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cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epoch

Image Classification
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swin
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End of training

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  1. README.md +177 -0
  2. all_results.json +8 -0
  3. eval_results.json +8 -0
  4. model.safetensors +1 -1
  5. runs/Mar04_10-25-19_e2e-84-59/events.out.tfevents.1709528138.e2e-84-59.768605.2 +2 -2
  6. runs/Mar04_10-25-19_e2e-84-59/events.out.tfevents.1709875342.e2e-84-59.768605.3 +3 -0
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/swin-tiny-patch4-window7-224
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epoch
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: test
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.5815593903514297
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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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+ # cards-top_left_swin-tiny-patch4-window7-224-finetuned-dough_100_epoch
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+
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+ This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0369
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+ - Accuracy: 0.5816
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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: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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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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+ - num_epochs: 100
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Accuracy | Validation Loss |
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+ |:-------------:|:-----:|:------:|:--------:|:---------------:|
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+ | 1.2196 | 1.0 | 1240 | 0.5816 | 1.0369 |
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+ | 1.2491 | 2.0 | 2481 | 0.5752 | 1.0638 |
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+ | 1.2016 | 3.0 | 3721 | 0.5792 | 1.0546 |
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+ | 1.2234 | 4.0 | 4962 | 0.5810 | 1.0560 |
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+ | 1.2298 | 5.0 | 6202 | 0.5725 | 1.0795 |
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+ | 1.287 | 6.0 | 7443 | 0.5731 | 1.0763 |
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+ | 1.2472 | 7.0 | 8683 | 0.5635 | 1.1067 |
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+ | 1.2171 | 8.0 | 9924 | 0.5775 | 1.0671 |
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+ | 1.3164 | 9.0 | 11164 | 0.5701 | 1.0681 |
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+ | 1.3019 | 10.0 | 12405 | 0.5698 | 1.0824 |
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+ | 1.2977 | 11.0 | 13645 | 0.5694 | 1.0721 |
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+ | 1.2587 | 12.0 | 14886 | 0.5704 | 1.0833 |
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+ | 1.2704 | 13.0 | 16126 | 0.5675 | 1.0934 |
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+ | 1.2604 | 14.0 | 17367 | 0.5730 | 1.0739 |
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+ | 1.2834 | 15.0 | 18607 | 0.5524 | 1.1210 |
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+ | 1.2082 | 16.0 | 19848 | 0.5611 | 1.1271 |
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+ | 1.2307 | 17.0 | 21088 | 0.5720 | 1.1013 |
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+ | 1.2136 | 18.0 | 22329 | 0.5753 | 1.1036 |
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+ | 1.2133 | 19.0 | 23569 | 0.5610 | 1.1350 |
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+ | 1.2478 | 20.0 | 24810 | 0.5676 | 1.1256 |
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+ | 1.2006 | 21.0 | 26050 | 0.5682 | 1.1288 |
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+ | 1.1934 | 22.0 | 27291 | 0.5619 | 1.1472 |
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+ | 1.2136 | 23.0 | 28531 | 0.5713 | 1.1304 |
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+ | 1.2449 | 24.0 | 29772 | 0.5581 | 1.1893 |
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+ | 1.1968 | 25.0 | 31012 | 0.5633 | 1.1754 |
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+ | 1.1582 | 26.0 | 32253 | 0.5651 | 1.1735 |
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+ | 1.1404 | 27.0 | 33493 | 0.5642 | 1.1752 |
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+ | 1.2011 | 28.0 | 34734 | 0.5538 | 1.2227 |
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+ | 1.1223 | 29.0 | 35974 | 0.5578 | 1.2200 |
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+ | 1.1427 | 30.0 | 37215 | 0.5608 | 1.2028 |
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+ | 1.1751 | 31.0 | 38455 | 0.5635 | 1.2253 |
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+ | 1.1012 | 32.0 | 39696 | 0.5543 | 1.2473 |
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+ | 1.0912 | 33.0 | 40936 | 0.5673 | 1.2370 |
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+ | 1.1085 | 34.0 | 42177 | 0.5534 | 1.2838 |
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+ | 1.099 | 35.0 | 43417 | 0.5526 | 1.2760 |
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+ | 1.1092 | 36.0 | 44658 | 0.5547 | 1.2769 |
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+ | 1.0655 | 37.0 | 45898 | 0.5534 | 1.3178 |
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+ | 1.0861 | 38.0 | 47139 | 0.5585 | 1.2943 |
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+ | 1.0917 | 39.0 | 48379 | 0.5518 | 1.3659 |
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+ | 1.0791 | 40.0 | 49620 | 0.5541 | 1.3413 |
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+ | 1.0356 | 41.0 | 50860 | 0.5495 | 1.3567 |
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+ | 1.0394 | 42.0 | 52101 | 0.5491 | 1.3648 |
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+ | 1.0096 | 43.0 | 53341 | 0.5574 | 1.3671 |
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+ | 1.0736 | 44.0 | 54582 | 0.5468 | 1.4142 |
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+ | 1.0145 | 45.0 | 55822 | 0.5462 | 1.4340 |
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+ | 1.0437 | 46.0 | 57063 | 0.5442 | 1.4734 |
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+ | 0.9771 | 47.0 | 58303 | 0.5446 | 1.4496 |
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+ | 0.9758 | 48.0 | 59544 | 0.5397 | 1.5071 |
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+ | 1.0199 | 49.0 | 60784 | 0.5437 | 1.5119 |
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+ | 0.9898 | 50.0 | 62025 | 0.5428 | 1.5066 |
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+ | 1.0139 | 51.0 | 63265 | 0.5375 | 1.5314 |
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+ | 1.0035 | 52.0 | 64506 | 0.5427 | 1.5604 |
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+ | 0.9786 | 53.0 | 65746 | 0.5396 | 1.5899 |
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+ | 0.9768 | 54.0 | 66987 | 0.5449 | 1.5642 |
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+ | 0.968 | 55.0 | 68227 | 0.5394 | 1.6056 |
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+ | 0.9254 | 56.0 | 69468 | 0.5380 | 1.6091 |
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+ | 0.9764 | 57.0 | 70680 | 0.5340 | 1.6646 |
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+ | 0.8998 | 58.0 | 71921 | 0.5323 | 1.6692 |
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+ | 0.9592 | 59.0 | 73161 | 0.5353 | 1.6395 |
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+ | 0.8722 | 60.0 | 74402 | 0.5393 | 1.6702 |
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+ | 0.888 | 61.0 | 75642 | 0.5336 | 1.6771 |
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+ | 0.872 | 62.0 | 76883 | 0.5331 | 1.6873 |
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+ | 0.9133 | 63.0 | 78123 | 0.5325 | 1.7182 |
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+ | 0.8815 | 64.0 | 79364 | 0.5310 | 1.7375 |
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+ | 0.9144 | 65.0 | 80604 | 0.5337 | 1.7263 |
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+ | 0.8712 | 66.0 | 81845 | 0.5284 | 1.7628 |
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+ | 0.8576 | 67.0 | 83080 | 1.7786 | 0.5322 |
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+ | 0.8677 | 68.0 | 84321 | 1.7947 | 0.5327 |
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+ | 0.8448 | 69.0 | 85561 | 1.8100 | 0.5314 |
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+ | 0.8102 | 70.0 | 86802 | 1.8256 | 0.5313 |
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+ | 0.8438 | 71.0 | 88042 | 1.8325 | 0.5273 |
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+ | 0.8015 | 72.0 | 89283 | 1.8564 | 0.5311 |
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+ | 0.8025 | 73.0 | 90523 | 1.8451 | 0.5342 |
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+ | 0.8295 | 74.0 | 91764 | 1.8748 | 0.5305 |
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+ | 0.8101 | 75.0 | 93004 | 1.8884 | 0.5297 |
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+ | 0.7883 | 76.0 | 94245 | 1.8777 | 0.5297 |
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+ | 0.7989 | 77.0 | 95485 | 1.9185 | 0.5262 |
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+ | 0.7791 | 78.0 | 96726 | 1.9436 | 0.5246 |
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+ | 0.7197 | 79.0 | 97966 | 1.9615 | 0.5222 |
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+ | 0.7639 | 80.0 | 99207 | 1.9567 | 0.5213 |
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+ | 0.7922 | 81.0 | 100447 | 1.9746 | 0.5248 |
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+ | 0.7874 | 82.0 | 101688 | 1.9960 | 0.5206 |
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+ | 0.8155 | 83.0 | 102928 | 2.0131 | 0.5211 |
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+ | 0.7791 | 84.0 | 104169 | 2.0559 | 0.5196 |
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+ | 0.7731 | 85.0 | 105409 | 2.0255 | 0.5192 |
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+ | 0.8018 | 86.0 | 106650 | 2.0784 | 0.5216 |
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+ | 0.777 | 87.0 | 107890 | 2.0482 | 0.5224 |
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+ | 0.7637 | 88.0 | 109131 | 2.0889 | 0.5201 |
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+ | 0.7783 | 89.0 | 110371 | 2.0663 | 0.5222 |
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+ | 0.7156 | 90.0 | 111612 | 2.0884 | 0.5200 |
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+ | 0.702 | 91.0 | 112852 | 2.1034 | 0.5215 |
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+ | 0.7136 | 92.0 | 114093 | 2.1380 | 0.5164 |
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+ | 0.6889 | 93.0 | 115333 | 2.1321 | 0.5198 |
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+ | 0.7117 | 94.0 | 116574 | 2.1175 | 0.5186 |
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+ | 0.6903 | 95.0 | 117814 | 2.1155 | 0.5187 |
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+ | 0.7334 | 96.0 | 119055 | 2.1197 | 0.5200 |
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+ | 0.6684 | 97.0 | 120295 | 2.1435 | 0.5192 |
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+ | 0.7471 | 98.0 | 121536 | 2.1403 | 0.5196 |
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+ | 0.7197 | 99.0 | 122776 | 2.1465 | 0.5182 |
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+ | 0.7026 | 99.99 | 124000 | 2.1492 | 0.5186 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.37.2
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.17.0
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+ - Tokenizers 0.15.2
all_results.json ADDED
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+ {
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+ "epoch": 99.99,
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+ "eval_accuracy": 0.5815593903514297,
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+ "eval_loss": 1.0368633270263672,
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+ "eval_runtime": 157.5169,
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+ "eval_samples_per_second": 252.005,
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+ "eval_steps_per_second": 7.879
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+ }
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+ {
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+ "epoch": 99.99,
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+ "eval_accuracy": 0.5815593903514297,
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+ "eval_loss": 1.0368633270263672,
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+ "eval_runtime": 157.5169,
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+ "eval_samples_per_second": 252.005,
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+ "eval_steps_per_second": 7.879
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+ }
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