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reward-model

Text Classification
Transformers
Safetensors
qwen2
trl
reward-trainer
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Community
lewtun HF staff commited on
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End of training

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  1. README.md +21 -98
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,8 +20,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5182
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- - Accuracy: 0.73
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  ## Model description
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@@ -56,102 +56,25 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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- | 0.7794 | 0.0103 | 10 | 0.6609 | 0.603 |
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- | 0.6484 | 0.0206 | 20 | 0.6517 | 0.639 |
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- | 0.6565 | 0.0310 | 30 | 0.6356 | 0.626 |
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- | 0.6522 | 0.0413 | 40 | 0.6274 | 0.649 |
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- | 0.6354 | 0.0516 | 50 | 0.6114 | 0.665 |
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- | 0.6337 | 0.0619 | 60 | 0.6120 | 0.674 |
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- | 0.6133 | 0.0722 | 70 | 0.5871 | 0.677 |
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- | 0.6075 | 0.0826 | 80 | 0.5823 | 0.685 |
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- | 0.5795 | 0.0929 | 90 | 0.5812 | 0.686 |
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- | 0.585 | 0.1032 | 100 | 0.5759 | 0.691 |
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- | 0.5664 | 0.1135 | 110 | 0.5713 | 0.687 |
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- | 0.5772 | 0.1238 | 120 | 0.5619 | 0.692 |
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- | 0.5762 | 0.1342 | 130 | 0.5701 | 0.701 |
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- | 0.5726 | 0.1445 | 140 | 0.5550 | 0.707 |
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- | 0.5889 | 0.1548 | 150 | 0.5864 | 0.678 |
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- | 0.5758 | 0.1651 | 160 | 0.5623 | 0.702 |
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- | 0.5721 | 0.1754 | 170 | 0.5510 | 0.708 |
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- | 0.5595 | 0.1858 | 180 | 0.5458 | 0.707 |
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- | 0.5601 | 0.1961 | 190 | 0.5531 | 0.709 |
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- | 0.5646 | 0.2064 | 200 | 0.5473 | 0.702 |
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- | 0.5467 | 0.2167 | 210 | 0.5586 | 0.704 |
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- | 0.5558 | 0.2270 | 220 | 0.5539 | 0.7 |
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- | 0.5457 | 0.2374 | 230 | 0.5744 | 0.686 |
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- | 0.5848 | 0.2477 | 240 | 0.5571 | 0.712 |
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- | 0.5478 | 0.2580 | 250 | 0.5432 | 0.701 |
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- | 0.5461 | 0.2683 | 260 | 0.5447 | 0.708 |
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- | 0.5607 | 0.2786 | 270 | 0.5433 | 0.711 |
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- | 0.5886 | 0.2890 | 280 | 0.5378 | 0.716 |
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- | 0.5616 | 0.2993 | 290 | 0.5354 | 0.724 |
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- | 0.5426 | 0.3096 | 300 | 0.5327 | 0.722 |
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- | 0.5553 | 0.3199 | 310 | 0.5358 | 0.728 |
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- | 0.5335 | 0.3302 | 320 | 0.5259 | 0.732 |
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- | 0.5354 | 0.3406 | 330 | 0.5277 | 0.725 |
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- | 0.5437 | 0.3509 | 340 | 0.5344 | 0.727 |
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- | 0.5317 | 0.3612 | 350 | 0.5372 | 0.718 |
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- | 0.5609 | 0.3715 | 360 | 0.5422 | 0.715 |
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- | 0.5522 | 0.3818 | 370 | 0.5349 | 0.721 |
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- | 0.5714 | 0.3922 | 380 | 0.5329 | 0.728 |
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- | 0.5099 | 0.4025 | 390 | 0.5296 | 0.719 |
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- | 0.5148 | 0.4128 | 400 | 0.5337 | 0.721 |
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- | 0.5366 | 0.4231 | 410 | 0.5308 | 0.733 |
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- | 0.5432 | 0.4334 | 420 | 0.5270 | 0.733 |
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- | 0.5431 | 0.4438 | 430 | 0.5318 | 0.725 |
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- | 0.5531 | 0.4541 | 440 | 0.5364 | 0.728 |
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- | 0.5585 | 0.4644 | 450 | 0.5436 | 0.731 |
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- | 0.5275 | 0.4747 | 460 | 0.5348 | 0.721 |
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- | 0.553 | 0.4850 | 470 | 0.5315 | 0.728 |
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- | 0.5152 | 0.4954 | 480 | 0.5309 | 0.721 |
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- | 0.5405 | 0.5057 | 490 | 0.5332 | 0.721 |
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- | 0.5089 | 0.5160 | 500 | 0.5339 | 0.727 |
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- | 0.5334 | 0.5263 | 510 | 0.5321 | 0.725 |
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- | 0.535 | 0.5366 | 520 | 0.5342 | 0.719 |
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- | 0.5228 | 0.5470 | 530 | 0.5327 | 0.725 |
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- | 0.5384 | 0.5573 | 540 | 0.5316 | 0.713 |
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- | 0.5306 | 0.5676 | 550 | 0.5274 | 0.728 |
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- | 0.5183 | 0.5779 | 560 | 0.5230 | 0.724 |
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- | 0.5205 | 0.5882 | 570 | 0.5259 | 0.723 |
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- | 0.5152 | 0.5986 | 580 | 0.5282 | 0.718 |
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- | 0.5266 | 0.6089 | 590 | 0.5236 | 0.716 |
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- | 0.5141 | 0.6192 | 600 | 0.5223 | 0.721 |
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- | 0.5527 | 0.6295 | 610 | 0.5169 | 0.727 |
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- | 0.5037 | 0.6398 | 620 | 0.5172 | 0.733 |
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- | 0.5305 | 0.6502 | 630 | 0.5174 | 0.733 |
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- | 0.5277 | 0.6605 | 640 | 0.5217 | 0.724 |
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- | 0.5157 | 0.6708 | 650 | 0.5191 | 0.725 |
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- | 0.4997 | 0.6811 | 660 | 0.5216 | 0.723 |
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- | 0.5298 | 0.6914 | 670 | 0.5262 | 0.716 |
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- | 0.509 | 0.7018 | 680 | 0.5245 | 0.723 |
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- | 0.5216 | 0.7121 | 690 | 0.5199 | 0.727 |
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- | 0.5012 | 0.7224 | 700 | 0.5175 | 0.724 |
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- | 0.544 | 0.7327 | 710 | 0.5200 | 0.722 |
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- | 0.513 | 0.7430 | 720 | 0.5202 | 0.723 |
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- | 0.5153 | 0.7534 | 730 | 0.5220 | 0.721 |
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- | 0.5065 | 0.7637 | 740 | 0.5232 | 0.721 |
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- | 0.5291 | 0.7740 | 750 | 0.5230 | 0.721 |
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- | 0.5506 | 0.7843 | 760 | 0.5203 | 0.717 |
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- | 0.517 | 0.7946 | 770 | 0.5211 | 0.719 |
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- | 0.5102 | 0.8050 | 780 | 0.5212 | 0.718 |
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- | 0.4861 | 0.8153 | 790 | 0.5225 | 0.715 |
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- | 0.5407 | 0.8256 | 800 | 0.5236 | 0.718 |
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- | 0.5067 | 0.8359 | 810 | 0.5254 | 0.718 |
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- | 0.5207 | 0.8462 | 820 | 0.5269 | 0.718 |
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- | 0.5134 | 0.8566 | 830 | 0.5256 | 0.723 |
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- | 0.5116 | 0.8669 | 840 | 0.5241 | 0.724 |
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- | 0.5256 | 0.8772 | 850 | 0.5235 | 0.723 |
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- | 0.5233 | 0.8875 | 860 | 0.5222 | 0.727 |
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- | 0.4895 | 0.8978 | 870 | 0.5194 | 0.727 |
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- | 0.4877 | 0.9082 | 880 | 0.5185 | 0.725 |
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- | 0.5299 | 0.9185 | 890 | 0.5194 | 0.725 |
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- | 0.4815 | 0.9288 | 900 | 0.5196 | 0.725 |
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- | 0.477 | 0.9391 | 910 | 0.5204 | 0.728 |
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- | 0.5368 | 0.9494 | 920 | 0.5199 | 0.73 |
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- | 0.5133 | 0.9598 | 930 | 0.5184 | 0.733 |
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- | 0.4974 | 0.9701 | 940 | 0.5183 | 0.731 |
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- | 0.4948 | 0.9804 | 950 | 0.5183 | 0.728 |
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- | 0.5217 | 0.9907 | 960 | 0.5182 | 0.73 |
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  ### Framework versions
 
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21
  This model is a fine-tuned version of [Qwen/Qwen2-0.5B](https://huggingface.co/Qwen/Qwen2-0.5B) on an unknown dataset.
22
  It achieves the following results on the evaluation set:
23
+ - Loss: 0.5217
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+ - Accuracy: 0.727
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26
  ## Model description
27
 
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|
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+ | 0.636 | 0.0516 | 50 | 0.6010 | 0.688 |
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+ | 0.5793 | 0.1032 | 100 | 0.5676 | 0.703 |
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+ | 0.5807 | 0.1548 | 150 | 0.5732 | 0.705 |
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+ | 0.5572 | 0.2064 | 200 | 0.5513 | 0.706 |
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+ | 0.5695 | 0.2580 | 250 | 0.5472 | 0.718 |
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+ | 0.5596 | 0.3096 | 300 | 0.5283 | 0.723 |
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+ | 0.54 | 0.3612 | 350 | 0.5445 | 0.715 |
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+ | 0.5291 | 0.4128 | 400 | 0.5387 | 0.722 |
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+ | 0.539 | 0.4644 | 450 | 0.5461 | 0.726 |
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+ | 0.5248 | 0.5160 | 500 | 0.5402 | 0.724 |
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+ | 0.5263 | 0.5676 | 550 | 0.5271 | 0.726 |
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+ | 0.5222 | 0.6192 | 600 | 0.5238 | 0.724 |
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+ | 0.5259 | 0.6708 | 650 | 0.5200 | 0.728 |
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+ | 0.5118 | 0.7224 | 700 | 0.5190 | 0.728 |
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+ | 0.513 | 0.7740 | 750 | 0.5213 | 0.731 |
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+ | 0.5141 | 0.8256 | 800 | 0.5253 | 0.729 |
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+ | 0.5197 | 0.8772 | 850 | 0.5256 | 0.724 |
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+ | 0.4968 | 0.9288 | 900 | 0.5231 | 0.726 |
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+ | 0.4983 | 0.9804 | 950 | 0.5217 | 0.727 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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