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deit_fold_3_v3

Image Classification
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End of training Fold 3

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  2. model.safetensors +1 -1
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: facebook/deit-small-patch16-224
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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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+ - recall
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+ model-index:
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+ - name: deit_fold_3_v3
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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: None
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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: 0.9775641025641025
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+ - name: Recall
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+ type: recall
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+ value: 0.9782603526485221
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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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+ # deit_fold_3_v3
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+
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+ This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0615
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+ - Accuracy: 0.9776
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+ - F1 Score: 0.9779
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+ - Recall: 0.9783
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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: 1e-05
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+ - train_batch_size: 64
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 150
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+ - num_epochs: 100
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+ - label_smoothing_factor: 0.15
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:------:|
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+ | 2.8067 | 1.0 | 20 | 2.8042 | 0.2308 | 0.1857 | 0.2025 |
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+ | 2.6818 | 2.0 | 40 | 2.6787 | 0.3301 | 0.2595 | 0.2840 |
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+ | 2.4775 | 3.0 | 60 | 2.4942 | 0.4744 | 0.4403 | 0.4350 |
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+ | 2.1304 | 4.0 | 80 | 2.1863 | 0.6571 | 0.6641 | 0.6486 |
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+ | 1.7918 | 5.0 | 100 | 1.8476 | 0.8045 | 0.8103 | 0.8106 |
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+ | 1.5272 | 6.0 | 120 | 1.5614 | 0.8590 | 0.8606 | 0.8641 |
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+ | 1.3652 | 7.0 | 140 | 1.3730 | 0.9199 | 0.9191 | 0.9261 |
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+ | 1.2514 | 8.0 | 160 | 1.2684 | 0.9263 | 0.9257 | 0.9337 |
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+ | 1.1792 | 9.0 | 180 | 1.2029 | 0.9551 | 0.9547 | 0.9597 |
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+ | 1.1409 | 10.0 | 200 | 1.1749 | 0.9551 | 0.9552 | 0.9597 |
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+ | 1.0983 | 11.0 | 220 | 1.1696 | 0.9583 | 0.9587 | 0.9634 |
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+ | 1.1016 | 12.0 | 240 | 1.1319 | 0.9647 | 0.9644 | 0.9677 |
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+ | 1.0771 | 13.0 | 260 | 1.1243 | 0.9647 | 0.9644 | 0.9677 |
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+ | 1.0477 | 14.0 | 280 | 1.1087 | 0.9615 | 0.9608 | 0.9627 |
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+ | 1.0469 | 15.0 | 300 | 1.1094 | 0.9551 | 0.9544 | 0.9552 |
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+ | 1.0373 | 16.0 | 320 | 1.1093 | 0.9487 | 0.9486 | 0.9516 |
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+ | 1.0312 | 17.0 | 340 | 1.1033 | 0.9551 | 0.9544 | 0.9552 |
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+ | 1.0166 | 18.0 | 360 | 1.0903 | 0.9615 | 0.9607 | 0.9614 |
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+ | 1.0146 | 19.0 | 380 | 1.0961 | 0.9679 | 0.9672 | 0.9676 |
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+ | 1.0268 | 20.0 | 400 | 1.0846 | 0.9712 | 0.9709 | 0.9720 |
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+ | 1.0042 | 21.0 | 420 | 1.0741 | 0.9712 | 0.9717 | 0.9727 |
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+ | 1.0118 | 22.0 | 440 | 1.0857 | 0.9679 | 0.9688 | 0.9715 |
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+ | 0.9751 | 23.0 | 460 | 1.0877 | 0.9712 | 0.9717 | 0.9727 |
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+ | 0.9916 | 24.0 | 480 | 1.0910 | 0.9647 | 0.9650 | 0.9658 |
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+ | 0.9838 | 25.0 | 500 | 1.0876 | 0.9647 | 0.9647 | 0.9652 |
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+ | 0.9865 | 26.0 | 520 | 1.0825 | 0.9647 | 0.9647 | 0.9652 |
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+ | 0.9818 | 27.0 | 540 | 1.0722 | 0.9712 | 0.9710 | 0.9714 |
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+ | 0.9899 | 28.0 | 560 | 1.0736 | 0.9744 | 0.9746 | 0.9764 |
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+ | 0.9816 | 29.0 | 580 | 1.0659 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9795 | 30.0 | 600 | 1.0697 | 0.9712 | 0.9714 | 0.9714 |
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+ | 0.9688 | 31.0 | 620 | 1.0696 | 0.9712 | 0.9714 | 0.9714 |
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+ | 0.9783 | 32.0 | 640 | 1.0826 | 0.9712 | 0.9714 | 0.9714 |
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+ | 0.9596 | 33.0 | 660 | 1.0759 | 0.9679 | 0.9687 | 0.9702 |
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+ | 0.9724 | 34.0 | 680 | 1.0751 | 0.9647 | 0.9651 | 0.9652 |
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+ | 0.9837 | 35.0 | 700 | 1.0623 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9736 | 36.0 | 720 | 1.0704 | 0.9679 | 0.9674 | 0.9683 |
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+ | 0.9763 | 37.0 | 740 | 1.0599 | 0.9712 | 0.9717 | 0.9727 |
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+ | 0.9683 | 38.0 | 760 | 1.0733 | 0.9679 | 0.9687 | 0.9702 |
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+ | 0.9641 | 39.0 | 780 | 1.0615 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9677 | 40.0 | 800 | 1.0642 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9718 | 41.0 | 820 | 1.0638 | 0.9744 | 0.9744 | 0.9745 |
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+ | 0.9649 | 42.0 | 840 | 1.0689 | 0.9712 | 0.9716 | 0.9721 |
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+ | 0.9724 | 43.0 | 860 | 1.0657 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9703 | 44.0 | 880 | 1.0595 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9675 | 45.0 | 900 | 1.0616 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9737 | 46.0 | 920 | 1.0590 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9640 | 47.0 | 940 | 1.0640 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9640 | 48.0 | 960 | 1.0613 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9581 | 49.0 | 980 | 1.0638 | 0.9744 | 0.9744 | 0.9745 |
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+ | 0.9784 | 50.0 | 1000 | 1.0627 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9603 | 51.0 | 1020 | 1.0665 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9601 | 52.0 | 1040 | 1.0627 | 0.9744 | 0.9748 | 0.9764 |
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+ | 0.9693 | 53.0 | 1060 | 1.0637 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9636 | 54.0 | 1080 | 1.0576 | 0.9744 | 0.9751 | 0.9758 |
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+ | 0.9686 | 55.0 | 1100 | 1.0593 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9585 | 56.0 | 1120 | 1.0593 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9615 | 57.0 | 1140 | 1.0586 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9671 | 58.0 | 1160 | 1.0549 | 0.9744 | 0.9751 | 0.9758 |
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+ | 0.9652 | 59.0 | 1180 | 1.0562 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9651 | 60.0 | 1200 | 1.0559 | 0.9744 | 0.9748 | 0.9764 |
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+ | 0.9645 | 61.0 | 1220 | 1.0538 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9570 | 62.0 | 1240 | 1.0575 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9532 | 63.0 | 1260 | 1.0546 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9587 | 64.0 | 1280 | 1.0525 | 0.9776 | 0.9779 | 0.9783 |
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+ | 0.9541 | 65.0 | 1300 | 1.0583 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9571 | 66.0 | 1320 | 1.0579 | 0.9744 | 0.9745 | 0.9751 |
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+ | 0.9548 | 67.0 | 1340 | 1.0539 | 0.9712 | 0.9717 | 0.9727 |
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+ | 0.9559 | 68.0 | 1360 | 1.0544 | 0.9712 | 0.9717 | 0.9727 |
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+ | 0.9594 | 69.0 | 1380 | 1.0542 | 0.9744 | 0.9751 | 0.9758 |
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+ | 0.9594 | 70.0 | 1400 | 1.0559 | 0.9744 | 0.9751 | 0.9758 |
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+ | 0.9599 | 71.0 | 1420 | 1.0551 | 0.9712 | 0.9717 | 0.9727 |
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+ | 0.9614 | 72.0 | 1440 | 1.0526 | 0.9712 | 0.9717 | 0.9727 |
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+ | 0.9568 | 73.0 | 1460 | 1.0535 | 0.9712 | 0.9717 | 0.9727 |
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+ | 0.9561 | 74.0 | 1480 | 1.0546 | 0.9712 | 0.9717 | 0.9727 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 5.0.0
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+ - Pytorch 2.10.0+cu128
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+ - Datasets 5.0.0
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+ - Tokenizers 0.22.2
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