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End of training

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README.md CHANGED
@@ -9,23 +9,23 @@ metrics:
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  - recall
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  - f1
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  model-index:
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- - name: wav2vec2-classifier
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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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- # wav2vec2-classifier
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  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8841
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- - Accuracy: 0.8015
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- - Precision: 0.8244
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- - Recall: 0.8015
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- - F1: 0.7954
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- - Binary: 0.8613
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  ## Model description
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@@ -59,63 +59,58 @@ 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 | 0.17 | 50 | 4.2299 | 0.0680 | 0.0274 | 0.0680 | 0.0275 | 0.3352 |
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- | No log | 0.35 | 100 | 3.9259 | 0.0631 | 0.0167 | 0.0631 | 0.0191 | 0.3408 |
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- | No log | 0.52 | 150 | 3.6877 | 0.1117 | 0.0535 | 0.1117 | 0.0606 | 0.3738 |
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- | No log | 0.69 | 200 | 3.4911 | 0.1650 | 0.0958 | 0.1650 | 0.1017 | 0.4119 |
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- | No log | 0.86 | 250 | 3.3070 | 0.2233 | 0.1497 | 0.2233 | 0.1517 | 0.4541 |
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- | 3.8291 | 1.04 | 300 | 3.1733 | 0.2354 | 0.1675 | 0.2354 | 0.1635 | 0.4619 |
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- | 3.8291 | 1.21 | 350 | 3.0042 | 0.3034 | 0.2792 | 0.3034 | 0.2378 | 0.5087 |
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- | 3.8291 | 1.38 | 400 | 2.8631 | 0.3519 | 0.3257 | 0.3519 | 0.2808 | 0.5434 |
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- | 3.8291 | 1.55 | 450 | 2.7172 | 0.3981 | 0.4132 | 0.3981 | 0.3454 | 0.5765 |
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- | 3.8291 | 1.73 | 500 | 2.5629 | 0.4733 | 0.4595 | 0.4733 | 0.4250 | 0.6299 |
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- | 3.8291 | 1.9 | 550 | 2.4316 | 0.4927 | 0.4601 | 0.4927 | 0.4352 | 0.6420 |
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- | 2.9187 | 2.07 | 600 | 2.3206 | 0.5218 | 0.5337 | 0.5218 | 0.4800 | 0.6638 |
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- | 2.9187 | 2.24 | 650 | 2.1817 | 0.5801 | 0.5746 | 0.5801 | 0.5292 | 0.7039 |
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- | 2.9187 | 2.42 | 700 | 2.0852 | 0.5631 | 0.5165 | 0.5631 | 0.5089 | 0.6927 |
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- | 2.9187 | 2.59 | 750 | 1.9932 | 0.5752 | 0.5836 | 0.5752 | 0.5310 | 0.7005 |
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- | 2.9187 | 2.76 | 800 | 1.8989 | 0.6189 | 0.6288 | 0.6189 | 0.5850 | 0.7318 |
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- | 2.9187 | 2.93 | 850 | 1.8100 | 0.6529 | 0.6389 | 0.6529 | 0.6181 | 0.7563 |
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- | 2.291 | 3.11 | 900 | 1.7138 | 0.6650 | 0.7058 | 0.6650 | 0.6360 | 0.7641 |
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- | 2.291 | 3.28 | 950 | 1.6582 | 0.6869 | 0.7094 | 0.6869 | 0.6585 | 0.7786 |
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- | 2.291 | 3.45 | 1000 | 1.5810 | 0.7039 | 0.7509 | 0.7039 | 0.6886 | 0.7913 |
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- | 2.291 | 3.62 | 1050 | 1.5116 | 0.7306 | 0.7799 | 0.7306 | 0.7263 | 0.8100 |
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- | 2.291 | 3.8 | 1100 | 1.4638 | 0.7039 | 0.7450 | 0.7039 | 0.6850 | 0.7920 |
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- | 2.291 | 3.97 | 1150 | 1.4173 | 0.7233 | 0.7744 | 0.7233 | 0.7099 | 0.8056 |
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- | 1.8674 | 4.14 | 1200 | 1.4021 | 0.6869 | 0.7375 | 0.6869 | 0.6707 | 0.7794 |
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- | 1.8674 | 4.31 | 1250 | 1.3271 | 0.7282 | 0.7796 | 0.7282 | 0.7240 | 0.8090 |
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- | 1.8674 | 4.49 | 1300 | 1.2851 | 0.7403 | 0.7903 | 0.7403 | 0.7305 | 0.8175 |
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- | 1.8674 | 4.66 | 1350 | 1.2666 | 0.7257 | 0.7796 | 0.7257 | 0.7162 | 0.8066 |
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- | 1.8674 | 4.83 | 1400 | 1.2354 | 0.7379 | 0.7785 | 0.7379 | 0.7301 | 0.8158 |
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- | 1.5849 | 5.0 | 1450 | 1.1930 | 0.7451 | 0.7913 | 0.7451 | 0.7420 | 0.8201 |
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- | 1.5849 | 5.18 | 1500 | 1.1529 | 0.7549 | 0.8124 | 0.7549 | 0.7527 | 0.8277 |
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- | 1.5849 | 5.35 | 1550 | 1.1293 | 0.7621 | 0.8236 | 0.7621 | 0.7626 | 0.8328 |
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- | 1.5849 | 5.52 | 1600 | 1.0915 | 0.7646 | 0.8132 | 0.7646 | 0.7609 | 0.8345 |
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- | 1.5849 | 5.69 | 1650 | 1.1038 | 0.7549 | 0.8013 | 0.7549 | 0.7518 | 0.8277 |
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- | 1.5849 | 5.87 | 1700 | 1.0632 | 0.7670 | 0.8125 | 0.7670 | 0.7604 | 0.8362 |
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- | 1.379 | 6.04 | 1750 | 1.0175 | 0.7767 | 0.8151 | 0.7767 | 0.7708 | 0.8430 |
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- | 1.379 | 6.21 | 1800 | 0.9889 | 0.7791 | 0.8137 | 0.7791 | 0.7746 | 0.8447 |
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- | 1.379 | 6.38 | 1850 | 0.9825 | 0.7816 | 0.8255 | 0.7816 | 0.7793 | 0.8464 |
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- | 1.379 | 6.56 | 1900 | 0.9725 | 0.7864 | 0.8442 | 0.7864 | 0.7826 | 0.8498 |
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- | 1.379 | 6.73 | 1950 | 0.9357 | 0.7961 | 0.8341 | 0.7961 | 0.7943 | 0.8566 |
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- | 1.379 | 6.9 | 2000 | 0.9351 | 0.7888 | 0.8315 | 0.7888 | 0.7848 | 0.8515 |
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- | 1.2346 | 7.08 | 2050 | 0.9187 | 0.7888 | 0.8374 | 0.7888 | 0.7895 | 0.8515 |
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- | 1.2346 | 7.25 | 2100 | 0.9028 | 0.7840 | 0.8361 | 0.7840 | 0.7835 | 0.8481 |
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- | 1.2346 | 7.42 | 2150 | 0.8773 | 0.7961 | 0.8419 | 0.7961 | 0.7909 | 0.8566 |
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- | 1.2346 | 7.59 | 2200 | 0.8816 | 0.7985 | 0.8399 | 0.7985 | 0.8016 | 0.8583 |
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- | 1.2346 | 7.77 | 2250 | 0.8604 | 0.7913 | 0.8294 | 0.7913 | 0.7919 | 0.8532 |
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- | 1.2346 | 7.94 | 2300 | 0.8579 | 0.8010 | 0.8381 | 0.8010 | 0.8003 | 0.8600 |
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- | 1.1154 | 8.11 | 2350 | 0.8552 | 0.7985 | 0.8439 | 0.7985 | 0.7996 | 0.8583 |
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- | 1.1154 | 8.28 | 2400 | 0.8493 | 0.7985 | 0.8461 | 0.7985 | 0.8000 | 0.8583 |
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- | 1.1154 | 8.46 | 2450 | 0.8421 | 0.7985 | 0.8421 | 0.7985 | 0.8009 | 0.8583 |
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- | 1.1154 | 8.63 | 2500 | 0.8416 | 0.8010 | 0.8424 | 0.8010 | 0.8013 | 0.8600 |
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- | 1.1154 | 8.8 | 2550 | 0.8375 | 0.8010 | 0.8460 | 0.8010 | 0.8010 | 0.8600 |
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- | 1.1154 | 8.97 | 2600 | 0.8304 | 0.8058 | 0.8444 | 0.8058 | 0.8072 | 0.8633 |
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- | 1.0661 | 9.15 | 2650 | 0.8183 | 0.8107 | 0.8471 | 0.8107 | 0.8076 | 0.8667 |
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- | 1.0661 | 9.32 | 2700 | 0.8093 | 0.8131 | 0.8521 | 0.8131 | 0.8123 | 0.8684 |
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- | 1.0661 | 9.49 | 2750 | 0.8104 | 0.8155 | 0.8544 | 0.8155 | 0.8148 | 0.8701 |
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- | 1.0661 | 9.66 | 2800 | 0.8106 | 0.8204 | 0.8581 | 0.8204 | 0.8208 | 0.8735 |
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- | 1.0661 | 9.84 | 2850 | 0.8094 | 0.8131 | 0.8485 | 0.8131 | 0.8126 | 0.8684 |
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  ### Framework versions
 
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  - recall
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  - f1
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  model-index:
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+ - name: wav2vec2-classifier-aug
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  results: []
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  ---
15
 
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
17
  should probably proofread and complete it, then remove this comment. -->
18
 
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+ # wav2vec2-classifier-aug
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21
  This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9532
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+ - Accuracy: 0.7951
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+ - Precision: 0.8280
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+ - Recall: 0.7951
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+ - F1: 0.7944
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+ - Binary: 0.8569
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  ## Model description
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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 | 4.3438 | 0.0566 | 0.0357 | 0.0566 | 0.0270 | 0.3183 |
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+ | No log | 0.38 | 100 | 3.9861 | 0.0916 | 0.0269 | 0.0916 | 0.0306 | 0.3604 |
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+ | No log | 0.58 | 150 | 3.7184 | 0.1429 | 0.0837 | 0.1429 | 0.0727 | 0.3954 |
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+ | No log | 0.77 | 200 | 3.5059 | 0.2345 | 0.2019 | 0.2345 | 0.1717 | 0.4631 |
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+ | No log | 0.96 | 250 | 3.3078 | 0.2857 | 0.2031 | 0.2857 | 0.2069 | 0.4989 |
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+ | 3.9492 | 1.15 | 300 | 3.1185 | 0.3396 | 0.2410 | 0.3396 | 0.2564 | 0.5412 |
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+ | 3.9492 | 1.34 | 350 | 2.9671 | 0.3666 | 0.3559 | 0.3666 | 0.3003 | 0.5582 |
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+ | 3.9492 | 1.53 | 400 | 2.8265 | 0.4151 | 0.3929 | 0.4151 | 0.3473 | 0.5911 |
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+ | 3.9492 | 1.73 | 450 | 2.6754 | 0.5040 | 0.4403 | 0.5040 | 0.4386 | 0.6523 |
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+ | 3.9492 | 1.92 | 500 | 2.5560 | 0.5148 | 0.4664 | 0.5148 | 0.4591 | 0.6593 |
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+ | 3.0798 | 2.11 | 550 | 2.4304 | 0.5660 | 0.5781 | 0.5660 | 0.5243 | 0.6968 |
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+ | 3.0798 | 2.3 | 600 | 2.3227 | 0.5768 | 0.5487 | 0.5768 | 0.5238 | 0.7065 |
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+ | 3.0798 | 2.49 | 650 | 2.2025 | 0.6011 | 0.5784 | 0.6011 | 0.5567 | 0.7240 |
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+ | 3.0798 | 2.68 | 700 | 2.1138 | 0.6199 | 0.5967 | 0.6199 | 0.5743 | 0.7326 |
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+ | 3.0798 | 2.88 | 750 | 2.0058 | 0.6307 | 0.6042 | 0.6307 | 0.5840 | 0.7420 |
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+ | 2.5472 | 3.07 | 800 | 1.9306 | 0.6577 | 0.6453 | 0.6577 | 0.6202 | 0.7617 |
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+ | 2.5472 | 3.26 | 850 | 1.8359 | 0.6604 | 0.6700 | 0.6604 | 0.6243 | 0.7644 |
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+ | 2.5472 | 3.45 | 900 | 1.7841 | 0.6712 | 0.6500 | 0.6712 | 0.6277 | 0.7701 |
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+ | 2.5472 | 3.64 | 950 | 1.7083 | 0.6765 | 0.6906 | 0.6765 | 0.6423 | 0.7739 |
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+ | 2.5472 | 3.84 | 1000 | 1.6311 | 0.7089 | 0.7518 | 0.7089 | 0.6864 | 0.7976 |
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+ | 2.1755 | 4.03 | 1050 | 1.5739 | 0.6765 | 0.6801 | 0.6765 | 0.6404 | 0.7747 |
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+ | 2.1755 | 4.22 | 1100 | 1.5318 | 0.7008 | 0.7197 | 0.7008 | 0.6679 | 0.7919 |
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+ | 2.1755 | 4.41 | 1150 | 1.4939 | 0.7143 | 0.7365 | 0.7143 | 0.6908 | 0.8003 |
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+ | 2.1755 | 4.6 | 1200 | 1.4532 | 0.7278 | 0.7410 | 0.7278 | 0.7051 | 0.8108 |
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+ | 2.1755 | 4.79 | 1250 | 1.3933 | 0.7305 | 0.7554 | 0.7305 | 0.7152 | 0.8127 |
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+ | 2.1755 | 4.99 | 1300 | 1.3863 | 0.7143 | 0.7404 | 0.7143 | 0.6923 | 0.8013 |
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+ | 1.9226 | 5.18 | 1350 | 1.3064 | 0.7493 | 0.7955 | 0.7493 | 0.7351 | 0.8248 |
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+ | 1.9226 | 5.37 | 1400 | 1.2828 | 0.7520 | 0.7651 | 0.7520 | 0.7361 | 0.8288 |
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+ | 1.9226 | 5.56 | 1450 | 1.2408 | 0.7520 | 0.7739 | 0.7520 | 0.7374 | 0.8267 |
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+ | 1.9226 | 5.75 | 1500 | 1.2134 | 0.7628 | 0.7862 | 0.7628 | 0.7507 | 0.8342 |
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+ | 1.9226 | 5.94 | 1550 | 1.1905 | 0.7628 | 0.7948 | 0.7628 | 0.7484 | 0.8353 |
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+ | 1.7422 | 6.14 | 1600 | 1.1820 | 0.7547 | 0.7966 | 0.7547 | 0.7427 | 0.8286 |
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+ | 1.7422 | 6.33 | 1650 | 1.1576 | 0.7574 | 0.8034 | 0.7574 | 0.7453 | 0.8305 |
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+ | 1.7422 | 6.52 | 1700 | 1.1313 | 0.7574 | 0.7991 | 0.7574 | 0.7486 | 0.8315 |
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+ | 1.7422 | 6.71 | 1750 | 1.1140 | 0.7709 | 0.8030 | 0.7709 | 0.7620 | 0.8410 |
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+ | 1.7422 | 6.9 | 1800 | 1.0973 | 0.7628 | 0.7881 | 0.7628 | 0.7563 | 0.8353 |
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+ | 1.6131 | 7.09 | 1850 | 1.0891 | 0.7709 | 0.8084 | 0.7709 | 0.7670 | 0.8412 |
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+ | 1.6131 | 7.29 | 1900 | 1.0634 | 0.7601 | 0.7970 | 0.7601 | 0.7520 | 0.8345 |
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+ | 1.6131 | 7.48 | 1950 | 1.0521 | 0.7736 | 0.7937 | 0.7736 | 0.7655 | 0.8439 |
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+ | 1.6131 | 7.67 | 2000 | 1.0334 | 0.7817 | 0.8189 | 0.7817 | 0.7752 | 0.8474 |
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+ | 1.6131 | 7.86 | 2050 | 1.0233 | 0.7844 | 0.8077 | 0.7844 | 0.7740 | 0.8504 |
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+ | 1.5198 | 8.05 | 2100 | 1.0091 | 0.7817 | 0.8098 | 0.7817 | 0.7735 | 0.8474 |
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+ | 1.5198 | 8.25 | 2150 | 1.0165 | 0.7709 | 0.8120 | 0.7709 | 0.7669 | 0.8399 |
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+ | 1.5198 | 8.44 | 2200 | 0.9963 | 0.7790 | 0.8136 | 0.7790 | 0.7721 | 0.8456 |
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+ | 1.5198 | 8.63 | 2250 | 0.9857 | 0.7763 | 0.8197 | 0.7763 | 0.7728 | 0.8437 |
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+ | 1.5198 | 8.82 | 2300 | 0.9730 | 0.7898 | 0.8255 | 0.7898 | 0.7877 | 0.8531 |
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+ | 1.4558 | 9.01 | 2350 | 0.9699 | 0.7978 | 0.8347 | 0.7978 | 0.7965 | 0.8588 |
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+ | 1.4558 | 9.2 | 2400 | 0.9636 | 0.7925 | 0.8309 | 0.7925 | 0.7927 | 0.8550 |
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+ | 1.4558 | 9.4 | 2450 | 0.9541 | 0.7898 | 0.8252 | 0.7898 | 0.7891 | 0.8531 |
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+ | 1.4558 | 9.59 | 2500 | 0.9534 | 0.7925 | 0.8317 | 0.7925 | 0.7939 | 0.8550 |
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+ | 1.4558 | 9.78 | 2550 | 0.9518 | 0.7951 | 0.8280 | 0.7951 | 0.7944 | 0.8569 |
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+ | 1.4558 | 9.97 | 2600 | 0.9532 | 0.7951 | 0.8280 | 0.7951 | 0.7944 | 0.8569 |
 
 
 
 
 
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  ### Framework versions
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- size 37830
 
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