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rtdetr-v2-r50-cppe5-finetune-2

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rt_detr_v2
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  1. README.md +114 -0
  2. config.json +126 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +35 -0
  5. runs/May17_12-33-57_50ba844597d8/events.out.tfevents.1747485285.50ba844597d8.20148.0 +3 -0
  6. runs/May17_12-33-57_50ba844597d8/events.out.tfevents.1747485668.50ba844597d8.20148.1 +3 -0
  7. runs/May17_12-53-18_50ba844597d8/events.out.tfevents.1747486401.50ba844597d8.20148.2 +3 -0
  8. runs/May17_13-14-11_50ba844597d8/events.out.tfevents.1747487657.50ba844597d8.20148.3 +3 -0
  9. runs/May17_13-14-11_50ba844597d8/events.out.tfevents.1747489695.50ba844597d8.20148.4 +3 -0
  10. runs/May17_13-14-11_50ba844597d8/events.out.tfevents.1747498219.50ba844597d8.20148.5 +3 -0
  11. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: PekingU/rtdetr_v2_r50vd
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: rtdetr-v2-r50-cppe5-finetune-2
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+ results: []
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+ ---
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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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+
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+ # rtdetr-v2-r50-cppe5-finetune-2
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+
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+ This model is a fine-tuned version of [PekingU/rtdetr_v2_r50vd](https://huggingface.co/PekingU/rtdetr_v2_r50vd) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 10.1375
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+ - Map: 0.4084
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+ - Map 50: 0.8596
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+ - Map 75: 0.3234
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+ - Map Small: 0.3882
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+ - Map Medium: 0.5785
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+ - Map Large: 0.5687
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+ - Mar 1: 0.2691
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+ - Mar 10: 0.4682
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+ - Mar 100: 0.5651
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+ - Mar Small: 0.5422
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+ - Mar Medium: 0.6918
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+ - Mar Large: 0.72
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+ - Map Football: 0.4618
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+ - Mar 100 Football: 0.5756
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+ - Map Player: 0.355
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+ - Mar 100 Player: 0.5547
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_steps: 300
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+ - num_epochs: 40
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Football | Mar 100 Football | Map Player | Mar 100 Player |
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+ |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:----------:|:--------------:|
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+ | No log | 1.0 | 124 | 10.3265 | 0.3566 | 0.7866 | 0.2572 | 0.3255 | 0.5516 | 0.4724 | 0.2255 | 0.418 | 0.5143 | 0.4799 | 0.6962 | 0.739 | 0.364 | 0.4775 | 0.3493 | 0.551 |
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+ | No log | 2.0 | 248 | 10.4388 | 0.3476 | 0.7973 | 0.24 | 0.3233 | 0.528 | 0.4569 | 0.2256 | 0.4136 | 0.5185 | 0.487 | 0.6865 | 0.7293 | 0.3559 | 0.4775 | 0.3392 | 0.5594 |
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+ | No log | 3.0 | 372 | 10.9180 | 0.3378 | 0.7542 | 0.2494 | 0.3061 | 0.5416 | 0.5064 | 0.2324 | 0.4123 | 0.5224 | 0.491 | 0.684 | 0.7439 | 0.3568 | 0.4888 | 0.3189 | 0.5559 |
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+ | No log | 4.0 | 496 | 11.2275 | 0.3287 | 0.7413 | 0.2213 | 0.2983 | 0.5347 | 0.4744 | 0.2308 | 0.4146 | 0.5263 | 0.4964 | 0.6861 | 0.7317 | 0.3609 | 0.503 | 0.2964 | 0.5496 |
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+ | 12.9724 | 5.0 | 620 | 11.2444 | 0.3375 | 0.7569 | 0.2466 | 0.3029 | 0.5587 | 0.5515 | 0.2323 | 0.4269 | 0.537 | 0.5104 | 0.6695 | 0.7488 | 0.37 | 0.5249 | 0.305 | 0.5492 |
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+ | 12.9724 | 6.0 | 744 | 11.4802 | 0.3462 | 0.7807 | 0.2382 | 0.3055 | 0.5936 | 0.5824 | 0.2315 | 0.4299 | 0.5303 | 0.4977 | 0.6974 | 0.7268 | 0.3582 | 0.5059 | 0.3341 | 0.5548 |
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+ | 12.9724 | 7.0 | 868 | 11.9056 | 0.3396 | 0.7337 | 0.2659 | 0.3012 | 0.5847 | 0.5642 | 0.2388 | 0.4265 | 0.5374 | 0.509 | 0.6914 | 0.7146 | 0.3767 | 0.5136 | 0.3026 | 0.5612 |
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+ | 12.9724 | 8.0 | 992 | 11.7865 | 0.3534 | 0.7781 | 0.2686 | 0.3112 | 0.5866 | 0.5549 | 0.2412 | 0.4391 | 0.5393 | 0.5122 | 0.6801 | 0.7195 | 0.385 | 0.5296 | 0.3219 | 0.549 |
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+ | 11.615 | 9.0 | 1116 | 12.0994 | 0.3361 | 0.7367 | 0.2398 | 0.2996 | 0.5776 | 0.5116 | 0.2415 | 0.4279 | 0.5319 | 0.5039 | 0.6768 | 0.7098 | 0.3727 | 0.5154 | 0.2994 | 0.5485 |
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+ | 11.615 | 10.0 | 1240 | 12.4295 | 0.3325 | 0.7519 | 0.2303 | 0.2921 | 0.5838 | 0.5518 | 0.2325 | 0.4276 | 0.5375 | 0.5091 | 0.689 | 0.7049 | 0.3564 | 0.5089 | 0.3086 | 0.5661 |
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+ | 11.615 | 11.0 | 1364 | 12.5365 | 0.3286 | 0.7469 | 0.228 | 0.289 | 0.5747 | 0.5284 | 0.2408 | 0.4268 | 0.5262 | 0.4969 | 0.6825 | 0.6805 | 0.3603 | 0.5142 | 0.2968 | 0.5383 |
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+ | 11.615 | 12.0 | 1488 | 12.5350 | 0.3099 | 0.7188 | 0.2137 | 0.2759 | 0.5623 | 0.5094 | 0.2376 | 0.4235 | 0.5252 | 0.4949 | 0.6815 | 0.7122 | 0.3523 | 0.5207 | 0.2676 | 0.5298 |
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+ | 11.1429 | 13.0 | 1612 | 12.5665 | 0.3037 | 0.7079 | 0.2014 | 0.2602 | 0.5672 | 0.4773 | 0.2273 | 0.4185 | 0.5224 | 0.4927 | 0.6817 | 0.6951 | 0.3329 | 0.513 | 0.2746 | 0.5319 |
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+ | 11.1429 | 14.0 | 1736 | 12.6380 | 0.3242 | 0.7318 | 0.2287 | 0.2833 | 0.5496 | 0.537 | 0.2359 | 0.4271 | 0.5303 | 0.5037 | 0.6635 | 0.7195 | 0.3556 | 0.5178 | 0.2928 | 0.5429 |
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+ | 11.1429 | 15.0 | 1860 | 12.6226 | 0.3387 | 0.7516 | 0.2502 | 0.3013 | 0.5664 | 0.5167 | 0.242 | 0.4358 | 0.5405 | 0.5148 | 0.672 | 0.7024 | 0.3785 | 0.5396 | 0.299 | 0.5414 |
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+ | 11.1429 | 16.0 | 1984 | 12.6546 | 0.3201 | 0.7384 | 0.2163 | 0.2788 | 0.5481 | 0.5198 | 0.2334 | 0.4177 | 0.5154 | 0.4877 | 0.6555 | 0.6756 | 0.335 | 0.5041 | 0.3051 | 0.5267 |
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+ | 10.8616 | 17.0 | 2108 | 12.7991 | 0.3023 | 0.6993 | 0.1833 | 0.2653 | 0.5407 | 0.5212 | 0.2226 | 0.4196 | 0.5267 | 0.502 | 0.6506 | 0.661 | 0.3164 | 0.5136 | 0.2883 | 0.5398 |
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+ | 10.8616 | 18.0 | 2232 | 13.0344 | 0.2959 | 0.6811 | 0.1957 | 0.2643 | 0.5249 | 0.4736 | 0.2203 | 0.4098 | 0.5206 | 0.4987 | 0.6312 | 0.639 | 0.3156 | 0.4947 | 0.2762 | 0.5464 |
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+ | 10.8616 | 19.0 | 2356 | 12.6805 | 0.3113 | 0.7391 | 0.1916 | 0.2837 | 0.5056 | 0.5156 | 0.2182 | 0.4131 | 0.519 | 0.4974 | 0.6222 | 0.6561 | 0.3318 | 0.4959 | 0.2907 | 0.5421 |
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+ | 10.8616 | 20.0 | 2480 | 12.8532 | 0.3159 | 0.7272 | 0.2227 | 0.2711 | 0.5479 | 0.5232 | 0.2346 | 0.4329 | 0.527 | 0.4992 | 0.6608 | 0.6951 | 0.3304 | 0.5266 | 0.3014 | 0.5273 |
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+ | 10.6669 | 21.0 | 2604 | 13.0404 | 0.3085 | 0.7068 | 0.212 | 0.2741 | 0.5373 | 0.4835 | 0.2291 | 0.426 | 0.5214 | 0.497 | 0.6443 | 0.6512 | 0.3297 | 0.5172 | 0.2872 | 0.5257 |
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+ | 10.6669 | 22.0 | 2728 | 13.2767 | 0.3004 | 0.7087 | 0.1731 | 0.2698 | 0.5037 | 0.4881 | 0.22 | 0.4177 | 0.5127 | 0.4878 | 0.6382 | 0.6195 | 0.3094 | 0.4929 | 0.2913 | 0.5324 |
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+ | 10.6669 | 23.0 | 2852 | 13.3145 | 0.3136 | 0.7285 | 0.2183 | 0.276 | 0.5546 | 0.3603 | 0.2339 | 0.4163 | 0.5121 | 0.4841 | 0.6596 | 0.6195 | 0.3348 | 0.5012 | 0.2924 | 0.5231 |
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+ | 10.6669 | 24.0 | 2976 | 13.2074 | 0.303 | 0.7025 | 0.2054 | 0.2746 | 0.5373 | 0.4228 | 0.2381 | 0.4138 | 0.5124 | 0.4857 | 0.6468 | 0.6463 | 0.3356 | 0.5036 | 0.2704 | 0.5213 |
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+ | 10.4375 | 25.0 | 3100 | 13.2769 | 0.2989 | 0.7142 | 0.182 | 0.2699 | 0.5093 | 0.3726 | 0.2209 | 0.4198 | 0.5128 | 0.4851 | 0.6534 | 0.6049 | 0.3063 | 0.5024 | 0.2914 | 0.5232 |
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+ | 10.4375 | 26.0 | 3224 | 13.2106 | 0.3122 | 0.7033 | 0.2326 | 0.2782 | 0.5588 | 0.4092 | 0.2428 | 0.4182 | 0.5147 | 0.4871 | 0.6609 | 0.6 | 0.3474 | 0.5083 | 0.277 | 0.5212 |
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+ | 10.4375 | 27.0 | 3348 | 13.4529 | 0.2912 | 0.6939 | 0.187 | 0.2549 | 0.534 | 0.4787 | 0.2234 | 0.4139 | 0.5027 | 0.4753 | 0.6399 | 0.6366 | 0.289 | 0.4882 | 0.2933 | 0.5173 |
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+ | 10.4375 | 28.0 | 3472 | 13.4314 | 0.2971 | 0.6934 | 0.1873 | 0.2615 | 0.5335 | 0.418 | 0.24 | 0.4137 | 0.5058 | 0.4794 | 0.6314 | 0.6293 | 0.3171 | 0.5053 | 0.2771 | 0.5063 |
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+ | 10.2288 | 29.0 | 3596 | 13.5119 | 0.2888 | 0.6785 | 0.1829 | 0.2527 | 0.5343 | 0.4394 | 0.231 | 0.4143 | 0.5081 | 0.4798 | 0.652 | 0.6171 | 0.3034 | 0.4994 | 0.2741 | 0.5168 |
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+ | 10.2288 | 30.0 | 3720 | 13.5972 | 0.2855 | 0.6767 | 0.1849 | 0.2538 | 0.529 | 0.4643 | 0.2364 | 0.4151 | 0.5133 | 0.489 | 0.633 | 0.6171 | 0.2936 | 0.5065 | 0.2773 | 0.5202 |
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+ | 10.2288 | 31.0 | 3844 | 13.4668 | 0.298 | 0.6943 | 0.1876 | 0.2653 | 0.5326 | 0.4253 | 0.2387 | 0.4162 | 0.5078 | 0.4824 | 0.6337 | 0.6098 | 0.315 | 0.4982 | 0.2811 | 0.5173 |
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+ | 10.2288 | 32.0 | 3968 | 13.4664 | 0.3062 | 0.712 | 0.1937 | 0.2753 | 0.5391 | 0.4017 | 0.2445 | 0.4223 | 0.5163 | 0.4911 | 0.6429 | 0.6146 | 0.3337 | 0.5136 | 0.2788 | 0.5191 |
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+ | 10.013 | 33.0 | 4092 | 13.5583 | 0.2948 | 0.6887 | 0.1853 | 0.2615 | 0.5387 | 0.4264 | 0.243 | 0.4227 | 0.5136 | 0.4856 | 0.6558 | 0.6293 | 0.3106 | 0.513 | 0.2789 | 0.5142 |
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+ | 10.013 | 34.0 | 4216 | 13.6147 | 0.2959 | 0.6873 | 0.1871 | 0.2623 | 0.5351 | 0.4337 | 0.2401 | 0.4189 | 0.5081 | 0.4807 | 0.6481 | 0.6146 | 0.3129 | 0.5089 | 0.2789 | 0.5073 |
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+ | 10.013 | 35.0 | 4340 | 13.6470 | 0.2945 | 0.6755 | 0.1859 | 0.2623 | 0.5389 | 0.4153 | 0.2391 | 0.4184 | 0.5096 | 0.4805 | 0.6599 | 0.6195 | 0.3147 | 0.5089 | 0.2743 | 0.5104 |
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+ | 10.013 | 36.0 | 4464 | 13.5936 | 0.2939 | 0.6818 | 0.1832 | 0.2623 | 0.538 | 0.3559 | 0.2409 | 0.4166 | 0.512 | 0.4839 | 0.6583 | 0.6049 | 0.3113 | 0.5089 | 0.2764 | 0.5152 |
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+ | 9.8455 | 37.0 | 4588 | 13.6458 | 0.2981 | 0.6889 | 0.1828 | 0.265 | 0.536 | 0.4118 | 0.2385 | 0.4208 | 0.5092 | 0.4804 | 0.6582 | 0.6122 | 0.3116 | 0.5101 | 0.2846 | 0.5083 |
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+ | 9.8455 | 38.0 | 4712 | 13.6006 | 0.2994 | 0.6924 | 0.1848 | 0.2681 | 0.5355 | 0.4329 | 0.2435 | 0.4233 | 0.5144 | 0.4868 | 0.6568 | 0.622 | 0.3188 | 0.5166 | 0.28 | 0.5122 |
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+ | 9.8455 | 39.0 | 4836 | 13.6255 | 0.3019 | 0.6929 | 0.201 | 0.2679 | 0.5438 | 0.4629 | 0.2446 | 0.4275 | 0.5138 | 0.4848 | 0.6606 | 0.6415 | 0.3204 | 0.5207 | 0.2833 | 0.5069 |
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+ | 9.8455 | 40.0 | 4960 | 13.5792 | 0.3006 | 0.6973 | 0.186 | 0.2692 | 0.5377 | 0.4263 | 0.2425 | 0.4254 | 0.5135 | 0.4851 | 0.6601 | 0.6146 | 0.3195 | 0.5148 | 0.2817 | 0.5123 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.51.3
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+ - Pytorch 2.7.0+cu126
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+ - Datasets 3.6.0
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+ - Tokenizers 0.21.1
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