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ast-arabic

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audio-spectrogram-transformer
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  1. README.md +59 -45
  2. runs/Aug05_11-15-53_LAPTOP-1GID9RGH/events.out.tfevents.1722831354.LAPTOP-1GID9RGH.20796.2 +2 -2
  3. runs/Aug05_11-15-53_LAPTOP-1GID9RGH/events.out.tfevents.1722834945.LAPTOP-1GID9RGH.20796.3 +3 -0
README.md CHANGED
@@ -20,12 +20,12 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [MIT/ast-finetuned-speech-commands-v2](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5734
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- - Accuracy: 0.8518
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- - Precision: 0.8598
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- - Recall: 0.8518
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- - F1: 0.8430
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- - Binary: 0.8957
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  ## Model description
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@@ -44,7 +44,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.0001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
@@ -60,44 +60,58 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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- | No log | 0.19 | 50 | 4.3695 | 0.0162 | 0.0086 | 0.0162 | 0.0095 | 0.1469 |
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- | No log | 0.38 | 100 | 3.3975 | 0.2480 | 0.2204 | 0.2480 | 0.2000 | 0.4747 |
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- | No log | 0.58 | 150 | 1.9473 | 0.5067 | 0.4987 | 0.5067 | 0.4568 | 0.6574 |
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- | No log | 0.77 | 200 | 1.2409 | 0.6685 | 0.6872 | 0.6685 | 0.6429 | 0.7704 |
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- | No log | 0.96 | 250 | 0.9154 | 0.7493 | 0.7869 | 0.7493 | 0.7383 | 0.8267 |
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- | No log | 1.15 | 300 | 0.7568 | 0.7601 | 0.7885 | 0.7601 | 0.7487 | 0.8342 |
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- | No log | 1.34 | 350 | 0.6875 | 0.7951 | 0.8316 | 0.7951 | 0.7902 | 0.8558 |
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- | No log | 1.53 | 400 | 0.6592 | 0.8032 | 0.8336 | 0.8032 | 0.7964 | 0.8633 |
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- | No log | 1.73 | 450 | 0.6041 | 0.8059 | 0.8317 | 0.8059 | 0.7943 | 0.8663 |
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- | 1.8008 | 1.92 | 500 | 0.6096 | 0.8194 | 0.8487 | 0.8194 | 0.8087 | 0.8747 |
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- | 1.8008 | 2.11 | 550 | 0.5057 | 0.8410 | 0.8677 | 0.8410 | 0.8395 | 0.8887 |
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- | 1.8008 | 2.3 | 600 | 0.5269 | 0.8248 | 0.8642 | 0.8248 | 0.8234 | 0.8757 |
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- | 1.8008 | 2.49 | 650 | 0.4551 | 0.8598 | 0.8851 | 0.8598 | 0.8598 | 0.9022 |
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- | 1.8008 | 2.68 | 700 | 0.4962 | 0.8571 | 0.8766 | 0.8571 | 0.8552 | 0.9022 |
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- | 1.8008 | 2.88 | 750 | 0.5232 | 0.8464 | 0.8737 | 0.8464 | 0.8463 | 0.8935 |
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- | 1.8008 | 3.07 | 800 | 0.5889 | 0.8248 | 0.8583 | 0.8248 | 0.8189 | 0.8774 |
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- | 1.8008 | 3.26 | 850 | 0.5301 | 0.8571 | 0.8811 | 0.8571 | 0.8547 | 0.8992 |
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- | 1.8008 | 3.45 | 900 | 0.4731 | 0.8625 | 0.8812 | 0.8625 | 0.8614 | 0.9049 |
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- | 1.8008 | 3.64 | 950 | 0.4917 | 0.8544 | 0.8782 | 0.8544 | 0.8522 | 0.8992 |
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- | 0.2592 | 3.84 | 1000 | 0.4909 | 0.8625 | 0.8895 | 0.8625 | 0.8629 | 0.9049 |
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- | 0.2592 | 4.03 | 1050 | 0.5065 | 0.8544 | 0.8790 | 0.8544 | 0.8545 | 0.8992 |
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- | 0.2592 | 4.22 | 1100 | 0.5261 | 0.8437 | 0.8790 | 0.8437 | 0.8435 | 0.8906 |
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- | 0.2592 | 4.41 | 1150 | 0.5140 | 0.8598 | 0.8935 | 0.8598 | 0.8591 | 0.9019 |
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- | 0.2592 | 4.6 | 1200 | 0.4787 | 0.8760 | 0.8979 | 0.8760 | 0.8751 | 0.9132 |
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- | 0.2592 | 4.79 | 1250 | 0.5090 | 0.8652 | 0.8861 | 0.8652 | 0.8637 | 0.9049 |
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- | 0.2592 | 4.99 | 1300 | 0.5697 | 0.8437 | 0.8753 | 0.8437 | 0.8416 | 0.8898 |
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- | 0.2592 | 5.18 | 1350 | 0.5416 | 0.8625 | 0.8868 | 0.8625 | 0.8618 | 0.9038 |
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- | 0.2592 | 5.37 | 1400 | 0.5563 | 0.8518 | 0.8767 | 0.8518 | 0.8513 | 0.8962 |
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- | 0.2592 | 5.56 | 1450 | 0.5191 | 0.8679 | 0.8835 | 0.8679 | 0.8655 | 0.9097 |
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- | 0.0549 | 5.75 | 1500 | 0.4890 | 0.8787 | 0.9009 | 0.8787 | 0.8768 | 0.9164 |
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- | 0.0549 | 5.94 | 1550 | 0.4928 | 0.8814 | 0.9090 | 0.8814 | 0.8820 | 0.9181 |
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- | 0.0549 | 6.14 | 1600 | 0.5669 | 0.8679 | 0.8912 | 0.8679 | 0.8685 | 0.9097 |
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- | 0.0549 | 6.33 | 1650 | 0.5215 | 0.8760 | 0.9003 | 0.8760 | 0.8758 | 0.9132 |
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- | 0.0549 | 6.52 | 1700 | 0.5385 | 0.8760 | 0.9052 | 0.8760 | 0.8764 | 0.9135 |
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- | 0.0549 | 6.71 | 1750 | 0.5916 | 0.8625 | 0.8908 | 0.8625 | 0.8603 | 0.9038 |
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- | 0.0549 | 6.9 | 1800 | 0.5863 | 0.8598 | 0.8798 | 0.8598 | 0.8591 | 0.9030 |
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- | 0.0549 | 7.09 | 1850 | 0.5955 | 0.8571 | 0.8844 | 0.8571 | 0.8574 | 0.9011 |
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- | 0.0549 | 7.29 | 1900 | 0.6003 | 0.8571 | 0.8867 | 0.8571 | 0.8549 | 0.9011 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [MIT/ast-finetuned-speech-commands-v2](https://huggingface.co/MIT/ast-finetuned-speech-commands-v2) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6961
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+ - Accuracy: 0.7871
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+ - Precision: 0.8186
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+ - Recall: 0.7871
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+ - F1: 0.7848
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+ - Binary: 0.8501
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  ## Model description
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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: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Binary |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
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+ | No log | 0.19 | 50 | 4.5626 | 0.0027 | 0.0001 | 0.0027 | 0.0003 | 0.0860 |
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+ | No log | 0.38 | 100 | 4.4915 | 0.0054 | 0.0037 | 0.0054 | 0.0034 | 0.0954 |
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+ | No log | 0.58 | 150 | 4.3465 | 0.0216 | 0.0105 | 0.0216 | 0.0123 | 0.1488 |
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+ | No log | 0.77 | 200 | 4.1317 | 0.0647 | 0.0355 | 0.0647 | 0.0372 | 0.2356 |
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+ | No log | 0.96 | 250 | 3.8709 | 0.1267 | 0.0963 | 0.1267 | 0.0880 | 0.3245 |
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+ | No log | 1.15 | 300 | 3.4181 | 0.2318 | 0.2405 | 0.2318 | 0.1927 | 0.4442 |
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+ | No log | 1.34 | 350 | 2.8697 | 0.3612 | 0.3714 | 0.3612 | 0.3150 | 0.5426 |
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+ | No log | 1.53 | 400 | 2.3609 | 0.4663 | 0.4425 | 0.4663 | 0.4157 | 0.6248 |
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+ | No log | 1.73 | 450 | 1.9516 | 0.5606 | 0.5573 | 0.5606 | 0.5246 | 0.6903 |
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+ | 3.5112 | 1.92 | 500 | 1.6670 | 0.6092 | 0.6428 | 0.6092 | 0.5849 | 0.7240 |
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+ | 3.5112 | 2.11 | 550 | 1.4376 | 0.6739 | 0.6988 | 0.6739 | 0.6544 | 0.7709 |
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+ | 3.5112 | 2.3 | 600 | 1.3049 | 0.6658 | 0.7019 | 0.6658 | 0.6495 | 0.7636 |
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+ | 3.5112 | 2.49 | 650 | 1.1820 | 0.6846 | 0.7214 | 0.6846 | 0.6731 | 0.7784 |
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+ | 3.5112 | 2.68 | 700 | 1.0847 | 0.7035 | 0.7220 | 0.7035 | 0.6913 | 0.7916 |
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+ | 3.5112 | 2.88 | 750 | 1.0370 | 0.7116 | 0.7415 | 0.7116 | 0.7004 | 0.7973 |
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+ | 3.5112 | 3.07 | 800 | 0.9833 | 0.7062 | 0.7132 | 0.7062 | 0.6903 | 0.7935 |
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+ | 3.5112 | 3.26 | 850 | 0.9453 | 0.7116 | 0.7384 | 0.7116 | 0.6986 | 0.7973 |
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+ | 3.5112 | 3.45 | 900 | 0.9140 | 0.7412 | 0.7724 | 0.7412 | 0.7313 | 0.8181 |
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+ | 3.5112 | 3.64 | 950 | 0.8602 | 0.7493 | 0.7719 | 0.7493 | 0.7403 | 0.8237 |
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+ | 1.0914 | 3.84 | 1000 | 0.8340 | 0.7520 | 0.7857 | 0.7520 | 0.7469 | 0.8256 |
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+ | 1.0914 | 4.03 | 1050 | 0.8317 | 0.7628 | 0.8014 | 0.7628 | 0.7602 | 0.8332 |
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+ | 1.0914 | 4.22 | 1100 | 0.7983 | 0.7628 | 0.7863 | 0.7628 | 0.7584 | 0.8332 |
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+ | 1.0914 | 4.41 | 1150 | 0.8015 | 0.7601 | 0.7852 | 0.7601 | 0.7529 | 0.8313 |
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+ | 1.0914 | 4.6 | 1200 | 0.7584 | 0.7709 | 0.7959 | 0.7709 | 0.7685 | 0.8388 |
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+ | 1.0914 | 4.79 | 1250 | 0.7518 | 0.7763 | 0.7957 | 0.7763 | 0.7699 | 0.8426 |
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+ | 1.0914 | 4.99 | 1300 | 0.7484 | 0.7520 | 0.7698 | 0.7520 | 0.7449 | 0.8256 |
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+ | 1.0914 | 5.18 | 1350 | 0.7518 | 0.7466 | 0.7711 | 0.7466 | 0.7397 | 0.8218 |
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+ | 1.0914 | 5.37 | 1400 | 0.7379 | 0.7682 | 0.7961 | 0.7682 | 0.7654 | 0.8369 |
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+ | 1.0914 | 5.56 | 1450 | 0.7356 | 0.7601 | 0.7770 | 0.7601 | 0.7533 | 0.8313 |
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+ | 0.6112 | 5.75 | 1500 | 0.7351 | 0.7763 | 0.8018 | 0.7763 | 0.7720 | 0.8426 |
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+ | 0.6112 | 5.94 | 1550 | 0.7230 | 0.7655 | 0.7886 | 0.7655 | 0.7631 | 0.8350 |
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+ | 0.6112 | 6.14 | 1600 | 0.7222 | 0.7709 | 0.7977 | 0.7709 | 0.7656 | 0.8388 |
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+ | 0.6112 | 6.33 | 1650 | 0.7054 | 0.7790 | 0.8022 | 0.7790 | 0.7744 | 0.8445 |
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+ | 0.6112 | 6.52 | 1700 | 0.7286 | 0.7736 | 0.8016 | 0.7736 | 0.7693 | 0.8407 |
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+ | 0.6112 | 6.71 | 1750 | 0.6991 | 0.7925 | 0.8141 | 0.7925 | 0.7907 | 0.8539 |
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+ | 0.6112 | 6.9 | 1800 | 0.7096 | 0.7655 | 0.7907 | 0.7655 | 0.7633 | 0.8350 |
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+ | 0.6112 | 7.09 | 1850 | 0.7010 | 0.7844 | 0.8062 | 0.7844 | 0.7821 | 0.8482 |
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+ | 0.6112 | 7.29 | 1900 | 0.7026 | 0.7574 | 0.7813 | 0.7574 | 0.7514 | 0.8294 |
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+ | 0.6112 | 7.48 | 1950 | 0.6973 | 0.7817 | 0.7992 | 0.7817 | 0.7785 | 0.8464 |
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+ | 0.4147 | 7.67 | 2000 | 0.7035 | 0.7736 | 0.7911 | 0.7736 | 0.7707 | 0.8407 |
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+ | 0.4147 | 7.86 | 2050 | 0.6894 | 0.7844 | 0.8089 | 0.7844 | 0.7827 | 0.8482 |
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+ | 0.4147 | 8.05 | 2100 | 0.7046 | 0.7763 | 0.8009 | 0.7763 | 0.7731 | 0.8426 |
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+ | 0.4147 | 8.25 | 2150 | 0.6994 | 0.7817 | 0.8028 | 0.7817 | 0.7786 | 0.8464 |
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+ | 0.4147 | 8.44 | 2200 | 0.6890 | 0.7763 | 0.7994 | 0.7763 | 0.7723 | 0.8426 |
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+ | 0.4147 | 8.63 | 2250 | 0.6988 | 0.7763 | 0.7954 | 0.7763 | 0.7722 | 0.8426 |
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+ | 0.4147 | 8.82 | 2300 | 0.7021 | 0.7763 | 0.7964 | 0.7763 | 0.7731 | 0.8426 |
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+ | 0.4147 | 9.01 | 2350 | 0.6974 | 0.7817 | 0.8048 | 0.7817 | 0.7784 | 0.8464 |
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+ | 0.4147 | 9.2 | 2400 | 0.6861 | 0.7817 | 0.8041 | 0.7817 | 0.7807 | 0.8464 |
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+ | 0.4147 | 9.4 | 2450 | 0.6930 | 0.7763 | 0.7994 | 0.7763 | 0.7734 | 0.8426 |
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+ | 0.3165 | 9.59 | 2500 | 0.6954 | 0.7763 | 0.8001 | 0.7763 | 0.7741 | 0.8426 |
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+ | 0.3165 | 9.78 | 2550 | 0.6953 | 0.7817 | 0.8045 | 0.7817 | 0.7794 | 0.8464 |
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+ | 0.3165 | 9.97 | 2600 | 0.6940 | 0.7817 | 0.8053 | 0.7817 | 0.7794 | 0.8464 |
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  ### Framework versions
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