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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: swin-tiny-patch4-window7-224-finetuned-ADC-3cls-0922 |
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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: 0.8285714285714286 |
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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-tiny-patch4-window7-224-finetuned-ADC-3cls-0922 |
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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: 0.6771 |
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- Accuracy: 0.8286 |
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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: 0.0001 |
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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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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 0.2 |
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- num_epochs: 200 |
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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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| No log | 1.0 | 2 | 0.6875 | 0.8143 | |
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| No log | 2.0 | 4 | 0.6874 | 0.8143 | |
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| No log | 3.0 | 6 | 0.6873 | 0.8143 | |
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| No log | 4.0 | 8 | 0.6871 | 0.8143 | |
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| 0.7555 | 5.0 | 10 | 0.6869 | 0.8143 | |
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| 0.7555 | 6.0 | 12 | 0.6866 | 0.8143 | |
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| 0.7555 | 7.0 | 14 | 0.6862 | 0.8143 | |
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| 0.7555 | 8.0 | 16 | 0.6858 | 0.8143 | |
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| 0.7555 | 9.0 | 18 | 0.6853 | 0.8143 | |
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| 0.7576 | 10.0 | 20 | 0.6848 | 0.8143 | |
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| 0.7576 | 11.0 | 22 | 0.6842 | 0.8143 | |
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| 0.7576 | 12.0 | 24 | 0.6836 | 0.8143 | |
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| 0.7576 | 13.0 | 26 | 0.6830 | 0.8143 | |
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| 0.7576 | 14.0 | 28 | 0.6823 | 0.8143 | |
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| 0.769 | 15.0 | 30 | 0.6816 | 0.8 | |
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| 0.769 | 16.0 | 32 | 0.6808 | 0.8 | |
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| 0.769 | 17.0 | 34 | 0.6800 | 0.8143 | |
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| 0.769 | 18.0 | 36 | 0.6791 | 0.8143 | |
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| 0.769 | 19.0 | 38 | 0.6781 | 0.8143 | |
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| 0.7564 | 20.0 | 40 | 0.6771 | 0.8286 | |
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| 0.7564 | 21.0 | 42 | 0.6760 | 0.8143 | |
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| 0.7564 | 22.0 | 44 | 0.6748 | 0.8143 | |
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| 0.7564 | 23.0 | 46 | 0.6737 | 0.8 | |
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| 0.7564 | 24.0 | 48 | 0.6725 | 0.8 | |
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| 0.7508 | 25.0 | 50 | 0.6713 | 0.8143 | |
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| 0.7508 | 26.0 | 52 | 0.6701 | 0.8143 | |
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| 0.7508 | 27.0 | 54 | 0.6689 | 0.8143 | |
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| 0.7508 | 28.0 | 56 | 0.6674 | 0.8143 | |
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| 0.7508 | 29.0 | 58 | 0.6660 | 0.8143 | |
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| 0.747 | 30.0 | 60 | 0.6646 | 0.8143 | |
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| 0.747 | 31.0 | 62 | 0.6631 | 0.8143 | |
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| 0.747 | 32.0 | 64 | 0.6616 | 0.8143 | |
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| 0.747 | 33.0 | 66 | 0.6601 | 0.8143 | |
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| 0.747 | 34.0 | 68 | 0.6586 | 0.8143 | |
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| 0.7343 | 35.0 | 70 | 0.6570 | 0.8143 | |
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| 0.7343 | 36.0 | 72 | 0.6553 | 0.8143 | |
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| 0.7343 | 37.0 | 74 | 0.6536 | 0.8143 | |
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| 0.7343 | 38.0 | 76 | 0.6517 | 0.8143 | |
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| 0.7343 | 39.0 | 78 | 0.6499 | 0.8143 | |
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| 0.7532 | 40.0 | 80 | 0.6480 | 0.8143 | |
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| 0.7532 | 41.0 | 82 | 0.6461 | 0.8143 | |
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| 0.7532 | 42.0 | 84 | 0.6442 | 0.8143 | |
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| 0.7532 | 43.0 | 86 | 0.6423 | 0.8143 | |
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| 0.7532 | 44.0 | 88 | 0.6405 | 0.8143 | |
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| 0.7239 | 45.0 | 90 | 0.6387 | 0.8143 | |
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| 0.7239 | 46.0 | 92 | 0.6368 | 0.8143 | |
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| 0.7239 | 47.0 | 94 | 0.6352 | 0.8143 | |
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| 0.7239 | 48.0 | 96 | 0.6337 | 0.8143 | |
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| 0.7239 | 49.0 | 98 | 0.6321 | 0.8286 | |
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| 0.7085 | 50.0 | 100 | 0.6307 | 0.8286 | |
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| 0.7085 | 51.0 | 102 | 0.6294 | 0.8286 | |
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| 0.7085 | 52.0 | 104 | 0.6278 | 0.8286 | |
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| 0.7085 | 53.0 | 106 | 0.6263 | 0.8286 | |
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| 0.7085 | 54.0 | 108 | 0.6248 | 0.8143 | |
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| 0.7203 | 55.0 | 110 | 0.6233 | 0.8143 | |
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| 0.7203 | 56.0 | 112 | 0.6219 | 0.8143 | |
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| 0.7203 | 57.0 | 114 | 0.6205 | 0.8143 | |
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| 0.7203 | 58.0 | 116 | 0.6191 | 0.8143 | |
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| 0.7203 | 59.0 | 118 | 0.6179 | 0.8143 | |
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| 0.7136 | 60.0 | 120 | 0.6167 | 0.8143 | |
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| 0.7136 | 61.0 | 122 | 0.6157 | 0.8143 | |
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| 0.7136 | 62.0 | 124 | 0.6148 | 0.8 | |
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| 0.7136 | 63.0 | 126 | 0.6138 | 0.8 | |
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| 0.7136 | 64.0 | 128 | 0.6125 | 0.8 | |
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| 0.7123 | 65.0 | 130 | 0.6111 | 0.8 | |
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| 0.7123 | 66.0 | 132 | 0.6096 | 0.8143 | |
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| 0.7123 | 67.0 | 134 | 0.6083 | 0.8143 | |
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| 0.7123 | 68.0 | 136 | 0.6070 | 0.8143 | |
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| 0.7123 | 69.0 | 138 | 0.6057 | 0.8143 | |
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| 0.7076 | 70.0 | 140 | 0.6046 | 0.8143 | |
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| 0.7076 | 71.0 | 142 | 0.6035 | 0.8143 | |
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| 0.7076 | 72.0 | 144 | 0.6023 | 0.8143 | |
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| 0.7076 | 73.0 | 146 | 0.6011 | 0.8143 | |
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| 0.7076 | 74.0 | 148 | 0.5999 | 0.8143 | |
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| 0.6878 | 75.0 | 150 | 0.5988 | 0.8143 | |
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| 0.6878 | 76.0 | 152 | 0.5975 | 0.8143 | |
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| 0.6878 | 77.0 | 154 | 0.5964 | 0.8143 | |
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| 0.6878 | 78.0 | 156 | 0.5953 | 0.8143 | |
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| 0.6878 | 79.0 | 158 | 0.5942 | 0.8143 | |
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| 0.6657 | 80.0 | 160 | 0.5932 | 0.8143 | |
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| 0.6657 | 81.0 | 162 | 0.5923 | 0.8143 | |
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| 0.6657 | 82.0 | 164 | 0.5914 | 0.8143 | |
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| 0.6657 | 83.0 | 166 | 0.5906 | 0.8143 | |
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| 0.6657 | 84.0 | 168 | 0.5897 | 0.8143 | |
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| 0.6434 | 85.0 | 170 | 0.5888 | 0.8143 | |
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| 0.6434 | 86.0 | 172 | 0.5878 | 0.8143 | |
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| 0.6434 | 87.0 | 174 | 0.5868 | 0.8143 | |
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| 0.6434 | 88.0 | 176 | 0.5859 | 0.8143 | |
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| 0.6434 | 89.0 | 178 | 0.5851 | 0.8143 | |
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| 0.6825 | 90.0 | 180 | 0.5843 | 0.8143 | |
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| 0.6825 | 91.0 | 182 | 0.5836 | 0.8143 | |
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| 0.6825 | 92.0 | 184 | 0.5828 | 0.8143 | |
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| 0.6825 | 93.0 | 186 | 0.5823 | 0.8143 | |
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| 0.6825 | 94.0 | 188 | 0.5817 | 0.8286 | |
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| 0.6695 | 95.0 | 190 | 0.5809 | 0.8143 | |
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| 0.6695 | 96.0 | 192 | 0.5801 | 0.8143 | |
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| 0.6695 | 97.0 | 194 | 0.5793 | 0.8143 | |
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| 0.6695 | 98.0 | 196 | 0.5787 | 0.8143 | |
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| 0.6695 | 99.0 | 198 | 0.5780 | 0.8143 | |
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| 0.6672 | 100.0 | 200 | 0.5772 | 0.8143 | |
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| 0.6672 | 101.0 | 202 | 0.5762 | 0.8143 | |
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| 0.6672 | 102.0 | 204 | 0.5754 | 0.8143 | |
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| 0.6672 | 103.0 | 206 | 0.5746 | 0.8143 | |
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| 0.6672 | 104.0 | 208 | 0.5738 | 0.8143 | |
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| 0.6569 | 105.0 | 210 | 0.5731 | 0.8143 | |
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| 0.6569 | 106.0 | 212 | 0.5724 | 0.8143 | |
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| 0.6569 | 107.0 | 214 | 0.5716 | 0.8143 | |
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| 0.6569 | 108.0 | 216 | 0.5708 | 0.8143 | |
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| 0.6569 | 109.0 | 218 | 0.5701 | 0.8143 | |
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| 0.6748 | 110.0 | 220 | 0.5694 | 0.8143 | |
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| 0.6748 | 111.0 | 222 | 0.5687 | 0.8143 | |
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| 0.6748 | 112.0 | 224 | 0.5680 | 0.8143 | |
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| 0.6748 | 113.0 | 226 | 0.5674 | 0.8143 | |
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| 0.6748 | 114.0 | 228 | 0.5668 | 0.8143 | |
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| 0.6388 | 115.0 | 230 | 0.5662 | 0.8143 | |
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| 0.6388 | 116.0 | 232 | 0.5657 | 0.8143 | |
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| 0.6388 | 117.0 | 234 | 0.5652 | 0.8143 | |
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| 0.6388 | 118.0 | 236 | 0.5648 | 0.8286 | |
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| 0.6388 | 119.0 | 238 | 0.5645 | 0.8286 | |
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| 0.6551 | 120.0 | 240 | 0.5641 | 0.8286 | |
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| 0.6551 | 121.0 | 242 | 0.5636 | 0.8143 | |
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| 0.6551 | 122.0 | 244 | 0.5631 | 0.8143 | |
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| 0.6551 | 123.0 | 246 | 0.5627 | 0.8143 | |
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| 0.6551 | 124.0 | 248 | 0.5624 | 0.8143 | |
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| 0.6452 | 125.0 | 250 | 0.5622 | 0.8143 | |
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| 0.6452 | 126.0 | 252 | 0.5620 | 0.8143 | |
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| 0.6452 | 127.0 | 254 | 0.5618 | 0.8143 | |
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| 0.6452 | 128.0 | 256 | 0.5615 | 0.8143 | |
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| 0.6452 | 129.0 | 258 | 0.5613 | 0.8143 | |
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| 0.645 | 130.0 | 260 | 0.5611 | 0.8143 | |
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| 0.645 | 131.0 | 262 | 0.5608 | 0.8143 | |
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| 0.645 | 132.0 | 264 | 0.5606 | 0.8143 | |
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| 0.645 | 133.0 | 266 | 0.5602 | 0.8143 | |
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| 0.645 | 134.0 | 268 | 0.5596 | 0.8143 | |
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| 0.629 | 135.0 | 270 | 0.5590 | 0.8143 | |
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| 0.629 | 136.0 | 272 | 0.5582 | 0.8143 | |
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| 0.629 | 137.0 | 274 | 0.5576 | 0.8143 | |
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| 0.629 | 138.0 | 276 | 0.5571 | 0.8143 | |
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| 0.629 | 139.0 | 278 | 0.5568 | 0.8143 | |
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| 0.7126 | 140.0 | 280 | 0.5565 | 0.8143 | |
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| 0.7126 | 141.0 | 282 | 0.5563 | 0.8143 | |
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| 0.7126 | 142.0 | 284 | 0.5561 | 0.8143 | |
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| 0.7126 | 143.0 | 286 | 0.5559 | 0.8143 | |
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| 0.7126 | 144.0 | 288 | 0.5555 | 0.8143 | |
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| 0.669 | 145.0 | 290 | 0.5552 | 0.8143 | |
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| 0.669 | 146.0 | 292 | 0.5547 | 0.8143 | |
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| 0.669 | 147.0 | 294 | 0.5542 | 0.8143 | |
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| 0.669 | 148.0 | 296 | 0.5538 | 0.8143 | |
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| 0.669 | 149.0 | 298 | 0.5534 | 0.8143 | |
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| 0.6481 | 150.0 | 300 | 0.5530 | 0.8143 | |
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| 0.6481 | 151.0 | 302 | 0.5526 | 0.8143 | |
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| 0.6481 | 152.0 | 304 | 0.5522 | 0.8143 | |
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| 0.6481 | 153.0 | 306 | 0.5519 | 0.8143 | |
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| 0.6481 | 154.0 | 308 | 0.5515 | 0.8143 | |
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| 0.6211 | 155.0 | 310 | 0.5510 | 0.8143 | |
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| 0.6211 | 156.0 | 312 | 0.5506 | 0.8143 | |
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| 0.6211 | 157.0 | 314 | 0.5502 | 0.8143 | |
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| 0.6211 | 158.0 | 316 | 0.5499 | 0.8143 | |
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| 0.6211 | 159.0 | 318 | 0.5496 | 0.8143 | |
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| 0.6458 | 160.0 | 320 | 0.5492 | 0.8286 | |
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| 0.6458 | 161.0 | 322 | 0.5490 | 0.8143 | |
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| 0.6458 | 162.0 | 324 | 0.5488 | 0.8143 | |
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| 0.6458 | 163.0 | 326 | 0.5486 | 0.8143 | |
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| 0.6458 | 164.0 | 328 | 0.5484 | 0.8143 | |
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| 0.6317 | 165.0 | 330 | 0.5481 | 0.8143 | |
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| 0.6317 | 166.0 | 332 | 0.5479 | 0.8286 | |
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| 0.6317 | 167.0 | 334 | 0.5476 | 0.8286 | |
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| 0.6317 | 168.0 | 336 | 0.5473 | 0.8286 | |
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| 0.6317 | 169.0 | 338 | 0.5471 | 0.8286 | |
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| 0.6154 | 170.0 | 340 | 0.5470 | 0.8286 | |
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| 0.6154 | 171.0 | 342 | 0.5468 | 0.8286 | |
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| 0.6154 | 172.0 | 344 | 0.5466 | 0.8286 | |
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| 0.6154 | 173.0 | 346 | 0.5464 | 0.8286 | |
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| 0.6154 | 174.0 | 348 | 0.5462 | 0.8286 | |
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| 0.6323 | 175.0 | 350 | 0.5460 | 0.8286 | |
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| 0.6323 | 176.0 | 352 | 0.5459 | 0.8286 | |
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| 0.6323 | 177.0 | 354 | 0.5457 | 0.8286 | |
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| 0.6323 | 178.0 | 356 | 0.5456 | 0.8286 | |
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| 0.6323 | 179.0 | 358 | 0.5455 | 0.8286 | |
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| 0.6331 | 180.0 | 360 | 0.5453 | 0.8286 | |
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| 0.6331 | 181.0 | 362 | 0.5452 | 0.8286 | |
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| 0.6331 | 182.0 | 364 | 0.5451 | 0.8286 | |
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| 0.6331 | 183.0 | 366 | 0.5449 | 0.8286 | |
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| 0.6331 | 184.0 | 368 | 0.5448 | 0.8286 | |
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| 0.6333 | 185.0 | 370 | 0.5447 | 0.8286 | |
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| 0.6333 | 186.0 | 372 | 0.5447 | 0.8286 | |
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| 0.6333 | 187.0 | 374 | 0.5446 | 0.8286 | |
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| 0.6333 | 188.0 | 376 | 0.5445 | 0.8286 | |
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| 0.6333 | 189.0 | 378 | 0.5445 | 0.8286 | |
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| 0.608 | 190.0 | 380 | 0.5444 | 0.8286 | |
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| 0.608 | 191.0 | 382 | 0.5444 | 0.8286 | |
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| 0.608 | 192.0 | 384 | 0.5443 | 0.8286 | |
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| 0.608 | 193.0 | 386 | 0.5443 | 0.8286 | |
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| 0.608 | 194.0 | 388 | 0.5442 | 0.8286 | |
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| 0.6155 | 195.0 | 390 | 0.5442 | 0.8286 | |
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| 0.6155 | 196.0 | 392 | 0.5442 | 0.8286 | |
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| 0.6155 | 197.0 | 394 | 0.5442 | 0.8286 | |
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| 0.6155 | 198.0 | 396 | 0.5441 | 0.8286 | |
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| 0.6155 | 199.0 | 398 | 0.5441 | 0.8286 | |
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| 0.6272 | 200.0 | 400 | 0.5441 | 0.8286 | |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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