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lora-roberta-large-no-ed

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@@ -19,34 +19,34 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6577
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- - Accuracy: 0.7631
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- - Prec: 0.6548
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- - Recall: 0.6054
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- - F1: 0.6277
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- - B Acc: 0.6054
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- - Micro F1: 0.7631
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- - Prec Joy: 0.7442
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- - Recall Joy: 0.7318
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- - F1 Joy: 0.7379
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- - Prec Anger: 0.6340
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- - Recall Anger: 0.6007
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- - F1 Anger: 0.6169
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- - Prec Disgust: 0.4641
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- - Recall Disgust: 0.4059
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- - F1 Disgust: 0.4330
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- - Prec Fear: 0.6923
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- - Recall Fear: 0.5930
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- - F1 Fear: 0.6388
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- - Prec Neutral: 0.8246
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- - Recall Neutral: 0.8811
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- - F1 Neutral: 0.8519
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- - Prec Sadness: 0.7164
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- - Recall Sadness: 0.6264
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- - F1 Sadness: 0.6684
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- - Prec Surprise: 0.5081
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- - Recall Surprise: 0.3990
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- - F1 Surprise: 0.4470
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  ## Model description
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@@ -67,38 +67,39 @@ More information needed
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  The following hyperparameters were used during training:
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  - learning_rate: 0.001
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  - train_batch_size: 32
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- - eval_batch_size: 16
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  - seed: 42
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  - gradient_accumulation_steps: 4
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  - total_train_batch_size: 128
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.05
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- - num_epochs: 15.0
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Prec | Recall | F1 | B Acc | Micro F1 | Prec Joy | Recall Joy | F1 Joy | Prec Anger | Recall Anger | F1 Anger | Prec Disgust | Recall Disgust | F1 Disgust | Prec Fear | Recall Fear | F1 Fear | Prec Neutral | Recall Neutral | F1 Neutral | Prec Sadness | Recall Sadness | F1 Sadness | Prec Surprise | Recall Surprise | F1 Surprise |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:|
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- | 0.8081 | 0.75 | 1099 | 0.7901 | 0.7138 | 0.5617 | 0.5642 | 0.5601 | 0.5642 | 0.7138 | 0.7312 | 0.6492 | 0.6878 | 0.4974 | 0.5354 | 0.5157 | 0.4330 | 0.3515 | 0.3880 | 0.5277 | 0.5447 | 0.5360 | 0.8403 | 0.8288 | 0.8345 | 0.5152 | 0.6820 | 0.5870 | 0.3867 | 0.3581 | 0.3718 |
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- | 0.7543 | 1.5 | 2198 | 0.7482 | 0.7263 | 0.5892 | 0.5714 | 0.5737 | 0.5714 | 0.7263 | 0.6611 | 0.7786 | 0.7151 | 0.578 | 0.4795 | 0.5242 | 0.4229 | 0.4644 | 0.4427 | 0.5082 | 0.5900 | 0.5461 | 0.8353 | 0.8242 | 0.8297 | 0.6356 | 0.5999 | 0.6172 | 0.4836 | 0.2634 | 0.3411 |
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- | 0.7292 | 2.25 | 3297 | 0.7176 | 0.7392 | 0.6337 | 0.5729 | 0.5834 | 0.5729 | 0.7392 | 0.7077 | 0.7367 | 0.7219 | 0.6069 | 0.4928 | 0.5440 | 0.3188 | 0.5816 | 0.4119 | 0.6310 | 0.5608 | 0.5938 | 0.8031 | 0.8778 | 0.8388 | 0.8176 | 0.5034 | 0.6232 | 0.5507 | 0.2570 | 0.3505 |
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- | 0.7138 | 3.0 | 4396 | 0.6883 | 0.7448 | 0.6145 | 0.5918 | 0.6005 | 0.5918 | 0.7448 | 0.7004 | 0.7614 | 0.7297 | 0.5848 | 0.5819 | 0.5833 | 0.4116 | 0.4142 | 0.4129 | 0.5827 | 0.5827 | 0.5827 | 0.8380 | 0.8428 | 0.8404 | 0.6838 | 0.6222 | 0.6515 | 0.5 | 0.3376 | 0.4031 |
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- | 0.7046 | 3.75 | 5495 | 0.6826 | 0.7465 | 0.6275 | 0.5789 | 0.5986 | 0.5789 | 0.7465 | 0.7145 | 0.748 | 0.7309 | 0.5822 | 0.5658 | 0.5739 | 0.5220 | 0.3222 | 0.3984 | 0.6403 | 0.5212 | 0.5747 | 0.8318 | 0.8535 | 0.8425 | 0.6559 | 0.6476 | 0.6517 | 0.4457 | 0.3939 | 0.4182 |
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- | 0.6767 | 4.5 | 6594 | 0.6971 | 0.7436 | 0.6423 | 0.5649 | 0.5923 | 0.5649 | 0.7436 | 0.7414 | 0.7028 | 0.7216 | 0.6387 | 0.5055 | 0.5644 | 0.5714 | 0.2678 | 0.3647 | 0.6597 | 0.5564 | 0.6037 | 0.8056 | 0.8720 | 0.8375 | 0.5985 | 0.6826 | 0.6378 | 0.4807 | 0.3670 | 0.4162 |
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- | 0.661 | 5.25 | 7693 | 0.7124 | 0.7384 | 0.6295 | 0.6028 | 0.6031 | 0.6028 | 0.7384 | 0.6697 | 0.7991 | 0.7287 | 0.4849 | 0.7124 | 0.5771 | 0.3955 | 0.4435 | 0.4181 | 0.7064 | 0.5461 | 0.6160 | 0.8814 | 0.7958 | 0.8364 | 0.6848 | 0.6322 | 0.6575 | 0.5835 | 0.2903 | 0.3877 |
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- | 0.6652 | 6.0 | 8792 | 0.6706 | 0.7529 | 0.6441 | 0.5942 | 0.6136 | 0.5942 | 0.7529 | 0.7386 | 0.7306 | 0.7346 | 0.7153 | 0.4309 | 0.5378 | 0.4612 | 0.4351 | 0.4478 | 0.6354 | 0.5944 | 0.6142 | 0.8081 | 0.8841 | 0.8444 | 0.6859 | 0.6354 | 0.6597 | 0.4643 | 0.4488 | 0.4564 |
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- | 0.6532 | 6.75 | 9891 | 0.6567 | 0.7582 | 0.6578 | 0.5853 | 0.6146 | 0.5853 | 0.7582 | 0.7473 | 0.7264 | 0.7367 | 0.6156 | 0.5642 | 0.5887 | 0.5014 | 0.3682 | 0.4246 | 0.7068 | 0.5505 | 0.6189 | 0.8176 | 0.8815 | 0.8484 | 0.6602 | 0.6672 | 0.6637 | 0.5556 | 0.3389 | 0.4210 |
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- | 0.6314 | 7.5 | 10990 | 0.6726 | 0.7555 | 0.6673 | 0.5864 | 0.6142 | 0.5864 | 0.7555 | 0.7029 | 0.7795 | 0.7393 | 0.5800 | 0.6433 | 0.6100 | 0.5350 | 0.3201 | 0.4005 | 0.8117 | 0.4861 | 0.6081 | 0.8422 | 0.8456 | 0.8439 | 0.6651 | 0.6725 | 0.6688 | 0.5344 | 0.3581 | 0.4288 |
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- | 0.6045 | 8.25 | 12089 | 0.6668 | 0.7578 | 0.6551 | 0.6006 | 0.6238 | 0.6006 | 0.7578 | 0.7288 | 0.7468 | 0.7377 | 0.6554 | 0.5597 | 0.6038 | 0.4684 | 0.4038 | 0.4337 | 0.7683 | 0.5388 | 0.6334 | 0.8249 | 0.8693 | 0.8466 | 0.6924 | 0.6418 | 0.6661 | 0.4472 | 0.4437 | 0.4454 |
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- | 0.6182 | 9.0 | 13188 | 0.6659 | 0.7571 | 0.6461 | 0.6044 | 0.6205 | 0.6044 | 0.7571 | 0.7164 | 0.7602 | 0.7377 | 0.6389 | 0.5813 | 0.6087 | 0.4511 | 0.4435 | 0.4473 | 0.6770 | 0.5739 | 0.6212 | 0.8373 | 0.8555 | 0.8463 | 0.6523 | 0.6842 | 0.6679 | 0.5497 | 0.3325 | 0.4143 |
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- | 0.5927 | 9.75 | 14287 | 0.7097 | 0.7466 | 0.6561 | 0.5640 | 0.5952 | 0.5640 | 0.7466 | 0.7228 | 0.7136 | 0.7182 | 0.6138 | 0.5785 | 0.5957 | 0.5833 | 0.2490 | 0.3490 | 0.7201 | 0.5652 | 0.6333 | 0.8081 | 0.8686 | 0.8372 | 0.6367 | 0.6688 | 0.6524 | 0.5075 | 0.3043 | 0.3805 |
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- | 0.5736 | 10.5 | 15386 | 0.6663 | 0.7587 | 0.6494 | 0.6092 | 0.6225 | 0.6092 | 0.7587 | 0.7282 | 0.7576 | 0.7426 | 0.5869 | 0.6554 | 0.6193 | 0.5 | 0.3745 | 0.4282 | 0.6807 | 0.5930 | 0.6338 | 0.8502 | 0.8443 | 0.8473 | 0.6361 | 0.7122 | 0.672 | 0.5639 | 0.3274 | 0.4142 |
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- | 0.5687 | 11.25 | 16485 | 0.6599 | 0.7633 | 0.6595 | 0.6148 | 0.6337 | 0.6148 | 0.7633 | 0.7366 | 0.7447 | 0.7406 | 0.6489 | 0.6062 | 0.6268 | 0.4898 | 0.4519 | 0.4701 | 0.7461 | 0.5637 | 0.6422 | 0.8389 | 0.8663 | 0.8524 | 0.6401 | 0.6804 | 0.6596 | 0.5161 | 0.3900 | 0.4443 |
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- | 0.5652 | 12.0 | 17584 | 0.6577 | 0.7631 | 0.6548 | 0.6054 | 0.6277 | 0.6054 | 0.7631 | 0.7442 | 0.7318 | 0.7379 | 0.6340 | 0.6007 | 0.6169 | 0.4641 | 0.4059 | 0.4330 | 0.6923 | 0.5930 | 0.6388 | 0.8246 | 0.8811 | 0.8519 | 0.7164 | 0.6264 | 0.6684 | 0.5081 | 0.3990 | 0.4470 |
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- | 0.5377 | 12.75 | 18683 | 0.6681 | 0.7620 | 0.6422 | 0.6124 | 0.6250 | 0.6124 | 0.7620 | 0.7324 | 0.7607 | 0.7463 | 0.5952 | 0.6482 | 0.6206 | 0.4619 | 0.3808 | 0.4174 | 0.6490 | 0.5900 | 0.6181 | 0.8475 | 0.8551 | 0.8513 | 0.6912 | 0.6608 | 0.6757 | 0.5178 | 0.3913 | 0.4457 |
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- | 0.5312 | 13.5 | 19782 | 0.6777 | 0.7594 | 0.6362 | 0.6162 | 0.6247 | 0.6162 | 0.7594 | 0.7351 | 0.7494 | 0.7422 | 0.6058 | 0.6399 | 0.6224 | 0.4489 | 0.4226 | 0.4353 | 0.6337 | 0.6003 | 0.6165 | 0.8454 | 0.8539 | 0.8497 | 0.6744 | 0.6608 | 0.6676 | 0.5101 | 0.3862 | 0.4396 |
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- | 0.512 | 14.25 | 20881 | 0.6823 | 0.7569 | 0.6409 | 0.6172 | 0.6274 | 0.6172 | 0.7569 | 0.7051 | 0.7805 | 0.7409 | 0.6291 | 0.6051 | 0.6169 | 0.4830 | 0.4163 | 0.4472 | 0.6461 | 0.5988 | 0.6216 | 0.8506 | 0.8388 | 0.8447 | 0.6613 | 0.6757 | 0.6684 | 0.5113 | 0.4054 | 0.4522 |
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6945
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+ - Accuracy: 0.7549
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+ - Prec: 0.6320
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+ - Recall: 0.6106
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+ - F1: 0.6199
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+ - B Acc: 0.6106
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+ - Micro F1: 0.7549
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+ - Prec Joy: 0.7138
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+ - Recall Joy: 0.7631
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+ - F1 Joy: 0.7376
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+ - Prec Anger: 0.6130
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+ - Recall Anger: 0.6316
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+ - F1 Anger: 0.6222
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+ - Prec Disgust: 0.4396
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+ - Recall Disgust: 0.4038
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+ - F1 Disgust: 0.4209
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+ - Prec Fear: 0.6623
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+ - Recall Fear: 0.5886
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+ - F1 Fear: 0.6233
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+ - Prec Neutral: 0.8481
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+ - Recall Neutral: 0.8428
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+ - F1 Neutral: 0.8455
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+ - Prec Sadness: 0.6626
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+ - Recall Sadness: 0.6619
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+ - F1 Sadness: 0.6622
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+ - Prec Surprise: 0.4846
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+ - Recall Surprise: 0.3824
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+ - F1 Surprise: 0.4274
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.001
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  - train_batch_size: 32
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+ - eval_batch_size: 32
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  - seed: 42
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  - gradient_accumulation_steps: 4
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  - total_train_batch_size: 128
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.05
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+ - num_epochs: 20.0
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Prec | Recall | F1 | B Acc | Micro F1 | Prec Joy | Recall Joy | F1 Joy | Prec Anger | Recall Anger | F1 Anger | Prec Disgust | Recall Disgust | F1 Disgust | Prec Fear | Recall Fear | F1 Fear | Prec Neutral | Recall Neutral | F1 Neutral | Prec Sadness | Recall Sadness | F1 Sadness | Prec Surprise | Recall Surprise | F1 Surprise |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:--------:|:--------:|:----------:|:------:|:----------:|:------------:|:--------:|:------------:|:--------------:|:----------:|:---------:|:-----------:|:-------:|:------------:|:--------------:|:----------:|:------------:|:--------------:|:----------:|:-------------:|:---------------:|:-----------:|
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+ | 0.7938 | 1.0 | 1465 | 0.7589 | 0.7257 | 0.6233 | 0.4993 | 0.5433 | 0.4993 | 0.7257 | 0.7259 | 0.6828 | 0.7037 | 0.6223 | 0.4082 | 0.4930 | 0.5359 | 0.2657 | 0.3552 | 0.5925 | 0.5110 | 0.5487 | 0.7564 | 0.9097 | 0.8260 | 0.7150 | 0.4865 | 0.5790 | 0.4151 | 0.2315 | 0.2972 |
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+ | 0.7546 | 2.0 | 2930 | 0.7482 | 0.7243 | 0.6272 | 0.5499 | 0.5735 | 0.5499 | 0.7243 | 0.6028 | 0.8315 | 0.6989 | 0.5325 | 0.5802 | 0.5553 | 0.5135 | 0.2782 | 0.3609 | 0.6619 | 0.5388 | 0.5940 | 0.8498 | 0.8045 | 0.8265 | 0.74 | 0.5294 | 0.6172 | 0.4902 | 0.2864 | 0.3616 |
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+ | 0.7289 | 3.0 | 4395 | 0.7293 | 0.7321 | 0.6234 | 0.5839 | 0.5984 | 0.5839 | 0.7321 | 0.6491 | 0.7901 | 0.7127 | 0.6129 | 0.5271 | 0.5668 | 0.4413 | 0.4561 | 0.4486 | 0.6974 | 0.5198 | 0.5956 | 0.8364 | 0.8146 | 0.8254 | 0.6406 | 0.6423 | 0.6414 | 0.4862 | 0.3376 | 0.3985 |
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+ | 0.7076 | 4.0 | 5860 | 0.6898 | 0.7466 | 0.6572 | 0.5649 | 0.5972 | 0.5649 | 0.7466 | 0.7573 | 0.6911 | 0.7227 | 0.5110 | 0.6565 | 0.5747 | 0.4868 | 0.3096 | 0.3785 | 0.8139 | 0.4802 | 0.6041 | 0.8125 | 0.8772 | 0.8436 | 0.6939 | 0.5946 | 0.6404 | 0.5253 | 0.3453 | 0.4167 |
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+ | 0.6925 | 5.0 | 7325 | 0.7039 | 0.7403 | 0.6121 | 0.5916 | 0.5972 | 0.5916 | 0.7403 | 0.6933 | 0.7525 | 0.7217 | 0.5234 | 0.6372 | 0.5747 | 0.3630 | 0.4100 | 0.3851 | 0.6121 | 0.5798 | 0.5955 | 0.8446 | 0.8363 | 0.8404 | 0.7512 | 0.5713 | 0.6490 | 0.4973 | 0.3542 | 0.4137 |
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+ | 0.6841 | 6.0 | 8790 | 0.6704 | 0.7516 | 0.6607 | 0.5820 | 0.6076 | 0.5820 | 0.7516 | 0.7158 | 0.7536 | 0.7342 | 0.6577 | 0.4856 | 0.5587 | 0.4195 | 0.5502 | 0.4760 | 0.8476 | 0.4641 | 0.5998 | 0.8120 | 0.8784 | 0.8439 | 0.6971 | 0.6184 | 0.6554 | 0.4756 | 0.3235 | 0.3851 |
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+ | 0.6715 | 7.0 | 10255 | 0.6919 | 0.7412 | 0.6246 | 0.6180 | 0.6112 | 0.6180 | 0.7412 | 0.7020 | 0.7642 | 0.7318 | 0.5513 | 0.6034 | 0.5762 | 0.3682 | 0.5962 | 0.4553 | 0.8024 | 0.4817 | 0.6020 | 0.8602 | 0.8191 | 0.8391 | 0.6611 | 0.6481 | 0.6545 | 0.4267 | 0.4130 | 0.4198 |
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+ | 0.6562 | 8.0 | 11720 | 0.7245 | 0.7325 | 0.5985 | 0.6129 | 0.6014 | 0.6129 | 0.7325 | 0.6499 | 0.8167 | 0.7238 | 0.5320 | 0.6211 | 0.5731 | 0.3779 | 0.4728 | 0.4201 | 0.5704 | 0.6047 | 0.5871 | 0.8771 | 0.7863 | 0.8292 | 0.7431 | 0.6010 | 0.6645 | 0.4391 | 0.3875 | 0.4117 |
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+ | 0.6426 | 9.0 | 13185 | 0.6683 | 0.7510 | 0.6304 | 0.6109 | 0.6175 | 0.6109 | 0.7510 | 0.7216 | 0.7506 | 0.7358 | 0.5768 | 0.6001 | 0.5882 | 0.3908 | 0.4603 | 0.4227 | 0.7469 | 0.5359 | 0.6240 | 0.8458 | 0.8458 | 0.8458 | 0.6966 | 0.6412 | 0.6678 | 0.4347 | 0.4425 | 0.4385 |
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+ | 0.6278 | 10.0 | 14650 | 0.6661 | 0.7545 | 0.6427 | 0.5968 | 0.6142 | 0.5968 | 0.7545 | 0.7531 | 0.712 | 0.7320 | 0.6346 | 0.5476 | 0.5879 | 0.4574 | 0.4268 | 0.4416 | 0.7220 | 0.5476 | 0.6228 | 0.8304 | 0.8692 | 0.8494 | 0.5931 | 0.7276 | 0.6535 | 0.5084 | 0.3465 | 0.4122 |
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+ | 0.6218 | 11.0 | 16115 | 0.6714 | 0.7507 | 0.6478 | 0.5958 | 0.6143 | 0.5958 | 0.7507 | 0.6878 | 0.7864 | 0.7338 | 0.6796 | 0.4950 | 0.5728 | 0.4181 | 0.4916 | 0.4519 | 0.7635 | 0.4963 | 0.6016 | 0.8324 | 0.8512 | 0.8417 | 0.6816 | 0.6524 | 0.6667 | 0.4719 | 0.3977 | 0.4316 |
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+ | 0.6077 | 12.0 | 17580 | 0.6649 | 0.7543 | 0.6216 | 0.6171 | 0.6187 | 0.6171 | 0.7543 | 0.7496 | 0.7249 | 0.7371 | 0.6055 | 0.6095 | 0.6075 | 0.4449 | 0.4142 | 0.4290 | 0.6194 | 0.6076 | 0.6135 | 0.8426 | 0.8568 | 0.8497 | 0.6894 | 0.6386 | 0.6630 | 0.4 | 0.4680 | 0.4313 |
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+ | 0.5868 | 13.0 | 19045 | 0.6680 | 0.7584 | 0.6473 | 0.6026 | 0.6224 | 0.6026 | 0.7584 | 0.7192 | 0.7522 | 0.7354 | 0.6442 | 0.5658 | 0.6025 | 0.4398 | 0.4435 | 0.4417 | 0.7127 | 0.5666 | 0.6313 | 0.8293 | 0.8711 | 0.8497 | 0.7187 | 0.6174 | 0.6642 | 0.4673 | 0.4015 | 0.4319 |
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+ | 0.5747 | 14.0 | 20510 | 0.6692 | 0.7551 | 0.6293 | 0.6049 | 0.6155 | 0.6049 | 0.7551 | 0.7114 | 0.7621 | 0.7359 | 0.5985 | 0.6167 | 0.6075 | 0.4461 | 0.3808 | 0.4108 | 0.6088 | 0.6061 | 0.6075 | 0.8444 | 0.8522 | 0.8483 | 0.7124 | 0.6222 | 0.6642 | 0.4835 | 0.3939 | 0.4341 |
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+ | 0.5632 | 15.0 | 21975 | 0.6763 | 0.7551 | 0.6390 | 0.6104 | 0.6185 | 0.6104 | 0.7551 | 0.6978 | 0.7812 | 0.7371 | 0.6381 | 0.5774 | 0.6063 | 0.4179 | 0.5272 | 0.4662 | 0.6260 | 0.5710 | 0.5972 | 0.8432 | 0.8479 | 0.8455 | 0.6950 | 0.6460 | 0.6696 | 0.5551 | 0.3223 | 0.4078 |
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+ | 0.546 | 16.0 | 23440 | 0.6880 | 0.7537 | 0.6365 | 0.6089 | 0.6205 | 0.6089 | 0.7537 | 0.6906 | 0.7878 | 0.7360 | 0.6121 | 0.625 | 0.6185 | 0.4564 | 0.3828 | 0.4164 | 0.6587 | 0.6076 | 0.6321 | 0.8493 | 0.8350 | 0.8421 | 0.6999 | 0.6428 | 0.6702 | 0.4885 | 0.3811 | 0.4282 |
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+ | 0.5354 | 17.0 | 24905 | 0.6823 | 0.7545 | 0.6399 | 0.6097 | 0.6222 | 0.6097 | 0.7545 | 0.6972 | 0.7828 | 0.7375 | 0.6131 | 0.6355 | 0.6241 | 0.4916 | 0.3682 | 0.4211 | 0.6979 | 0.5783 | 0.6325 | 0.8525 | 0.8357 | 0.8440 | 0.6820 | 0.6455 | 0.6632 | 0.4447 | 0.4220 | 0.4331 |
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+ | 0.5103 | 18.0 | 26370 | 0.6852 | 0.7581 | 0.6440 | 0.6039 | 0.6206 | 0.6039 | 0.7581 | 0.7167 | 0.7642 | 0.7397 | 0.6203 | 0.6289 | 0.6246 | 0.4767 | 0.3849 | 0.4259 | 0.6752 | 0.5813 | 0.6247 | 0.8418 | 0.8525 | 0.8471 | 0.6674 | 0.6614 | 0.6644 | 0.5101 | 0.3542 | 0.4181 |
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+ | 0.4972 | 19.0 | 27835 | 0.6948 | 0.7535 | 0.6350 | 0.6039 | 0.6162 | 0.6039 | 0.7535 | 0.7038 | 0.7715 | 0.7361 | 0.5989 | 0.6515 | 0.6241 | 0.4658 | 0.3703 | 0.4126 | 0.6739 | 0.5871 | 0.6275 | 0.8495 | 0.8381 | 0.8438 | 0.6631 | 0.6571 | 0.6601 | 0.4902 | 0.3517 | 0.4095 |
102
+ | 0.4801 | 20.0 | 29300 | 0.6945 | 0.7549 | 0.6320 | 0.6106 | 0.6199 | 0.6106 | 0.7549 | 0.7138 | 0.7631 | 0.7376 | 0.6130 | 0.6316 | 0.6222 | 0.4396 | 0.4038 | 0.4209 | 0.6623 | 0.5886 | 0.6233 | 0.8481 | 0.8428 | 0.8455 | 0.6626 | 0.6619 | 0.6622 | 0.4846 | 0.3824 | 0.4274 |
103
 
104
 
105
  ### Framework versions