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---
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license: apache-2.0
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base_model: microsoft/swin-base-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: swin-base-patch4-window7-224-finetuned-st-wsdmhar
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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: train
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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: 0.7937443336355394
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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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# swin-base-patch4-window7-224-finetuned-st-wsdmhar
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This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-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: 0.8737
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- Accuracy: 0.7937
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|
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| 1.7172 | 0.9938 | 40 | 1.6300 | 0.3908 |
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| 1.2025 | 1.9876 | 80 | 1.0530 | 0.5290 |
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| 0.9391 | 2.9814 | 120 | 0.8234 | 0.5997 |
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| 0.858 | 4.0 | 161 | 0.7816 | 0.6179 |
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| 0.7816 | 4.9938 | 201 | 0.7162 | 0.6328 |
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| 0.7094 | 5.9876 | 241 | 0.6275 | 0.6936 |
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| 0.7139 | 6.9814 | 281 | 0.5860 | 0.7203 |
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| 0.6836 | 8.0 | 322 | 0.6788 | 0.6777 |
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| 0.7075 | 8.9938 | 362 | 0.6239 | 0.7135 |
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| 0.6309 | 9.9876 | 402 | 0.6282 | 0.6995 |
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| 0.6596 | 10.9814 | 442 | 0.6014 | 0.6995 |
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| 0.6589 | 12.0 | 483 | 0.5762 | 0.7208 |
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| 0.5947 | 12.9938 | 523 | 0.5150 | 0.7588 |
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| 0.5929 | 13.9876 | 563 | 0.5373 | 0.7403 |
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| 0.5775 | 14.9814 | 603 | 0.5327 | 0.7584 |
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| 0.5465 | 16.0 | 644 | 0.5778 | 0.7421 |
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| 0.5614 | 16.9938 | 684 | 0.4826 | 0.7747 |
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| 0.5535 | 17.9876 | 724 | 0.4898 | 0.7788 |
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| 0.4965 | 18.9814 | 764 | 0.5204 | 0.7539 |
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| 0.5177 | 20.0 | 805 | 0.4869 | 0.7693 |
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| 0.513 | 20.9938 | 845 | 0.4848 | 0.7797 |
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| 0.4824 | 21.9876 | 885 | 0.4821 | 0.7788 |
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| 0.5241 | 22.9814 | 925 | 0.4995 | 0.7715 |
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| 0.4363 | 24.0 | 966 | 0.5120 | 0.7806 |
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| 0.4549 | 24.9938 | 1006 | 0.5031 | 0.7811 |
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| 0.4445 | 25.9876 | 1046 | 0.5340 | 0.7665 |
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| 0.4337 | 26.9814 | 1086 | 0.5099 | 0.7783 |
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| 0.4272 | 28.0 | 1127 | 0.5166 | 0.7824 |
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| 0.3732 | 28.9938 | 1167 | 0.5644 | 0.7620 |
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| 0.4252 | 29.9876 | 1207 | 0.5171 | 0.7856 |
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| 0.3844 | 30.9814 | 1247 | 0.5678 | 0.7756 |
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| 0.3663 | 32.0 | 1288 | 0.5667 | 0.7743 |
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| 0.3456 | 32.9938 | 1328 | 0.5701 | 0.7688 |
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| 0.3809 | 33.9876 | 1368 | 0.5687 | 0.7729 |
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| 0.3499 | 34.9814 | 1408 | 0.5615 | 0.7820 |
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| 0.3443 | 36.0 | 1449 | 0.5977 | 0.7806 |
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| 0.2993 | 36.9938 | 1489 | 0.6292 | 0.7806 |
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| 0.3333 | 37.9876 | 1529 | 0.6492 | 0.7715 |
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| 0.3586 | 38.9814 | 1569 | 0.6130 | 0.7756 |
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| 0.2979 | 40.0 | 1610 | 0.5870 | 0.7806 |
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| 0.266 | 40.9938 | 1650 | 0.6225 | 0.7833 |
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| 0.2585 | 41.9876 | 1690 | 0.6603 | 0.7765 |
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| 0.2741 | 42.9814 | 1730 | 0.6642 | 0.7752 |
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| 0.2674 | 44.0 | 1771 | 0.6706 | 0.7851 |
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| 0.2619 | 44.9938 | 1811 | 0.6730 | 0.7715 |
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| 0.252 | 45.9876 | 1851 | 0.7346 | 0.7811 |
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| 0.2417 | 46.9814 | 1891 | 0.6707 | 0.7829 |
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| 0.2406 | 48.0 | 1932 | 0.6497 | 0.7833 |
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| 0.2348 | 48.9938 | 1972 | 0.6786 | 0.7833 |
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| 0.2265 | 49.9876 | 2012 | 0.7158 | 0.7906 |
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| 0.1967 | 50.9814 | 2052 | 0.7403 | 0.7947 |
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| 0.1932 | 52.0 | 2093 | 0.7282 | 0.7869 |
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| 0.2047 | 52.9938 | 2133 | 0.6987 | 0.7842 |
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| 0.1966 | 53.9876 | 2173 | 0.7779 | 0.7851 |
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| 0.1824 | 54.9814 | 2213 | 0.7815 | 0.7910 |
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| 0.1963 | 56.0 | 2254 | 0.6768 | 0.7933 |
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| 0.1984 | 56.9938 | 2294 | 0.7527 | 0.7833 |
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| 0.1777 | 57.9876 | 2334 | 0.7672 | 0.7865 |
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| 0.1666 | 58.9814 | 2374 | 0.7881 | 0.7901 |
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| 0.1649 | 60.0 | 2415 | 0.7903 | 0.7856 |
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| 0.1785 | 60.9938 | 2455 | 0.7483 | 0.7874 |
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| 0.167 | 61.9876 | 2495 | 0.7278 | 0.7915 |
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| 0.1514 | 62.9814 | 2535 | 0.8130 | 0.7761 |
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| 0.1423 | 64.0 | 2576 | 0.8144 | 0.7829 |
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| 0.1476 | 64.9938 | 2616 | 0.8000 | 0.7815 |
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| 0.1742 | 65.9876 | 2656 | 0.7660 | 0.7815 |
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| 0.1362 | 66.9814 | 2696 | 0.8117 | 0.7888 |
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| 0.126 | 68.0 | 2737 | 0.8394 | 0.7874 |
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| 0.1278 | 68.9938 | 2777 | 0.8493 | 0.7847 |
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| 0.1181 | 69.9876 | 2817 | 0.7959 | 0.7937 |
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| 0.1457 | 70.9814 | 2857 | 0.8036 | 0.7860 |
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| 0.1322 | 72.0 | 2898 | 0.8474 | 0.8001 |
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| 0.1312 | 72.9938 | 2938 | 0.8026 | 0.7774 |
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| 0.1146 | 73.9876 | 2978 | 0.8388 | 0.8064 |
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| 0.141 | 74.9814 | 3018 | 0.8053 | 0.7987 |
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| 0.1396 | 76.0 | 3059 | 0.8439 | 0.7937 |
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| 0.113 | 76.9938 | 3099 | 0.9004 | 0.7919 |
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| 0.1219 | 77.9876 | 3139 | 0.8423 | 0.7951 |
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| 0.1132 | 78.9814 | 3179 | 0.8309 | 0.7937 |
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| 0.119 | 80.0 | 3220 | 0.8210 | 0.8015 |
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| 0.111 | 80.9938 | 3260 | 0.8238 | 0.7983 |
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| 0.0973 | 81.9876 | 3300 | 0.8422 | 0.7983 |
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| 0.1118 | 82.9814 | 3340 | 0.8389 | 0.8010 |
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| 0.1296 | 84.0 | 3381 | 0.8178 | 0.8019 |
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| 0.089 | 84.9938 | 3421 | 0.8456 | 0.7987 |
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| 0.1003 | 85.9876 | 3461 | 0.8626 | 0.8001 |
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| 0.1123 | 86.9814 | 3501 | 0.8494 | 0.7928 |
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| 0.1038 | 88.0 | 3542 | 0.8584 | 0.8064 |
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| 0.1055 | 88.9938 | 3582 | 0.8513 | 0.7933 |
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| 0.1031 | 89.9876 | 3622 | 0.8592 | 0.7978 |
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| 0.1028 | 90.9814 | 3662 | 0.8452 | 0.7969 |
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| 0.0998 | 92.0 | 3703 | 0.8605 | 0.7983 |
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| 0.1005 | 92.9938 | 3743 | 0.8805 | 0.7947 |
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| 0.0936 | 93.9876 | 3783 | 0.8735 | 0.7969 |
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| 0.0779 | 94.9814 | 3823 | 0.8776 | 0.7960 |
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| 0.0972 | 96.0 | 3864 | 0.8784 | 0.7951 |
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| 0.0973 | 96.9938 | 3904 | 0.8782 | 0.7933 |
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| 0.0932 | 97.9876 | 3944 | 0.8779 | 0.7924 |
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| 0.0863 | 98.9814 | 3984 | 0.8741 | 0.7919 |
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| 0.0827 | 99.3789 | 4000 | 0.8737 | 0.7937 |
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### Framework versions
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- Transformers 4.44.0
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- Pytorch 1.12.1+cu113
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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