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--- |
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license: mit |
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base_model: cardiffnlp/twitter-roberta-base-2019-90m |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: 2020-Q2-50p-filtered |
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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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# 2020-Q2-50p-filtered |
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2019-90m](https://huggingface.co/cardiffnlp/twitter-roberta-base-2019-90m) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.5608 |
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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: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1400 |
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- training_steps: 2400000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-------:|:---------------:| |
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| No log | 0.03 | 8000 | 2.6645 | |
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| 2.8656 | 0.07 | 16000 | 2.6465 | |
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| 2.8656 | 0.1 | 24000 | 2.6186 | |
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| 2.7946 | 0.13 | 32000 | 2.6235 | |
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| 2.7946 | 0.17 | 40000 | 2.6151 | |
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| 2.7911 | 0.2 | 48000 | 2.6128 | |
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| 2.7911 | 0.24 | 56000 | 2.6010 | |
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| 2.7898 | 0.27 | 64000 | 2.6144 | |
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| 2.7898 | 0.3 | 72000 | 2.5976 | |
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| 2.7791 | 0.34 | 80000 | 2.6006 | |
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| 2.7791 | 0.37 | 88000 | 2.5889 | |
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| 2.7776 | 0.4 | 96000 | 2.5888 | |
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| 2.7776 | 0.44 | 104000 | 2.5842 | |
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| 2.7702 | 0.47 | 112000 | 2.5760 | |
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| 2.7702 | 0.51 | 120000 | 2.5720 | |
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| 2.7661 | 0.54 | 128000 | 2.5710 | |
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| 2.7661 | 0.57 | 136000 | 2.5673 | |
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| 2.7609 | 0.61 | 144000 | 2.5693 | |
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| 2.7609 | 0.64 | 152000 | 2.5623 | |
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| 2.7557 | 0.67 | 160000 | 2.5559 | |
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| 2.7557 | 0.71 | 168000 | 2.5650 | |
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| 2.7584 | 0.74 | 176000 | 2.5584 | |
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| 2.7584 | 0.77 | 184000 | 2.5591 | |
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| 2.7619 | 0.81 | 192000 | 2.5597 | |
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| 2.7619 | 0.84 | 200000 | 2.5650 | |
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| 2.7678 | 0.88 | 208000 | 2.5728 | |
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| 2.7678 | 0.91 | 216000 | 2.5712 | |
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| 2.7735 | 0.94 | 224000 | 2.5729 | |
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| 2.7735 | 0.98 | 232000 | 2.5755 | |
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| 2.777 | 1.01 | 240000 | 2.5715 | |
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| 2.777 | 1.04 | 248000 | 2.5747 | |
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| 2.7692 | 1.08 | 256000 | 2.5782 | |
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| 2.7692 | 1.11 | 264000 | 2.5841 | |
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| 2.7826 | 1.15 | 272000 | 2.5731 | |
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| 2.7826 | 1.18 | 280000 | 2.5836 | |
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| 2.7845 | 1.21 | 288000 | 2.5841 | |
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| 2.7845 | 1.25 | 296000 | 2.5811 | |
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| 2.7909 | 1.28 | 304000 | 2.5928 | |
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| 2.7909 | 1.31 | 312000 | 2.5977 | |
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| 2.7993 | 1.35 | 320000 | 2.6025 | |
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| 2.7993 | 1.38 | 328000 | 2.6072 | |
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| 2.8107 | 1.41 | 336000 | 2.6110 | |
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| 2.8107 | 1.45 | 344000 | 2.6020 | |
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| 2.8102 | 1.48 | 352000 | 2.6065 | |
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| 2.8102 | 1.52 | 360000 | 2.6207 | |
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| 2.8247 | 1.55 | 368000 | 2.6192 | |
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| 2.8247 | 1.58 | 376000 | 2.6224 | |
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| 2.8271 | 1.62 | 384000 | 2.6205 | |
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| 2.8271 | 1.65 | 392000 | 2.6292 | |
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| 2.8415 | 1.68 | 400000 | 2.6348 | |
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| 2.8415 | 1.72 | 408000 | 2.6518 | |
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| 2.842 | 1.75 | 416000 | 2.6465 | |
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| 2.842 | 1.79 | 424000 | 2.6434 | |
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| 2.8431 | 1.82 | 432000 | 2.6414 | |
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| 2.8431 | 1.85 | 440000 | 2.6532 | |
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| 2.8599 | 1.89 | 448000 | 2.6645 | |
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| 2.8599 | 1.92 | 456000 | 2.6651 | |
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| 2.8567 | 1.95 | 464000 | 2.6694 | |
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| 2.8567 | 1.99 | 472000 | 2.6610 | |
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| 2.8682 | 2.02 | 480000 | 2.6877 | |
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| 2.8682 | 2.05 | 488000 | 2.6724 | |
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| 2.8693 | 2.09 | 496000 | 2.6839 | |
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| 2.8693 | 2.12 | 504000 | 2.6923 | |
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| 2.8881 | 2.16 | 512000 | 2.6964 | |
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| 2.8881 | 2.19 | 520000 | 2.6982 | |
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| 2.8874 | 2.22 | 528000 | 2.6961 | |
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| 2.8874 | 2.26 | 536000 | 2.6884 | |
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| 2.8899 | 2.29 | 544000 | 2.7055 | |
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| 2.8899 | 2.32 | 552000 | 2.6988 | |
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| 2.8966 | 2.36 | 560000 | 2.7103 | |
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| 2.8966 | 2.39 | 568000 | 2.7100 | |
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| 2.9 | 2.43 | 576000 | 2.7169 | |
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| 2.9 | 2.46 | 584000 | 2.7180 | |
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| 2.9237 | 2.49 | 592000 | 2.7270 | |
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| 2.9237 | 2.53 | 600000 | 2.7265 | |
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| 2.9236 | 2.56 | 608000 | 2.7323 | |
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| 2.9236 | 2.59 | 616000 | 2.7350 | |
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| 2.9276 | 2.63 | 624000 | 2.7333 | |
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| 2.9276 | 2.66 | 632000 | 2.7345 | |
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| 2.9252 | 2.69 | 640000 | 2.7497 | |
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| 2.9252 | 2.73 | 648000 | 2.7428 | |
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| 2.9364 | 2.76 | 656000 | 2.7392 | |
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| 2.9364 | 2.8 | 664000 | 2.7505 | |
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| 2.9366 | 2.83 | 672000 | 2.7393 | |
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| 2.9366 | 2.86 | 680000 | 2.7372 | |
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| 2.9437 | 2.9 | 688000 | 2.7451 | |
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| 2.9437 | 2.93 | 696000 | 2.7488 | |
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| 2.9483 | 2.96 | 704000 | 2.7586 | |
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| 2.9483 | 3.0 | 712000 | 2.7613 | |
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| 2.9588 | 3.03 | 720000 | 2.7619 | |
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| 2.9588 | 3.07 | 728000 | 2.7680 | |
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| 2.9422 | 3.1 | 736000 | 2.7546 | |
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| 2.9422 | 3.13 | 744000 | 2.7629 | |
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| 2.965 | 3.17 | 752000 | 2.7595 | |
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| 2.965 | 3.2 | 760000 | 2.7763 | |
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| 2.959 | 3.23 | 768000 | 2.7739 | |
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| 2.959 | 3.27 | 776000 | 2.7839 | |
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| 2.9604 | 3.3 | 784000 | 2.7681 | |
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| 2.9604 | 3.33 | 792000 | 2.7816 | |
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| 2.9638 | 3.37 | 800000 | 2.7812 | |
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| 2.9638 | 3.4 | 808000 | 2.7846 | |
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| 2.9704 | 3.44 | 816000 | 2.7766 | |
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| 2.9704 | 3.47 | 824000 | 2.7869 | |
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| 2.9684 | 3.5 | 832000 | 2.7741 | |
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| 2.9684 | 3.54 | 840000 | 2.7735 | |
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| 2.9723 | 3.57 | 848000 | 2.7701 | |
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| 2.9723 | 3.6 | 856000 | 2.7780 | |
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| 2.9734 | 3.64 | 864000 | 2.7833 | |
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| 2.9734 | 3.67 | 872000 | 2.7910 | |
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| 2.9806 | 3.71 | 880000 | 2.7941 | |
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| 2.9806 | 3.74 | 888000 | 2.7997 | |
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| 2.9808 | 3.77 | 896000 | 2.8027 | |
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| 2.9808 | 3.81 | 904000 | 2.7972 | |
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| 3.0008 | 3.84 | 912000 | 2.8026 | |
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| 3.0008 | 3.87 | 920000 | 2.7975 | |
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| 2.9934 | 3.91 | 928000 | 2.7971 | |
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| 2.9934 | 3.94 | 936000 | 2.8030 | |
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| 2.9927 | 3.97 | 944000 | 2.8082 | |
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| 2.9927 | 4.01 | 952000 | 2.8208 | |
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| 3.0013 | 4.04 | 960000 | 2.8129 | |
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| 3.0013 | 4.08 | 968000 | 2.8236 | |
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| 2.9996 | 4.11 | 976000 | 2.8226 | |
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| 2.9996 | 4.14 | 984000 | 2.8273 | |
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| 3.0125 | 4.18 | 992000 | 2.8161 | |
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| 3.0125 | 4.21 | 1000000 | 2.8249 | |
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| 3.0086 | 4.24 | 1008000 | 2.8320 | |
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| 3.0086 | 4.28 | 1016000 | 2.8313 | |
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| 3.0077 | 4.31 | 1024000 | 2.8321 | |
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| 3.0077 | 4.35 | 1032000 | 2.8332 | |
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| 3.0186 | 4.38 | 1040000 | 2.8288 | |
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| 3.0186 | 4.41 | 1048000 | 2.8392 | |
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| 3.0311 | 4.45 | 1056000 | 2.8243 | |
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| 3.0311 | 4.48 | 1064000 | 2.8524 | |
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| 3.0199 | 4.51 | 1072000 | 2.8347 | |
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| 3.0199 | 4.55 | 1080000 | 2.8438 | |
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| 3.0198 | 4.58 | 1088000 | 2.8415 | |
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| 3.0198 | 4.61 | 1096000 | 2.8460 | |
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| 3.0279 | 4.65 | 1104000 | 2.8551 | |
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| 3.0279 | 4.68 | 1112000 | 2.8528 | |
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| 3.0319 | 4.72 | 1120000 | 2.8601 | |
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| 3.0319 | 4.75 | 1128000 | 2.8544 | |
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| 3.0371 | 4.78 | 1136000 | 2.8553 | |
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| 3.0371 | 4.82 | 1144000 | 2.8597 | |
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| 3.038 | 4.85 | 1152000 | 2.8653 | |
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| 3.038 | 4.88 | 1160000 | 2.8560 | |
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| 3.0318 | 4.92 | 1168000 | 2.8602 | |
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| 3.0318 | 4.95 | 1176000 | 2.8484 | |
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| 3.0449 | 4.99 | 1184000 | 2.8612 | |
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| 3.0449 | 5.02 | 1192000 | 2.8598 | |
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| 3.0384 | 5.05 | 1200000 | 2.8581 | |
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| 3.0384 | 5.09 | 1208000 | 2.8481 | |
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| 3.0243 | 5.12 | 1216000 | 2.8458 | |
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| 3.0243 | 5.15 | 1224000 | 2.8494 | |
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| 3.0345 | 5.19 | 1232000 | 2.8544 | |
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| 3.0345 | 5.22 | 1240000 | 2.8488 | |
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| 3.0251 | 5.25 | 1248000 | 2.8453 | |
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| 3.0251 | 5.29 | 1256000 | 2.8464 | |
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| 3.0234 | 5.32 | 1264000 | 2.8486 | |
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| 3.0234 | 5.36 | 1272000 | 2.8436 | |
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| 3.0205 | 5.39 | 1280000 | 2.8476 | |
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| 3.0205 | 5.42 | 1288000 | 2.8327 | |
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| 3.0228 | 5.46 | 1296000 | 2.8452 | |
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| 3.0228 | 5.49 | 1304000 | 2.8372 | |
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| 3.0063 | 5.52 | 1312000 | 2.8306 | |
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| 3.0063 | 5.56 | 1320000 | 2.8411 | |
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| 3.0068 | 5.59 | 1328000 | 2.8273 | |
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| 3.0068 | 5.63 | 1336000 | 2.8343 | |
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| 3.0109 | 5.66 | 1344000 | 2.8328 | |
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| 3.0109 | 5.69 | 1352000 | 2.8431 | |
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| 3.0068 | 5.73 | 1360000 | 2.8332 | |
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| 3.0068 | 5.76 | 1368000 | 2.8275 | |
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| 3.002 | 5.79 | 1376000 | 2.8314 | |
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| 3.002 | 5.83 | 1384000 | 2.8324 | |
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| 3.0037 | 5.86 | 1392000 | 2.8394 | |
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| 3.0037 | 5.89 | 1400000 | 2.8338 | |
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| 3.0086 | 5.93 | 1408000 | 2.8448 | |
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| 3.0086 | 5.96 | 1416000 | 2.8326 | |
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| 2.9977 | 6.0 | 1424000 | 2.8311 | |
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| 2.9977 | 6.03 | 1432000 | 2.8410 | |
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| 2.9984 | 6.06 | 1440000 | 2.8359 | |
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| 2.9984 | 6.1 | 1448000 | 2.8393 | |
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| 3.0095 | 6.13 | 1456000 | 2.8388 | |
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| 3.0095 | 6.16 | 1464000 | 2.8448 | |
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| 3.0051 | 6.2 | 1472000 | 2.8472 | |
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| 3.0051 | 6.23 | 1480000 | 2.8421 | |
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| 3.0142 | 6.27 | 1488000 | 2.8424 | |
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| 3.0142 | 6.3 | 1496000 | 2.8477 | |
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| 3.0149 | 6.33 | 1504000 | 2.8428 | |
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| 3.0149 | 6.37 | 1512000 | 2.8529 | |
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| 3.0147 | 6.4 | 1520000 | 2.8541 | |
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| 3.0147 | 6.43 | 1528000 | 2.8519 | |
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| 3.0205 | 6.47 | 1536000 | 2.8527 | |
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| 3.0205 | 6.5 | 1544000 | 2.8471 | |
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| 3.029 | 6.53 | 1552000 | 2.8583 | |
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| 3.029 | 6.57 | 1560000 | 2.8497 | |
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| 3.024 | 6.6 | 1568000 | 2.8653 | |
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| 3.024 | 6.64 | 1576000 | 2.8553 | |
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| 3.0371 | 6.67 | 1584000 | 2.8653 | |
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| 3.0371 | 6.7 | 1592000 | 2.8604 | |
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| 3.0319 | 6.74 | 1600000 | 2.8624 | |
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| 3.0319 | 6.77 | 1608000 | 2.8657 | |
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| 3.0369 | 6.8 | 1616000 | 2.8616 | |
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| 3.0369 | 6.84 | 1624000 | 2.8667 | |
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| 3.0357 | 6.87 | 1632000 | 2.8660 | |
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| 3.0357 | 6.91 | 1640000 | 2.8682 | |
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| 3.0342 | 6.94 | 1648000 | 2.8676 | |
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| 3.0342 | 6.97 | 1656000 | 2.8815 | |
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| 3.0375 | 7.01 | 1664000 | 2.8667 | |
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| 3.0375 | 7.04 | 1672000 | 2.8735 | |
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| 3.0419 | 7.07 | 1680000 | 2.8788 | |
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| 3.0419 | 7.11 | 1688000 | 2.8767 | |
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| 3.0403 | 7.14 | 1696000 | 2.8812 | |
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| 3.0403 | 7.17 | 1704000 | 2.8795 | |
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| 3.0482 | 7.21 | 1712000 | 2.8805 | |
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| 3.0482 | 7.24 | 1720000 | 2.8794 | |
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| 3.0533 | 7.28 | 1728000 | 2.8788 | |
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| 3.0533 | 7.31 | 1736000 | 2.8844 | |
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| 3.0453 | 7.34 | 1744000 | 2.8709 | |
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| 3.0453 | 7.38 | 1752000 | 2.8835 | |
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| 3.0562 | 7.41 | 1760000 | 2.8891 | |
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| 3.0562 | 7.44 | 1768000 | 2.8903 | |
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| 3.0617 | 7.48 | 1776000 | 2.8849 | |
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| 3.0617 | 7.51 | 1784000 | 2.8766 | |
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| 3.0539 | 7.55 | 1792000 | 2.8872 | |
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| 3.0539 | 7.58 | 1800000 | 2.8981 | |
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| 3.0561 | 7.61 | 1808000 | 2.8862 | |
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| 3.0561 | 7.65 | 1816000 | 2.8940 | |
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| 3.0529 | 7.68 | 1824000 | 2.8874 | |
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| 3.0529 | 7.71 | 1832000 | 2.8839 | |
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| 3.0484 | 7.75 | 1840000 | 2.8838 | |
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| 3.0484 | 7.78 | 1848000 | 2.8856 | |
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| 3.0562 | 7.81 | 1856000 | 2.8984 | |
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| 3.0562 | 7.85 | 1864000 | 2.8844 | |
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| 3.0578 | 7.88 | 1872000 | 2.8874 | |
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| 3.0578 | 7.92 | 1880000 | 2.8887 | |
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| 3.0553 | 7.95 | 1888000 | 2.8798 | |
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| 3.0553 | 7.98 | 1896000 | 2.8789 | |
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| 3.0623 | 8.02 | 1904000 | 2.8968 | |
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| 3.0623 | 8.05 | 1912000 | 2.8834 | |
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| 3.0652 | 8.08 | 1920000 | 2.8902 | |
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| 3.0652 | 8.12 | 1928000 | 2.8822 | |
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| 3.0487 | 8.15 | 1936000 | 2.8844 | |
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| 3.0487 | 8.19 | 1944000 | 2.8909 | |
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| 3.0546 | 8.22 | 1952000 | 2.8915 | |
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| 3.0546 | 8.25 | 1960000 | 2.8870 | |
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| 3.0524 | 8.29 | 1968000 | 2.8828 | |
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| 3.0524 | 8.32 | 1976000 | 2.8781 | |
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| 3.0491 | 8.35 | 1984000 | 2.8948 | |
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| 3.0491 | 8.39 | 1992000 | 2.8904 | |
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| 3.0534 | 8.42 | 2000000 | 2.8839 | |
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| 3.0534 | 8.45 | 2008000 | 2.8918 | |
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| 3.0547 | 8.49 | 2016000 | 2.8739 | |
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| 3.0547 | 8.52 | 2024000 | 2.8684 | |
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| 3.0544 | 8.56 | 2032000 | 2.8740 | |
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| 3.0544 | 8.59 | 2040000 | 2.8784 | |
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| 3.0448 | 8.62 | 2048000 | 2.8758 | |
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| 3.0448 | 8.66 | 2056000 | 2.8801 | |
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| 3.0499 | 8.69 | 2064000 | 2.8793 | |
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| 3.0499 | 8.72 | 2072000 | 2.8707 | |
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| 3.0368 | 8.76 | 2080000 | 2.8722 | |
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| 3.0368 | 8.79 | 2088000 | 2.8752 | |
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| 3.0548 | 8.83 | 2096000 | 2.8880 | |
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| 3.0548 | 8.86 | 2104000 | 2.8781 | |
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| 3.0457 | 8.89 | 2112000 | 2.8825 | |
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| 3.0457 | 8.93 | 2120000 | 2.8827 | |
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| 3.0377 | 8.96 | 2128000 | 2.8810 | |
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| 3.0377 | 8.99 | 2136000 | 2.8727 | |
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| 3.0341 | 9.03 | 2144000 | 2.8750 | |
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| 3.0341 | 9.06 | 2152000 | 2.8638 | |
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| 3.0275 | 9.09 | 2160000 | 2.8690 | |
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| 3.0275 | 9.13 | 2168000 | 2.8660 | |
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| 3.0413 | 9.16 | 2176000 | 2.8578 | |
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| 3.0413 | 9.2 | 2184000 | 2.8692 | |
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| 3.0272 | 9.23 | 2192000 | 2.8702 | |
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| 3.0272 | 9.26 | 2200000 | 2.8707 | |
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| 3.034 | 9.3 | 2208000 | 2.8666 | |
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| 3.034 | 9.33 | 2216000 | 2.8734 | |
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| 3.0346 | 9.36 | 2224000 | 2.8685 | |
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| 3.0346 | 9.4 | 2232000 | 2.8675 | |
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| 3.0234 | 9.43 | 2240000 | 2.8662 | |
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| 3.0234 | 9.47 | 2248000 | 2.8670 | |
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| 3.0256 | 9.5 | 2256000 | 2.8764 | |
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| 3.0256 | 9.53 | 2264000 | 2.8664 | |
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| 3.0232 | 9.57 | 2272000 | 2.8625 | |
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| 3.0232 | 9.6 | 2280000 | 2.8647 | |
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| 3.0309 | 9.63 | 2288000 | 2.8561 | |
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| 3.0309 | 9.67 | 2296000 | 2.8657 | |
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| 3.0254 | 9.7 | 2304000 | 2.8667 | |
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| 3.0254 | 9.73 | 2312000 | 2.8618 | |
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| 3.0198 | 9.77 | 2320000 | 2.8650 | |
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| 3.0198 | 9.8 | 2328000 | 2.8630 | |
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| 3.0109 | 9.84 | 2336000 | 2.8533 | |
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| 3.0109 | 9.87 | 2344000 | 2.8656 | |
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| 3.0316 | 9.9 | 2352000 | 2.8607 | |
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| 3.0316 | 9.94 | 2360000 | 2.8572 | |
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| 3.0225 | 9.97 | 2368000 | 2.8617 | |
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| 3.0225 | 10.0 | 2376000 | 2.8604 | |
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| 3.0132 | 10.04 | 2384000 | 2.8577 | |
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| 3.0132 | 10.07 | 2392000 | 2.8535 | |
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| 3.0202 | 10.11 | 2400000 | 2.8566 | |
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### Framework versions |
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- Transformers 4.35.0.dev0 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.14.5 |
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- Tokenizers 0.14.0 |
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