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Joy28
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videomae-base-finetuned-subset

Video Classification
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TensorBoard
Safetensors
videomae
Generated from Trainer
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  1. README.md +63 -63
  2. model.safetensors +1 -1
README.md CHANGED
@@ -17,8 +17,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0747
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- - Accuracy: 0.6991
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  ## Model description
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@@ -44,72 +44,72 @@ The following hyperparameters were used during training:
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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: 0.1
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- - training_steps: 7020
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 0.9225 | 0.02 | 118 | 0.7363 | 0.7419 |
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- | 0.8357 | 1.02 | 236 | 0.9119 | 0.7005 |
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- | 0.474 | 2.02 | 354 | 0.9698 | 0.6820 |
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- | 0.7899 | 3.02 | 472 | 1.1351 | 0.6774 |
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- | 0.9015 | 4.02 | 590 | 1.3823 | 0.4977 |
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- | 0.7402 | 5.02 | 708 | 0.8661 | 0.6959 |
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- | 0.6343 | 6.02 | 826 | 0.6689 | 0.7005 |
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- | 0.7427 | 7.02 | 944 | 0.9109 | 0.6728 |
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- | 0.5898 | 8.02 | 1062 | 1.0127 | 0.5945 |
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- | 0.6258 | 9.02 | 1180 | 0.7131 | 0.7235 |
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- | 0.9957 | 10.02 | 1298 | 0.9507 | 0.6728 |
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- | 0.401 | 11.02 | 1416 | 0.6259 | 0.7189 |
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- | 0.5422 | 12.02 | 1534 | 0.9453 | 0.6774 |
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- | 0.6852 | 13.02 | 1652 | 0.8649 | 0.7005 |
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- | 0.8469 | 14.02 | 1770 | 0.9379 | 0.6912 |
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- | 0.8492 | 15.02 | 1888 | 0.9003 | 0.6452 |
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- | 0.7633 | 16.02 | 2006 | 0.7601 | 0.7235 |
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- | 0.6063 | 17.02 | 2124 | 0.6181 | 0.7788 |
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- | 0.6436 | 18.02 | 2242 | 0.9445 | 0.6313 |
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- | 0.8931 | 19.02 | 2360 | 0.8515 | 0.7281 |
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- | 0.8599 | 20.02 | 2478 | 1.0786 | 0.6359 |
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- | 0.5183 | 21.02 | 2596 | 0.9481 | 0.6866 |
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- | 0.7982 | 22.02 | 2714 | 0.8364 | 0.7235 |
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- | 1.0003 | 23.02 | 2832 | 0.7811 | 0.7327 |
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- | 0.6666 | 24.02 | 2950 | 0.7552 | 0.7465 |
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- | 0.8527 | 25.02 | 3068 | 0.8201 | 0.7189 |
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- | 0.4678 | 26.02 | 3186 | 1.0260 | 0.6959 |
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- | 0.7354 | 27.02 | 3304 | 0.8520 | 0.6866 |
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- | 1.1097 | 28.02 | 3422 | 0.9239 | 0.7327 |
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- | 0.6264 | 29.02 | 3540 | 0.6894 | 0.7558 |
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- | 0.3348 | 30.02 | 3658 | 0.6230 | 0.8065 |
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- | 0.5548 | 31.02 | 3776 | 0.6431 | 0.8203 |
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- | 0.4242 | 32.02 | 3894 | 0.8081 | 0.7051 |
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- | 0.5805 | 33.02 | 4012 | 0.5598 | 0.8203 |
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- | 0.7064 | 34.02 | 4130 | 0.7341 | 0.7926 |
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- | 0.2534 | 35.02 | 4248 | 0.6685 | 0.7834 |
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- | 0.7578 | 36.02 | 4366 | 0.7592 | 0.7604 |
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- | 0.5822 | 37.02 | 4484 | 0.9472 | 0.7281 |
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- | 0.2939 | 38.02 | 4602 | 0.8888 | 0.7281 |
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- | 0.4795 | 39.02 | 4720 | 1.0768 | 0.6636 |
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- | 0.4038 | 40.02 | 4838 | 0.6452 | 0.8065 |
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- | 0.8347 | 41.02 | 4956 | 0.7040 | 0.7926 |
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- | 0.4113 | 42.02 | 5074 | 0.8012 | 0.7373 |
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- | 0.3681 | 43.02 | 5192 | 0.7622 | 0.7880 |
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- | 1.0092 | 44.02 | 5310 | 0.7932 | 0.7880 |
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- | 0.321 | 45.02 | 5428 | 0.9069 | 0.7373 |
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- | 0.399 | 46.02 | 5546 | 0.6439 | 0.8111 |
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- | 0.3699 | 47.02 | 5664 | 0.7740 | 0.7696 |
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- | 0.4297 | 48.02 | 5782 | 0.6811 | 0.8249 |
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- | 0.2783 | 49.02 | 5900 | 0.5868 | 0.8525 |
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- | 0.4946 | 50.02 | 6018 | 0.6732 | 0.7926 |
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- | 0.3058 | 51.02 | 6136 | 0.5511 | 0.8341 |
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- | 0.1286 | 52.02 | 6254 | 0.5877 | 0.8295 |
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- | 0.2013 | 53.02 | 6372 | 0.6508 | 0.8157 |
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- | 0.2027 | 54.02 | 6490 | 0.6630 | 0.8157 |
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- | 0.6267 | 55.02 | 6608 | 0.7373 | 0.8065 |
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- | 0.4561 | 56.02 | 6726 | 0.7383 | 0.8018 |
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- | 0.7002 | 57.02 | 6844 | 0.7073 | 0.8111 |
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- | 0.1823 | 58.02 | 6962 | 0.6871 | 0.8203 |
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- | 0.2439 | 59.01 | 7020 | 0.6901 | 0.8203 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8930
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+ - Accuracy: 0.6296
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  ## Model description
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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: 0.1
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+ - training_steps: 6660
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 1.6074 | 0.02 | 112 | 1.5690 | 0.3687 |
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+ | 1.6001 | 1.02 | 224 | 1.5783 | 0.3041 |
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+ | 1.4193 | 2.02 | 336 | 1.4874 | 0.3825 |
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+ | 1.398 | 3.02 | 448 | 1.0197 | 0.6406 |
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+ | 1.2217 | 4.02 | 560 | 1.3386 | 0.3917 |
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+ | 1.2577 | 5.02 | 672 | 1.2196 | 0.5392 |
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+ | 1.0121 | 6.02 | 784 | 1.2319 | 0.4793 |
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+ | 1.2485 | 7.02 | 896 | 0.8230 | 0.7512 |
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+ | 1.025 | 8.02 | 1008 | 0.8023 | 0.6866 |
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+ | 1.2952 | 9.02 | 1120 | 0.9130 | 0.6037 |
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+ | 0.9499 | 10.02 | 1232 | 1.0621 | 0.6037 |
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+ | 0.8805 | 11.02 | 1344 | 0.8713 | 0.7051 |
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+ | 1.2066 | 12.02 | 1456 | 0.9364 | 0.5853 |
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+ | 0.9358 | 13.02 | 1568 | 0.9107 | 0.5853 |
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+ | 0.9043 | 14.02 | 1680 | 0.9147 | 0.6359 |
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+ | 0.8383 | 15.02 | 1792 | 0.9451 | 0.6359 |
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+ | 0.7482 | 16.02 | 1904 | 0.8765 | 0.6221 |
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+ | 0.9547 | 17.02 | 2016 | 0.7998 | 0.7650 |
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+ | 0.7028 | 18.02 | 2128 | 0.9257 | 0.6406 |
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+ | 0.8659 | 19.02 | 2240 | 1.0655 | 0.5853 |
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+ | 0.5591 | 20.02 | 2352 | 1.2794 | 0.5760 |
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+ | 0.8963 | 21.02 | 2464 | 1.0049 | 0.6959 |
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+ | 0.9221 | 22.02 | 2576 | 1.1113 | 0.6083 |
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+ | 0.7154 | 23.02 | 2688 | 0.9371 | 0.6406 |
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+ | 0.8795 | 24.02 | 2800 | 0.6838 | 0.7235 |
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+ | 0.631 | 25.02 | 2912 | 1.2093 | 0.6129 |
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+ | 1.0489 | 26.02 | 3024 | 1.4720 | 0.5484 |
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+ | 0.5881 | 27.02 | 3136 | 1.1905 | 0.6313 |
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+ | 0.7919 | 28.02 | 3248 | 1.1292 | 0.5760 |
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+ | 0.9158 | 29.02 | 3360 | 1.0214 | 0.6359 |
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+ | 0.8319 | 30.02 | 3472 | 1.2862 | 0.6682 |
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+ | 0.6775 | 31.02 | 3584 | 1.0971 | 0.6406 |
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+ | 0.7191 | 32.02 | 3696 | 1.0264 | 0.6498 |
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+ | 0.7662 | 33.02 | 3808 | 1.0589 | 0.6406 |
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+ | 0.7313 | 34.02 | 3920 | 1.5076 | 0.5622 |
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+ | 0.7539 | 35.02 | 4032 | 1.2265 | 0.5899 |
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+ | 0.571 | 36.02 | 4144 | 1.1598 | 0.6267 |
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+ | 0.3404 | 37.02 | 4256 | 1.0307 | 0.6359 |
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+ | 0.5553 | 38.02 | 4368 | 0.8180 | 0.7235 |
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+ | 0.8499 | 39.02 | 4480 | 1.0074 | 0.6498 |
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+ | 0.5036 | 40.02 | 4592 | 1.1160 | 0.6313 |
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+ | 0.814 | 41.02 | 4704 | 0.9032 | 0.6959 |
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+ | 0.7293 | 42.02 | 4816 | 0.9331 | 0.7281 |
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+ | 0.4402 | 43.02 | 4928 | 1.4190 | 0.5668 |
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+ | 0.4625 | 44.02 | 5040 | 1.0268 | 0.7005 |
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+ | 0.2266 | 45.02 | 5152 | 1.2808 | 0.6406 |
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+ | 0.7424 | 46.02 | 5264 | 1.1821 | 0.6498 |
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+ | 0.4852 | 47.02 | 5376 | 1.2434 | 0.6590 |
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+ | 0.523 | 48.02 | 5488 | 1.2123 | 0.6267 |
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+ | 0.8344 | 49.02 | 5600 | 1.1889 | 0.6636 |
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+ | 0.6648 | 50.02 | 5712 | 1.2328 | 0.6406 |
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+ | 0.6929 | 51.02 | 5824 | 1.3269 | 0.6129 |
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+ | 0.4253 | 52.02 | 5936 | 1.1885 | 0.6820 |
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+ | 0.7003 | 53.02 | 6048 | 1.1522 | 0.7005 |
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+ | 0.4105 | 54.02 | 6160 | 1.0037 | 0.7373 |
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+ | 0.5206 | 55.02 | 6272 | 1.0913 | 0.7189 |
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+ | 0.7129 | 56.02 | 6384 | 1.1083 | 0.6866 |
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+ | 0.4772 | 57.02 | 6496 | 1.1276 | 0.7143 |
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+ | 0.4822 | 58.02 | 6608 | 1.0920 | 0.7235 |
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+ | 0.6307 | 59.01 | 6660 | 1.0987 | 0.7189 |
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  ### Framework versions
model.safetensors CHANGED
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