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lexical_classifier_bangla_assamese_v2

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  1. README.md +56 -56
  2. model.safetensors +1 -1
  3. runs/Aug01_01-36-12_03f54f1304b7/events.out.tfevents.1722476173.03f54f1304b7.335.13 +2 -2
  4. runs/Aug01_01-36-12_03f54f1304b7/events.out.tfevents.1722476637.03f54f1304b7.335.14 +3 -0
  5. runs/Aug01_01-36-12_03f54f1304b7/events.out.tfevents.1722476639.03f54f1304b7.335.15 +3 -0
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.1268
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- - Accuracy: 0.7133
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- - Precision: 0.75
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- - Recall: 0.64
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- - F1: 0.6906
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  ## Model description
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@@ -43,7 +43,7 @@ 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: 1e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
@@ -56,56 +56,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 0.1546 | 1.0 | 35 | 1.0898 | 0.7071 | 0.7321 | 0.6119 | 0.6667 |
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- | 0.1803 | 2.0 | 70 | 0.8476 | 0.7714 | 0.7869 | 0.7164 | 0.7500 |
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- | 0.1466 | 3.0 | 105 | 1.1954 | 0.7143 | 0.7647 | 0.5821 | 0.6610 |
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- | 0.1448 | 4.0 | 140 | 1.2709 | 0.6286 | 0.5701 | 0.9104 | 0.7011 |
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- | 0.1817 | 5.0 | 175 | 0.7679 | 0.7571 | 0.8667 | 0.5821 | 0.6964 |
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- | 0.209 | 6.0 | 210 | 1.1365 | 0.7214 | 0.7059 | 0.7164 | 0.7111 |
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- | 0.1137 | 7.0 | 245 | 1.2758 | 0.6143 | 0.5699 | 0.7910 | 0.6625 |
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- | 0.1919 | 8.0 | 280 | 0.9086 | 0.75 | 0.7667 | 0.6866 | 0.7244 |
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- | 0.1751 | 9.0 | 315 | 1.3040 | 0.6643 | 0.6667 | 0.5970 | 0.6299 |
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- | 0.1873 | 10.0 | 350 | 1.4273 | 0.6214 | 0.5761 | 0.7910 | 0.6667 |
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- | 0.2169 | 11.0 | 385 | 0.8060 | 0.7 | 0.6471 | 0.8209 | 0.7237 |
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- | 0.1729 | 12.0 | 420 | 0.9649 | 0.7 | 0.7358 | 0.5821 | 0.65 |
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- | 0.1361 | 13.0 | 455 | 0.9584 | 0.7286 | 0.7377 | 0.6716 | 0.7031 |
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- | 0.1001 | 14.0 | 490 | 1.2653 | 0.6786 | 0.6667 | 0.6567 | 0.6617 |
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- | 0.2205 | 15.0 | 525 | 1.0082 | 0.7143 | 0.7368 | 0.6269 | 0.6774 |
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- | 0.2171 | 16.0 | 560 | 1.0590 | 0.7143 | 0.7455 | 0.6119 | 0.6721 |
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- | 0.2125 | 17.0 | 595 | 1.1836 | 0.6857 | 0.7170 | 0.5672 | 0.6333 |
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- | 0.2154 | 18.0 | 630 | 1.0341 | 0.7071 | 0.7241 | 0.6269 | 0.672 |
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- | 0.2504 | 19.0 | 665 | 0.8796 | 0.75 | 0.8077 | 0.6269 | 0.7059 |
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- | 0.2402 | 20.0 | 700 | 0.8713 | 0.7357 | 0.7679 | 0.6418 | 0.6992 |
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- | 0.1581 | 21.0 | 735 | 1.3205 | 0.6643 | 0.6471 | 0.6567 | 0.6519 |
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- | 0.2365 | 22.0 | 770 | 1.0934 | 0.6571 | 0.6792 | 0.5373 | 0.6000 |
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- | 0.1733 | 23.0 | 805 | 1.3353 | 0.6571 | 0.6939 | 0.5075 | 0.5862 |
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- | 0.1867 | 24.0 | 840 | 1.2319 | 0.6929 | 0.6875 | 0.6567 | 0.6718 |
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- | 0.1949 | 25.0 | 875 | 1.0804 | 0.7643 | 0.7237 | 0.8209 | 0.7692 |
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- | 0.3426 | 26.0 | 910 | 1.0227 | 0.7214 | 0.7333 | 0.6567 | 0.6929 |
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- | 0.165 | 27.0 | 945 | 0.8362 | 0.7071 | 0.7097 | 0.6567 | 0.6822 |
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- | 0.1733 | 28.0 | 980 | 1.0676 | 0.7214 | 0.7258 | 0.6716 | 0.6977 |
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- | 0.1981 | 29.0 | 1015 | 0.8669 | 0.7571 | 0.8113 | 0.6418 | 0.7167 |
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- | 0.2463 | 30.0 | 1050 | 0.6745 | 0.7571 | 0.8 | 0.6567 | 0.7213 |
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- | 0.19 | 31.0 | 1085 | 1.2424 | 0.6714 | 0.6981 | 0.5522 | 0.6167 |
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- | 0.183 | 32.0 | 1120 | 1.0610 | 0.6929 | 0.6935 | 0.6418 | 0.6667 |
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- | 0.2122 | 33.0 | 1155 | 1.1757 | 0.6857 | 0.6949 | 0.6119 | 0.6508 |
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- | 0.2296 | 34.0 | 1190 | 1.1922 | 0.6214 | 0.6167 | 0.5522 | 0.5827 |
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- | 0.1708 | 35.0 | 1225 | 1.3425 | 0.6357 | 0.6333 | 0.5672 | 0.5984 |
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- | 0.2535 | 36.0 | 1260 | 1.0998 | 0.7143 | 0.7288 | 0.6418 | 0.6825 |
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- | 0.2081 | 37.0 | 1295 | 1.2987 | 0.6143 | 0.6102 | 0.5373 | 0.5714 |
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- | 0.2355 | 38.0 | 1330 | 0.7853 | 0.7643 | 0.7833 | 0.7015 | 0.7402 |
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- | 0.176 | 39.0 | 1365 | 1.2652 | 0.65 | 0.6667 | 0.5373 | 0.5950 |
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- | 0.2087 | 40.0 | 1400 | 1.0776 | 0.7 | 0.7119 | 0.6269 | 0.6667 |
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- | 0.1618 | 41.0 | 1435 | 0.9671 | 0.7286 | 0.7302 | 0.6866 | 0.7077 |
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- | 0.2631 | 42.0 | 1470 | 1.0189 | 0.7143 | 0.7288 | 0.6418 | 0.6825 |
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- | 0.253 | 43.0 | 1505 | 1.1626 | 0.7143 | 0.7143 | 0.6716 | 0.6923 |
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- | 0.2604 | 44.0 | 1540 | 0.8882 | 0.7571 | 0.7619 | 0.7164 | 0.7385 |
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- | 0.2674 | 45.0 | 1575 | 0.9212 | 0.7286 | 0.7101 | 0.7313 | 0.7206 |
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- | 0.2629 | 46.0 | 1610 | 1.2781 | 0.6857 | 0.7170 | 0.5672 | 0.6333 |
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- | 0.2848 | 47.0 | 1645 | 1.1481 | 0.7214 | 0.78 | 0.5821 | 0.6667 |
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- | 0.3771 | 48.0 | 1680 | 0.7907 | 0.7571 | 0.8 | 0.6567 | 0.7213 |
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- | 0.2988 | 49.0 | 1715 | 0.8732 | 0.7214 | 0.7414 | 0.6418 | 0.688 |
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- | 0.3449 | 50.0 | 1750 | 0.9338 | 0.7143 | 0.7647 | 0.5821 | 0.6610 |
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  ### Framework versions
 
20
 
21
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
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+ - Loss: 1.1317
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+ - Accuracy: 0.7033
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+ - Precision: 0.7480
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+ - Recall: 0.6133
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+ - F1: 0.6740
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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-06
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.1501 | 1.0 | 35 | 1.0773 | 0.7286 | 0.7302 | 0.6866 | 0.7077 |
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+ | 0.1789 | 2.0 | 70 | 0.8471 | 0.7714 | 0.7869 | 0.7164 | 0.7500 |
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+ | 0.1463 | 3.0 | 105 | 1.3021 | 0.7071 | 0.7407 | 0.5970 | 0.6612 |
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+ | 0.1664 | 4.0 | 140 | 1.1251 | 0.6857 | 0.6825 | 0.6418 | 0.6615 |
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+ | 0.1746 | 5.0 | 175 | 0.8354 | 0.7214 | 0.7692 | 0.5970 | 0.6723 |
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+ | 0.2282 | 6.0 | 210 | 1.2394 | 0.6929 | 0.6935 | 0.6418 | 0.6667 |
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+ | 0.103 | 7.0 | 245 | 1.3935 | 0.6857 | 0.6769 | 0.6567 | 0.6667 |
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+ | 0.186 | 8.0 | 280 | 1.1753 | 0.7 | 0.6812 | 0.7015 | 0.6912 |
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+ | 0.2189 | 9.0 | 315 | 1.1819 | 0.6929 | 0.7069 | 0.6119 | 0.6560 |
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+ | 0.1476 | 10.0 | 350 | 1.4722 | 0.65 | 0.65 | 0.5821 | 0.6142 |
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+ | 0.2055 | 11.0 | 385 | 0.7656 | 0.7571 | 0.7895 | 0.6716 | 0.7258 |
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+ | 0.1607 | 12.0 | 420 | 0.9756 | 0.7071 | 0.76 | 0.5672 | 0.6496 |
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+ | 0.1532 | 13.0 | 455 | 0.9945 | 0.7071 | 0.7031 | 0.6716 | 0.6870 |
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+ | 0.1023 | 14.0 | 490 | 1.1967 | 0.7071 | 0.7031 | 0.6716 | 0.6870 |
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+ | 0.2389 | 15.0 | 525 | 0.7984 | 0.7643 | 0.7742 | 0.7164 | 0.7442 |
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+ | 0.1925 | 16.0 | 560 | 0.9343 | 0.7143 | 0.7368 | 0.6269 | 0.6774 |
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+ | 0.2038 | 17.0 | 595 | 1.1440 | 0.6857 | 0.6949 | 0.6119 | 0.6508 |
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+ | 0.2193 | 18.0 | 630 | 0.9709 | 0.7071 | 0.7167 | 0.6418 | 0.6772 |
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+ | 0.1719 | 19.0 | 665 | 0.9007 | 0.7429 | 0.7818 | 0.6418 | 0.7049 |
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+ | 0.2334 | 20.0 | 700 | 0.8711 | 0.7429 | 0.7818 | 0.6418 | 0.7049 |
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+ | 0.131 | 21.0 | 735 | 1.0785 | 0.7143 | 0.7288 | 0.6418 | 0.6825 |
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+ | 0.2316 | 22.0 | 770 | 1.1080 | 0.6643 | 0.6786 | 0.5672 | 0.6179 |
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+ | 0.1815 | 23.0 | 805 | 1.2657 | 0.6929 | 0.7308 | 0.5672 | 0.6387 |
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+ | 0.1521 | 24.0 | 840 | 1.2584 | 0.7 | 0.6812 | 0.7015 | 0.6912 |
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+ | 0.244 | 25.0 | 875 | 1.0375 | 0.7786 | 0.7812 | 0.7463 | 0.7634 |
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+ | 0.3668 | 26.0 | 910 | 1.1253 | 0.7286 | 0.7458 | 0.6567 | 0.6984 |
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+ | 0.1564 | 27.0 | 945 | 0.9891 | 0.7214 | 0.7414 | 0.6418 | 0.688 |
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+ | 0.1782 | 28.0 | 980 | 0.9936 | 0.7357 | 0.75 | 0.6716 | 0.7087 |
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+ | 0.1945 | 29.0 | 1015 | 0.9586 | 0.7357 | 0.7419 | 0.6866 | 0.7132 |
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+ | 0.271 | 30.0 | 1050 | 0.8128 | 0.7357 | 0.7778 | 0.6269 | 0.6942 |
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+ | 0.1889 | 31.0 | 1085 | 1.2141 | 0.6714 | 0.7059 | 0.5373 | 0.6102 |
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+ | 0.1928 | 32.0 | 1120 | 1.0059 | 0.7143 | 0.7368 | 0.6269 | 0.6774 |
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+ | 0.2035 | 33.0 | 1155 | 1.1185 | 0.6929 | 0.7069 | 0.6119 | 0.6560 |
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+ | 0.226 | 34.0 | 1190 | 1.1719 | 0.6286 | 0.6271 | 0.5522 | 0.5873 |
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+ | 0.1801 | 35.0 | 1225 | 1.1689 | 0.6786 | 0.6719 | 0.6418 | 0.6565 |
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+ | 0.2353 | 36.0 | 1260 | 1.1392 | 0.7 | 0.6923 | 0.6716 | 0.6818 |
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+ | 0.1686 | 37.0 | 1295 | 1.2064 | 0.6429 | 0.6667 | 0.5075 | 0.5763 |
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+ | 0.2278 | 38.0 | 1330 | 0.8528 | 0.75 | 0.7759 | 0.6716 | 0.7200 |
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+ | 0.1905 | 39.0 | 1365 | 1.2736 | 0.6643 | 0.6786 | 0.5672 | 0.6179 |
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+ | 0.2136 | 40.0 | 1400 | 1.0255 | 0.7214 | 0.7333 | 0.6567 | 0.6929 |
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+ | 0.1544 | 41.0 | 1435 | 0.9427 | 0.7214 | 0.7333 | 0.6567 | 0.6929 |
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+ | 0.2691 | 42.0 | 1470 | 1.0433 | 0.7286 | 0.7544 | 0.6418 | 0.6935 |
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+ | 0.2804 | 43.0 | 1505 | 1.2006 | 0.6929 | 0.7143 | 0.5970 | 0.6504 |
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+ | 0.2345 | 44.0 | 1540 | 0.9487 | 0.75 | 0.7857 | 0.6567 | 0.7154 |
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+ | 0.2541 | 45.0 | 1575 | 0.9468 | 0.7429 | 0.7246 | 0.7463 | 0.7353 |
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+ | 0.2718 | 46.0 | 1610 | 1.3955 | 0.6714 | 0.6909 | 0.5672 | 0.6230 |
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+ | 0.3179 | 47.0 | 1645 | 1.3356 | 0.6786 | 0.7037 | 0.5672 | 0.6281 |
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+ | 0.4808 | 48.0 | 1680 | 0.9297 | 0.7429 | 0.7719 | 0.6567 | 0.7097 |
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+ | 0.3231 | 49.0 | 1715 | 0.8732 | 0.7429 | 0.7818 | 0.6418 | 0.7049 |
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+ | 0.3681 | 50.0 | 1750 | 1.0578 | 0.6857 | 0.7255 | 0.5522 | 0.6271 |
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  ### Framework versions
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