8.2707 | 6600 | 3.3867 | 0.3367 | 0.3380 |
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- | 0.0941 | 8.5213 | 6800 | 3.5136 | 0.3441 | 0.3415 |
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- | 0.0941 | 8.7719 | 7000 | 3.4875 | 0.3394 | 0.3362 |
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- | 0.0802 | 9.0226 | 7200 | 3.4856 | 0.3493 | 0.3387 |
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- | 0.0802 | 9.2732 | 7400 | 3.6581 | 0.3347 | 0.3328 |
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- | 0.0802 | 9.5238 | 7600 | 3.5639 | 0.3369 | 0.3356 |
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- | 0.0802 | 9.7744 | 7800 | 3.6503 | 0.3354 | 0.3322 |
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- | 0.0704 | 10.0251 | 8000 | 3.7072 | 0.3339 | 0.3290 |
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- | 0.0704 | 10.2757 | 8200 | 3.7708 | 0.3334 | 0.3290 |
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- | 0.0704 | 10.5263 | 8400 | 3.7225 | 0.3381 | 0.3303 |
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- | 0.0704 | 10.7769 | 8600 | 3.7052 | 0.3270 | 0.3266 |
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- | 0.0615 | 11.0276 | 8800 | 3.8478 | 0.3317 | 0.3274 |
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- | 0.0615 | 11.2782 | 9000 | 3.8760 | 0.3326 | 0.3284 |
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- | 0.0615 | 11.5288 | 9200 | 3.9961 | 0.3294 | 0.3274 |
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- | 0.0615 | 11.7794 | 9400 | 3.9562 | 0.3271 | 0.3253 |
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- | 0.0537 | 12.0301 | 9600 | 4.0215 | 0.3267 | 0.3242 |
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- | 0.0537 | 12.2807 | 9800 | 3.9788 | 0.3333 | 0.3258 |
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- | 0.0537 | 12.5313 | 10000 | 3.9873 | 0.3311 | 0.3248 |
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- | 0.0537 | 12.7820 | 10200 | 4.1186 | 0.3246 | 0.3226 |
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- | 0.048 | 13.0326 | 10400 | 4.1224 | 0.3246 | 0.3225 |
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- | 0.048 | 13.2832 | 10600 | 4.2128 | 0.3253 | 0.3222 |
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- | 0.048 | 13.5338 | 10800 | 4.2580 | 0.3244 | 0.3213 |
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- | 0.048 | 13.7845 | 11000 | 4.2199 | 0.3253 | 0.3237 |
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- | 0.0419 | 14.0351 | 11200 | 4.2173 | 0.3267 | 0.3223 |
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- | 0.0419 | 14.2857 | 11400 | 4.2538 | 0.3261 | 0.3227 |
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- | 0.0419 | 14.5363 | 11600 | 4.2315 | 0.3284 | 0.3236 |
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- | 0.0419 | 14.7870 | 11800 | 4.2529 | 0.3281 | 0.3249 |
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  ### Framework versions
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
14
  should probably proofread and complete it, then remove this comment. -->
15
 
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/phunganhsang123/huggingface/runs/3lc2k8h1)
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  # PhoBert_Lexical_Dataset51KBoDuoiWithNewLexical
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  This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8197
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+ - Accuracy: 0.8365
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+ - F1: 0.8356
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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 | 0.2506 | 200 | 0.7452 | 0.6740 | 0.6750 |
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+ | No log | 0.5013 | 400 | 0.6223 | 0.7292 | 0.7124 |
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+ | No log | 0.7519 | 600 | 0.5929 | 0.7394 | 0.7379 |
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+ | 0.3501 | 1.0025 | 800 | 0.5602 | 0.7622 | 0.7502 |
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+ | 0.3501 | 1.2531 | 1000 | 0.5534 | 0.7711 | 0.7628 |
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+ | 0.3501 | 1.5038 | 1200 | 0.6296 | 0.7518 | 0.7517 |
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+ | 0.3501 | 1.7544 | 1400 | 0.5476 | 0.7646 | 0.7562 |
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+ | 0.2598 | 2.0050 | 1600 | 0.5547 | 0.7742 | 0.7672 |
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+ | 0.2598 | 2.2556 | 1800 | 0.6056 | 0.7662 | 0.7628 |
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+ | 0.2598 | 2.5063 | 2000 | 0.5986 | 0.7575 | 0.7566 |
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+ | 0.2598 | 2.7569 | 2200 | 0.5618 | 0.7851 | 0.7795 |
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+ | 0.2143 | 3.0075 | 2400 | 0.5639 | 0.7806 | 0.7783 |
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+ | 0.2143 | 3.2581 | 2600 | 0.5837 | 0.7726 | 0.7643 |
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+ | 0.2143 | 3.5088 | 2800 | 0.5915 | 0.7735 | 0.7724 |
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+ | 0.2143 | 3.7594 | 3000 | 0.6132 | 0.7772 | 0.7735 |
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+ | 0.184 | 4.0100 | 3200 | 0.5625 | 0.7946 | 0.7895 |
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+ | 0.184 | 4.2607 | 3400 | 0.5947 | 0.7862 | 0.7841 |
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+ | 0.184 | 4.5113 | 3600 | 0.5733 | 0.8033 | 0.7998 |
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+ | 0.184 | 4.7619 | 3800 | 0.6023 | 0.7928 | 0.7882 |
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+ | 0.1534 | 5.0125 | 4000 | 0.5951 | 0.7955 | 0.7901 |
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+ | 0.1534 | 5.2632 | 4200 | 0.6342 | 0.7975 | 0.7953 |
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+ | 0.1534 | 5.5138 | 4400 | 0.6433 | 0.8002 | 0.7982 |
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+ | 0.1534 | 5.7644 | 4600 | 0.6160 | 0.8018 | 0.7998 |
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+ | 0.1316 | 6.0150 | 4800 | 0.6199 | 0.8129 | 0.8102 |
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+ | 0.1316 | 6.2657 | 5000 | 0.6368 | 0.8061 | 0.8043 |
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+ | 0.1316 | 6.5163 | 5200 | 0.6319 | 0.8143 | 0.8099 |
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+ | 0.1316 | 6.7669 | 5400 | 0.6837 | 0.7915 | 0.7900 |
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+ | 0.1123 | 7.0175 | 5600 | 0.7237 | 0.8041 | 0.8036 |
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+ | 0.1123 | 7.2682 | 5800 | 0.6456 | 0.8095 | 0.8079 |
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+ | 0.1123 | 7.5188 | 6000 | 0.6659 | 0.8181 | 0.8152 |
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+ | 0.1123 | 7.7694 | 6200 | 0.7378 | 0.8028 | 0.8021 |
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+ | 0.0958 | 8.0201 | 6400 | 0.6836 | 0.8102 | 0.8095 |
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+ | 0.0958 | 8.2707 | 6600 | 0.7123 | 0.8121 | 0.8122 |
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+ | 0.0958 | 8.5213 | 6800 | 0.7342 | 0.8182 | 0.8163 |
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+ | 0.0958 | 8.7719 | 7000 | 0.7296 | 0.8192 | 0.8178 |
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+ | 0.0806 | 9.0226 | 7200 | 0.7005 | 0.8233 | 0.8208 |
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+ | 0.0806 | 9.2732 | 7400 | 0.7088 | 0.8253 | 0.8237 |
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+ | 0.0806 | 9.5238 | 7600 | 0.7216 | 0.8192 | 0.8185 |
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+ | 0.0806 | 9.7744 | 7800 | 0.7438 | 0.8215 | 0.8205 |
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+ | 0.0712 | 10.0251 | 8000 | 0.7037 | 0.8328 | 0.8315 |
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+ | 0.0712 | 10.2757 | 8200 | 0.7506 | 0.8293 | 0.8282 |
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+ | 0.0712 | 10.5263 | 8400 | 0.7582 | 0.8222 | 0.8215 |
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+ | 0.0712 | 10.7769 | 8600 | 0.7381 | 0.8266 | 0.8258 |
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+ | 0.0622 | 11.0276 | 8800 | 0.7813 | 0.8265 | 0.8251 |
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+ | 0.0622 | 11.2782 | 9000 | 0.7565 | 0.8339 | 0.8330 |
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+ | 0.0622 | 11.5288 | 9200 | 0.7879 | 0.8310 | 0.8307 |
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+ | 0.0622 | 11.7794 | 9400 | 0.7770 | 0.8309 | 0.8305 |
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+ | 0.0534 | 12.0301 | 9600 | 0.7488 | 0.8360 | 0.8353 |
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+ | 0.0534 | 12.2807 | 9800 | 0.7980 | 0.8352 | 0.8340 |
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+ | 0.0534 | 12.5313 | 10000 | 0.7541 | 0.8393 | 0.8381 |
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+ | 0.0534 | 12.7820 | 10200 | 0.7996 | 0.8330 | 0.8324 |
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+ | 0.0482 | 13.0326 | 10400 | 0.7863 | 0.8350 | 0.8343 |
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+ | 0.0482 | 13.2832 | 10600 | 0.8185 | 0.8355 | 0.8349 |
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+ | 0.0482 | 13.5338 | 10800 | 0.8225 | 0.8353 | 0.8346 |
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+ | 0.0482 | 13.7845 | 11000 | 0.8023 | 0.8363 | 0.8355 |
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+ | 0.0426 | 14.0351 | 11200 | 0.8098 | 0.8360 | 0.8352 |
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+ | 0.0426 | 14.2857 | 11400 | 0.8205 | 0.8326 | 0.8319 |
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+ | 0.0426 | 14.5363 | 11600 | 0.8161 | 0.8353 | 0.8344 |
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+ | 0.0426 | 14.7870 | 11800 | 0.8197 | 0.8365 | 0.8356 |
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  ### Framework versions
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