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2020-Q3-90p-filtered-random

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roberta
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@@ -4,18 +4,18 @@ 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-Q3-90p-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-Q3-90p-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: 3.7327
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  ## Model description
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@@ -46,306 +46,306 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss |
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  |:-------------:|:-----:|:-------:|:---------------:|
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- | No log | 0.17 | 8000 | 4.1206 |
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- | 4.294 | 0.34 | 16000 | 4.0057 |
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- | 4.294 | 0.51 | 24000 | 3.9651 |
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- | 4.0928 | 0.68 | 32000 | 3.8968 |
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- | 4.0928 | 0.85 | 40000 | 3.8837 |
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- | 4.0249 | 1.01 | 48000 | 3.8693 |
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- | 4.0249 | 1.18 | 56000 | 3.8535 |
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- | 3.9984 | 1.35 | 64000 | 3.8320 |
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- | 3.9984 | 1.52 | 72000 | 3.8221 |
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- | 3.9768 | 1.69 | 80000 | 3.8312 |
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- | 3.9768 | 1.86 | 88000 | 3.8663 |
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- | 3.9664 | 2.03 | 96000 | 3.8159 |
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- | 3.9664 | 2.2 | 104000 | 3.8216 |
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- | 3.9582 | 2.37 | 112000 | 3.8313 |
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- | 3.9582 | 2.54 | 120000 | 3.8215 |
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- | 3.9513 | 2.7 | 128000 | 3.7974 |
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- | 3.9513 | 2.87 | 136000 | 3.8077 |
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- | 3.9604 | 3.04 | 144000 | 3.7820 |
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- | 3.9604 | 3.21 | 152000 | 3.7946 |
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- | 3.9564 | 3.38 | 160000 | 3.8025 |
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- | 3.9564 | 3.55 | 168000 | 3.8236 |
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- | 3.9533 | 3.72 | 176000 | 3.8194 |
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- | 3.9533 | 3.89 | 184000 | 3.7996 |
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- | 3.9554 | 4.06 | 192000 | 3.7775 |
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- | 3.9554 | 4.23 | 200000 | 3.7879 |
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- | 3.9471 | 4.4 | 208000 | 3.8141 |
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- | 3.9471 | 4.56 | 216000 | 3.8025 |
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- | 3.9478 | 4.73 | 224000 | 3.7839 |
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- | 3.9478 | 4.9 | 232000 | 3.8026 |
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- | 3.9336 | 5.07 | 240000 | 3.8102 |
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- | 3.9336 | 5.24 | 248000 | 3.7985 |
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- | 3.9384 | 5.41 | 256000 | 3.8082 |
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- | 3.9384 | 5.58 | 264000 | 3.7966 |
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- | 3.9467 | 5.75 | 272000 | 3.7826 |
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- | 3.9467 | 5.92 | 280000 | 3.7730 |
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- | 3.936 | 6.09 | 288000 | 3.7910 |
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- | 3.936 | 6.26 | 296000 | 3.7904 |
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- | 3.9438 | 6.42 | 304000 | 3.7918 |
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- | 3.9438 | 6.59 | 312000 | 3.7773 |
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- | 3.9386 | 6.76 | 320000 | 3.7665 |
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- | 3.9386 | 6.93 | 328000 | 3.7803 |
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- | 3.9365 | 7.1 | 336000 | 3.7851 |
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- | 3.9365 | 7.27 | 344000 | 3.7787 |
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- | 3.946 | 7.44 | 352000 | 3.7683 |
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- | 3.946 | 7.61 | 360000 | 3.7781 |
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- | 3.9323 | 7.78 | 368000 | 3.7725 |
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- | 3.9323 | 7.95 | 376000 | 3.7714 |
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- | 3.9355 | 8.11 | 384000 | 3.7898 |
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- | 3.9355 | 8.28 | 392000 | 3.7781 |
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- | 3.9296 | 8.45 | 400000 | 3.7859 |
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- | 3.9296 | 8.62 | 408000 | 3.7685 |
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- | 3.9272 | 8.79 | 416000 | 3.7745 |
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- | 3.9272 | 8.96 | 424000 | 3.7547 |
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- | 3.9377 | 9.13 | 432000 | 3.7588 |
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- | 3.9377 | 9.3 | 440000 | 3.7635 |
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- | 3.9279 | 9.47 | 448000 | 3.7588 |
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- | 3.9279 | 9.64 | 456000 | 3.7531 |
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- | 3.9267 | 9.81 | 464000 | 3.7778 |
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- | 3.9267 | 9.97 | 472000 | 3.7847 |
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- | 3.9168 | 10.14 | 480000 | 3.7633 |
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- | 3.9168 | 10.31 | 488000 | 3.7564 |
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- | 3.9304 | 10.48 | 496000 | 3.7547 |
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- | 3.9304 | 10.65 | 504000 | 3.7544 |
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- | 3.9218 | 10.82 | 512000 | 3.7564 |
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- | 3.9218 | 10.99 | 520000 | 3.7775 |
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- | 3.9247 | 11.16 | 528000 | 3.7621 |
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- | 3.9247 | 11.33 | 536000 | 3.7585 |
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- | 3.9148 | 11.5 | 544000 | 3.7779 |
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- | 3.9148 | 11.66 | 552000 | 3.7580 |
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- | 3.9224 | 11.83 | 560000 | 3.7564 |
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- | 3.9224 | 12.0 | 568000 | 3.7553 |
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- | 3.9196 | 12.17 | 576000 | 3.7440 |
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- | 3.9196 | 12.34 | 584000 | 3.7556 |
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- | 3.9228 | 12.51 | 592000 | 3.7632 |
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- | 3.9228 | 12.68 | 600000 | 3.7566 |
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- | 3.926 | 12.85 | 608000 | 3.7429 |
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- | 3.926 | 13.02 | 616000 | 3.7447 |
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- | 3.922 | 13.19 | 624000 | 3.7573 |
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- | 3.922 | 13.36 | 632000 | 3.7698 |
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- | 3.9269 | 13.52 | 640000 | 3.7592 |
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- | 3.9269 | 13.69 | 648000 | 3.7665 |
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- | 3.918 | 13.86 | 656000 | 3.7588 |
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- | 3.918 | 14.03 | 664000 | 3.7394 |
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- | 3.9173 | 14.2 | 672000 | 3.7605 |
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- | 3.9173 | 14.37 | 680000 | 3.7484 |
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- | 3.9115 | 14.54 | 688000 | 3.7686 |
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- | 3.9115 | 14.71 | 696000 | 3.7632 |
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- | 3.9111 | 14.88 | 704000 | 3.7682 |
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- | 3.9111 | 15.05 | 712000 | 3.7497 |
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- | 3.9131 | 15.21 | 720000 | 3.7470 |
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- | 3.9131 | 15.38 | 728000 | 3.7567 |
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- | 3.9211 | 15.55 | 736000 | 3.7546 |
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- | 3.9211 | 15.72 | 744000 | 3.7390 |
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- | 3.9059 | 15.89 | 752000 | 3.7530 |
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- | 3.9059 | 16.06 | 760000 | 3.7728 |
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- | 3.9051 | 16.23 | 768000 | 3.7345 |
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- | 3.9051 | 16.4 | 776000 | 3.7468 |
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- | 3.9103 | 16.57 | 784000 | 3.7357 |
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- | 3.9103 | 16.74 | 792000 | 3.7434 |
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- | 3.9058 | 16.91 | 800000 | 3.7456 |
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- | 3.9058 | 17.07 | 808000 | 3.7634 |
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- | 3.9076 | 17.24 | 816000 | 3.7374 |
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- | 3.9076 | 17.41 | 824000 | 3.7534 |
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- | 3.9071 | 17.58 | 832000 | 3.7576 |
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- | 3.9071 | 17.75 | 840000 | 3.7682 |
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- | 3.8976 | 17.92 | 848000 | 3.7599 |
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- | 3.8976 | 18.09 | 856000 | 3.7436 |
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- | 3.9071 | 18.26 | 864000 | 3.7564 |
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- | 3.9071 | 18.43 | 872000 | 3.7284 |
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- | 3.9067 | 18.6 | 880000 | 3.7458 |
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- | 3.9067 | 18.77 | 888000 | 3.7448 |
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- | 3.914 | 18.93 | 896000 | 3.7490 |
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- | 3.914 | 19.1 | 904000 | 3.7290 |
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- | 3.9086 | 19.27 | 912000 | 3.7238 |
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- | 3.9086 | 19.44 | 920000 | 3.7402 |
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- | 3.9115 | 19.61 | 928000 | 3.7399 |
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- | 3.9115 | 19.78 | 936000 | 3.7504 |
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- | 3.905 | 19.95 | 944000 | 3.7206 |
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- | 3.905 | 20.12 | 952000 | 3.7351 |
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- | 3.9012 | 20.29 | 960000 | 3.7409 |
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- | 3.9012 | 20.46 | 968000 | 3.7426 |
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- | 3.9043 | 20.62 | 976000 | 3.7349 |
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- | 3.9043 | 20.79 | 984000 | 3.7376 |
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- | 3.9104 | 20.96 | 992000 | 3.7325 |
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- | 3.9104 | 21.13 | 1000000 | 3.7488 |
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- | 3.9025 | 21.3 | 1008000 | 3.7192 |
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- | 3.9025 | 21.47 | 1016000 | 3.7437 |
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- | 3.9131 | 21.64 | 1024000 | 3.7483 |
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- | 3.9131 | 21.81 | 1032000 | 3.7290 |
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- | 3.9048 | 21.98 | 1040000 | 3.7547 |
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- | 3.9048 | 22.15 | 1048000 | 3.7387 |
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- | 3.9011 | 22.32 | 1056000 | 3.7373 |
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- | 3.9011 | 22.48 | 1064000 | 3.7222 |
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- | 3.8988 | 22.65 | 1072000 | 3.7362 |
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- | 3.8988 | 22.82 | 1080000 | 3.7068 |
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- | 3.9022 | 22.99 | 1088000 | 3.7358 |
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- | 3.9022 | 23.16 | 1096000 | 3.7317 |
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- | 3.8916 | 23.33 | 1104000 | 3.7264 |
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- | 3.8916 | 23.5 | 1112000 | 3.7487 |
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- | 3.9079 | 23.67 | 1120000 | 3.7486 |
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- | 3.9079 | 23.84 | 1128000 | 3.7403 |
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- | 3.9037 | 24.01 | 1136000 | 3.7345 |
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- | 3.9037 | 24.17 | 1144000 | 3.7221 |
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- | 3.8932 | 24.34 | 1152000 | 3.7286 |
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- | 3.8932 | 24.51 | 1160000 | 3.7309 |
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- | 3.9055 | 24.68 | 1168000 | 3.7464 |
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- | 3.9055 | 24.85 | 1176000 | 3.7289 |
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- | 3.901 | 25.02 | 1184000 | 3.7245 |
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- | 3.901 | 25.19 | 1192000 | 3.7169 |
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- | 3.8934 | 25.36 | 1200000 | 3.7176 |
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- | 3.8934 | 25.53 | 1208000 | 3.7250 |
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- | 3.9063 | 25.7 | 1216000 | 3.7347 |
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- | 3.9063 | 25.87 | 1224000 | 3.7445 |
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- | 3.8937 | 26.03 | 1232000 | 3.7318 |
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- | 3.8937 | 26.2 | 1240000 | 3.7202 |
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- | 3.9056 | 26.37 | 1248000 | 3.7427 |
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- | 3.9056 | 26.54 | 1256000 | 3.7317 |
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- | 3.8966 | 26.71 | 1264000 | 3.7210 |
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- | 3.8966 | 26.88 | 1272000 | 3.7238 |
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- | 3.8888 | 27.05 | 1280000 | 3.7164 |
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- | 3.8888 | 27.22 | 1288000 | 3.7068 |
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- | 3.8924 | 27.39 | 1296000 | 3.7498 |
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- | 3.8924 | 27.56 | 1304000 | 3.7183 |
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- | 3.8992 | 27.72 | 1312000 | 3.7284 |
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- | 3.8992 | 27.89 | 1320000 | 3.7397 |
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- | 3.891 | 28.06 | 1328000 | 3.7212 |
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- | 3.891 | 28.23 | 1336000 | 3.7249 |
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- | 3.8947 | 28.4 | 1344000 | 3.7143 |
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- | 3.8947 | 28.57 | 1352000 | 3.7273 |
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- | 3.8938 | 28.74 | 1360000 | 3.7317 |
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- | 3.8938 | 28.91 | 1368000 | 3.7182 |
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- | 3.8848 | 29.08 | 1376000 | 3.7199 |
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- | 3.8848 | 29.25 | 1384000 | 3.7309 |
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- | 3.8907 | 29.42 | 1392000 | 3.7221 |
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- | 3.8907 | 29.58 | 1400000 | 3.7299 |
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- | 3.8956 | 29.75 | 1408000 | 3.7257 |
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- | 3.8956 | 29.92 | 1416000 | 3.7313 |
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- | 3.8915 | 30.09 | 1424000 | 3.7374 |
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- | 3.8915 | 30.26 | 1432000 | 3.7146 |
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- | 3.901 | 30.43 | 1440000 | 3.7190 |
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- | 3.901 | 30.6 | 1448000 | 3.7211 |
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- | 3.8901 | 30.77 | 1456000 | 3.7215 |
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- | 3.8901 | 30.94 | 1464000 | 3.7189 |
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- | 3.89 | 31.11 | 1472000 | 3.6938 |
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- | 3.89 | 31.28 | 1480000 | 3.7315 |
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- | 3.8981 | 31.44 | 1488000 | 3.7328 |
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- | 3.8981 | 31.61 | 1496000 | 3.6945 |
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- | 3.8866 | 31.78 | 1504000 | 3.7125 |
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- | 3.8866 | 31.95 | 1512000 | 3.7229 |
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- | 3.8943 | 32.12 | 1520000 | 3.7248 |
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- | 3.8943 | 32.29 | 1528000 | 3.7076 |
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- | 3.8978 | 32.46 | 1536000 | 3.7276 |
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- | 3.8978 | 32.63 | 1544000 | 3.7146 |
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- | 3.8917 | 32.8 | 1552000 | 3.7323 |
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- | 3.8917 | 32.97 | 1560000 | 3.7308 |
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- | 3.902 | 33.13 | 1568000 | 3.7290 |
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- | 3.902 | 33.3 | 1576000 | 3.7153 |
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- | 3.8942 | 33.47 | 1584000 | 3.7271 |
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- | 3.8942 | 33.64 | 1592000 | 3.7061 |
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- | 3.885 | 33.81 | 1600000 | 3.7335 |
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- | 3.885 | 33.98 | 1608000 | 3.7009 |
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- | 3.8916 | 34.15 | 1616000 | 3.7184 |
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- | 3.8916 | 34.32 | 1624000 | 3.7282 |
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- | 3.8941 | 34.49 | 1632000 | 3.7347 |
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- | 3.8941 | 34.66 | 1640000 | 3.7183 |
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- | 3.8923 | 34.83 | 1648000 | 3.7179 |
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- | 3.8923 | 34.99 | 1656000 | 3.7358 |
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- | 3.8959 | 35.16 | 1664000 | 3.7375 |
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- | 3.8959 | 35.33 | 1672000 | 3.7214 |
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- | 3.8864 | 35.5 | 1680000 | 3.6894 |
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- | 3.8864 | 35.67 | 1688000 | 3.7273 |
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- | 3.8882 | 35.84 | 1696000 | 3.7326 |
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- | 3.8882 | 36.01 | 1704000 | 3.7454 |
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- | 3.8897 | 36.18 | 1712000 | 3.7255 |
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- | 3.8897 | 36.35 | 1720000 | 3.7154 |
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- | 3.8846 | 36.52 | 1728000 | 3.7161 |
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- | 3.8846 | 36.68 | 1736000 | 3.7116 |
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- | 3.8768 | 36.85 | 1744000 | 3.6959 |
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- | 3.8768 | 37.02 | 1752000 | 3.7220 |
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- | 3.8972 | 37.19 | 1760000 | 3.7378 |
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- | 3.8972 | 37.36 | 1768000 | 3.7325 |
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- | 3.8849 | 37.53 | 1776000 | 3.7259 |
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- | 3.8849 | 37.7 | 1784000 | 3.7256 |
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- | 3.892 | 37.87 | 1792000 | 3.7198 |
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- | 3.892 | 38.04 | 1800000 | 3.7202 |
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- | 3.8947 | 38.21 | 1808000 | 3.7185 |
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- | 3.8947 | 38.38 | 1816000 | 3.7097 |
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- | 3.8904 | 38.54 | 1824000 | 3.7317 |
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- | 3.8904 | 38.71 | 1832000 | 3.7153 |
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- | 3.8884 | 38.88 | 1840000 | 3.7399 |
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- | 3.8884 | 39.05 | 1848000 | 3.7260 |
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- | 3.8815 | 39.22 | 1856000 | 3.7156 |
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- | 3.8815 | 39.39 | 1864000 | 3.7196 |
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- | 3.8915 | 39.56 | 1872000 | 3.7313 |
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- | 3.8915 | 39.73 | 1880000 | 3.7223 |
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- | 3.8795 | 39.9 | 1888000 | 3.7163 |
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- | 3.8795 | 40.07 | 1896000 | 3.7089 |
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- | 3.8865 | 40.23 | 1904000 | 3.7227 |
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- | 3.8865 | 40.4 | 1912000 | 3.7250 |
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- | 3.8987 | 40.57 | 1920000 | 3.6921 |
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- | 3.8987 | 40.74 | 1928000 | 3.7254 |
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- | 3.8826 | 40.91 | 1936000 | 3.7100 |
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- | 3.8826 | 41.08 | 1944000 | 3.7356 |
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- | 3.8911 | 41.25 | 1952000 | 3.7179 |
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- | 3.8911 | 41.42 | 1960000 | 3.7277 |
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- | 3.8752 | 41.59 | 1968000 | 3.7068 |
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- | 3.8752 | 41.76 | 1976000 | 3.7191 |
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- | 3.8818 | 41.93 | 1984000 | 3.7262 |
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- | 3.8818 | 42.09 | 1992000 | 3.7295 |
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- | 3.8774 | 42.26 | 2000000 | 3.7066 |
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- | 3.8774 | 42.43 | 2008000 | 3.6993 |
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- | 3.883 | 42.6 | 2016000 | 3.7086 |
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- | 3.883 | 42.77 | 2024000 | 3.7209 |
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- | 3.8885 | 42.94 | 2032000 | 3.7046 |
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- | 3.8885 | 43.11 | 2040000 | 3.6927 |
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- | 3.8909 | 43.28 | 2048000 | 3.7372 |
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- | 3.8909 | 43.45 | 2056000 | 3.7064 |
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- | 3.8904 | 43.62 | 2064000 | 3.7271 |
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- | 3.8904 | 43.79 | 2072000 | 3.7223 |
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- | 3.8877 | 43.95 | 2080000 | 3.7141 |
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- | 3.8877 | 44.12 | 2088000 | 3.6969 |
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- | 3.8886 | 44.29 | 2096000 | 3.7125 |
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- | 3.8886 | 44.46 | 2104000 | 3.7268 |
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- | 3.8832 | 44.63 | 2112000 | 3.7046 |
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- | 3.8832 | 44.8 | 2120000 | 3.7419 |
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- | 3.8812 | 44.97 | 2128000 | 3.7163 |
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- | 3.8812 | 45.14 | 2136000 | 3.7046 |
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- | 3.8852 | 45.31 | 2144000 | 3.7006 |
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- | 3.8852 | 45.48 | 2152000 | 3.7212 |
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- | 3.8864 | 45.64 | 2160000 | 3.7030 |
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- | 3.8864 | 45.81 | 2168000 | 3.7296 |
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- | 3.8867 | 45.98 | 2176000 | 3.7194 |
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- | 3.8867 | 46.15 | 2184000 | 3.7131 |
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- | 3.8856 | 46.32 | 2192000 | 3.7299 |
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- | 3.8856 | 46.49 | 2200000 | 3.6991 |
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- | 3.8843 | 46.66 | 2208000 | 3.7246 |
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- | 3.8843 | 46.83 | 2216000 | 3.7112 |
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- | 3.8923 | 47.0 | 2224000 | 3.7293 |
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- | 3.8923 | 47.17 | 2232000 | 3.7117 |
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- | 3.8905 | 47.34 | 2240000 | 3.7194 |
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- | 3.8905 | 47.5 | 2248000 | 3.6994 |
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- | 3.8818 | 47.67 | 2256000 | 3.7122 |
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- | 3.8818 | 47.84 | 2264000 | 3.7011 |
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- | 3.888 | 48.01 | 2272000 | 3.7052 |
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- | 3.888 | 48.18 | 2280000 | 3.7164 |
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- | 3.8801 | 48.35 | 2288000 | 3.7082 |
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- | 3.8801 | 48.52 | 2296000 | 3.7080 |
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- | 3.8818 | 48.69 | 2304000 | 3.7073 |
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- | 3.8818 | 48.86 | 2312000 | 3.7132 |
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- | 3.8889 | 49.03 | 2320000 | 3.7126 |
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- | 3.8889 | 49.19 | 2328000 | 3.7306 |
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- | 3.8868 | 49.36 | 2336000 | 3.7130 |
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- | 3.8868 | 49.53 | 2344000 | 3.7146 |
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- | 3.8849 | 49.7 | 2352000 | 3.7087 |
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- | 3.8849 | 49.87 | 2360000 | 3.7074 |
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- | 3.8807 | 50.04 | 2368000 | 3.7131 |
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- | 3.8807 | 50.21 | 2376000 | 3.7047 |
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- | 3.8847 | 50.38 | 2384000 | 3.7149 |
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- | 3.8847 | 50.55 | 2392000 | 3.7103 |
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- | 3.8927 | 50.72 | 2400000 | 3.7306 |
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350
 
351
  ### Framework versions
 
4
  tags:
5
  - generated_from_trainer
6
  model-index:
7
+ - name: 2020-Q3-90p-filtered-random
8
  results: []
9
  ---
10
 
11
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
12
  should probably proofread and complete it, then remove this comment. -->
13
 
14
+ # 2020-Q3-90p-filtered-random
15
 
16
  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.
17
  It achieves the following results on the evaluation set:
18
+ - Loss: 2.2642
19
 
20
  ## Model description
21
 
 
46
 
47
  | Training Loss | Epoch | Step | Validation Loss |
48
  |:-------------:|:-----:|:-------:|:---------------:|
49
+ | No log | 0.17 | 8000 | 2.5349 |
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+ | 2.7955 | 0.34 | 16000 | 2.4448 |
51
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53
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54
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55
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56
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57
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58
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59
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66
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68
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69
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70
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80
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81
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82
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84
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100
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101
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102
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103
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104
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105
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108
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112
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113
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114
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116
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117
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118
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119
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120
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121
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122
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123
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124
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125
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127
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128
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129
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131
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133
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134
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135
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136
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138
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140
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143
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144
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145
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146
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147
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148
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149
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150
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151
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152
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153
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154
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155
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156
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157
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158
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159
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160
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162
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163
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164
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165
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166
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167
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168
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169
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170
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171
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172
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173
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174
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179
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180
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184
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187
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188
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189
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190
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191
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192
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194
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195
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196
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198
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199
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200
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201
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202
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203
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204
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205
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209
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210
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211
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214
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215
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216
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217
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218
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219
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220
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221
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222
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223
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224
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225
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226
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229
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230
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231
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232
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233
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234
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236
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238
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240
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241
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244
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245
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248
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249
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250
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251
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254
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255
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256
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257
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258
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259
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260
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261
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262
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263
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264
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265
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266
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267
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268
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269
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270
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271
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272
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273
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274
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275
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277
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278
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279
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280
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283
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284
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285
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286
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287
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288
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289
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290
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291
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292
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293
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295
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296
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297
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298
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299
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300
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301
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303
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305
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309
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310
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311
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312
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313
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314
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315
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316
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317
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318
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319
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320
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321
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322
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323
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324
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325
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326
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327
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328
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329
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330
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331
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332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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343
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344
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345
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346
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347
+ | 2.39 | 50.36 | 2392000 | 2.2460 |
348
+ | 2.3937 | 50.53 | 2400000 | 2.2642 |
349
 
350
 
351
  ### Framework versions
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