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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- image-classification |
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- vision |
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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: tobikoi-classifier-alpha1 |
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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: 1.0 |
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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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# tobikoi-classifier-alpha1 |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0002 |
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- Accuracy: 1.0 |
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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: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 1337 |
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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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- num_epochs: 150.0 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | Validation Loss | |
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|:-------------:|:-----:|:----:|:--------:|:---------------:| |
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| 0.4014 | 1.0 | 54 | 0.7632 | 0.3552 | |
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| 0.2253 | 2.0 | 108 | 0.9737 | 0.1712 | |
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| 0.0768 | 3.0 | 162 | 0.9868 | 0.0763 | |
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| 0.0694 | 4.0 | 216 | 0.9868 | 0.0615 | |
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| 0.0433 | 5.0 | 270 | 0.9868 | 0.0504 | |
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| 0.1045 | 6.0 | 324 | 0.9868 | 0.0323 | |
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| 0.0148 | 7.0 | 378 | 0.9868 | 0.0436 | |
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| 0.0156 | 8.0 | 432 | 0.9868 | 0.0271 | |
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| 0.0109 | 9.0 | 486 | 0.9868 | 0.0511 | |
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| 0.0142 | 10.0 | 540 | 0.9868 | 0.0563 | |
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| 0.0307 | 11.0 | 594 | 0.9868 | 0.0633 | |
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| 0.0092 | 12.0 | 648 | 0.9868 | 0.0430 | |
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| 0.007 | 13.0 | 702 | 0.9868 | 0.0508 | |
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| 0.0059 | 14.0 | 756 | 0.9868 | 0.0598 | |
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| 0.0057 | 15.0 | 810 | 0.9868 | 0.0639 | |
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| 0.0513 | 16.0 | 864 | 0.9868 | 0.0579 | |
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| 0.0259 | 17.0 | 918 | 0.9868 | 0.0707 | |
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| 0.0111 | 18.0 | 972 | 0.9868 | 0.0611 | |
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| 0.014 | 19.0 | 1026 | 0.9868 | 0.0620 | |
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| 0.004 | 20.0 | 1080 | 1.0 | 0.0058 | |
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| 0.0036 | 21.0 | 1134 | 1.0 | 0.0044 | |
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| 0.0545 | 22.0 | 1188 | 1.0 | 0.0114 | |
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| 0.0131 | 23.0 | 1242 | 0.9868 | 0.0621 | |
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| 0.0651 | 24.0 | 1296 | 0.9868 | 0.0692 | |
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| 0.0047 | 25.0 | 1350 | 1.0 | 0.0034 | |
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| 0.0374 | 26.0 | 1404 | 1.0 | 0.0031 | |
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| 0.0482 | 27.0 | 1458 | 1.0 | 0.0045 | |
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| 0.0026 | 28.0 | 1512 | 1.0 | 0.0028 | |
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| 0.0038 | 29.0 | 1566 | 1.0 | 0.0025 | |
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| 0.0027 | 30.0 | 1620 | 1.0 | 0.0023 | |
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| 0.0145 | 31.0 | 1674 | 0.9868 | 0.0698 | |
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| 0.0022 | 32.0 | 1728 | 0.9868 | 0.0255 | |
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| 0.0025 | 33.0 | 1782 | 1.0 | 0.0095 | |
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| 0.0022 | 34.0 | 1836 | 0.9868 | 0.0725 | |
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| 0.0019 | 35.0 | 1890 | 0.9868 | 0.0592 | |
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| 0.0159 | 36.0 | 1944 | 0.9868 | 0.0747 | |
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| 0.0018 | 37.0 | 1998 | 0.9868 | 0.0244 | |
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| 0.0016 | 38.0 | 2052 | 1.0 | 0.0019 | |
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| 0.0017 | 39.0 | 2106 | 1.0 | 0.0018 | |
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| 0.053 | 40.0 | 2160 | 1.0 | 0.0023 | |
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| 0.0016 | 41.0 | 2214 | 1.0 | 0.0061 | |
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| 0.0015 | 42.0 | 2268 | 1.0 | 0.0102 | |
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| 0.0015 | 43.0 | 2322 | 1.0 | 0.0019 | |
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| 0.0015 | 44.0 | 2376 | 1.0 | 0.0062 | |
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| 0.0014 | 45.0 | 2430 | 1.0 | 0.0014 | |
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| 0.0015 | 46.0 | 2484 | 1.0 | 0.0015 | |
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| 0.0013 | 47.0 | 2538 | 0.9868 | 0.0672 | |
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| 0.0012 | 48.0 | 2592 | 1.0 | 0.0015 | |
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| 0.0012 | 49.0 | 2646 | 0.9868 | 0.0700 | |
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| 0.0012 | 50.0 | 2700 | 0.9868 | 0.0579 | |
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| 0.0011 | 51.0 | 2754 | 0.9868 | 0.0571 | |
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| 0.001 | 52.0 | 2808 | 0.9868 | 0.0670 | |
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| 0.001 | 53.0 | 2862 | 0.9868 | 0.0730 | |
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| 0.0013 | 54.0 | 2916 | 0.9868 | 0.0135 | |
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| 0.001 | 55.0 | 2970 | 0.9868 | 0.0836 | |
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| 0.0009 | 56.0 | 3024 | 1.0 | 0.0010 | |
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| 0.0009 | 57.0 | 3078 | 0.9868 | 0.0122 | |
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| 0.001 | 58.0 | 3132 | 0.9868 | 0.0105 | |
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| 0.0017 | 59.0 | 3186 | 1.0 | 0.0074 | |
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| 0.0009 | 60.0 | 3240 | 1.0 | 0.0010 | |
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| 0.0009 | 61.0 | 3294 | 1.0 | 0.0009 | |
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| 0.0381 | 62.0 | 3348 | 1.0 | 0.0020 | |
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| 0.0008 | 63.0 | 3402 | 1.0 | 0.0008 | |
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| 0.0099 | 64.0 | 3456 | 1.0 | 0.0008 | |
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| 0.0007 | 65.0 | 3510 | 0.9868 | 0.0757 | |
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| 0.0008 | 66.0 | 3564 | 0.9868 | 0.0764 | |
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| 0.0007 | 67.0 | 3618 | 0.9737 | 0.1257 | |
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| 0.0007 | 68.0 | 3672 | 0.9868 | 0.0098 | |
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| 0.0736 | 69.0 | 3726 | 1.0 | 0.0008 | |
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| 0.0007 | 70.0 | 3780 | 0.9868 | 0.0605 | |
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| 0.0006 | 71.0 | 3834 | 1.0 | 0.0012 | |
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| 0.001 | 72.0 | 3888 | 0.9737 | 0.1666 | |
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| 0.0042 | 73.0 | 3942 | 1.0 | 0.0007 | |
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| 0.0006 | 74.0 | 3996 | 1.0 | 0.0007 | |
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| 0.0007 | 75.0 | 4050 | 1.0 | 0.0007 | |
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| 0.0006 | 76.0 | 4104 | 0.9868 | 0.0331 | |
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| 0.0006 | 77.0 | 4158 | 0.9868 | 0.0169 | |
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| 0.0345 | 78.0 | 4212 | 1.0 | 0.0006 | |
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| 0.0005 | 79.0 | 4266 | 0.9868 | 0.0762 | |
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| 0.0005 | 80.0 | 4320 | 1.0 | 0.0007 | |
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| 0.0005 | 81.0 | 4374 | 1.0 | 0.0005 | |
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| 0.0005 | 82.0 | 4428 | 1.0 | 0.0006 | |
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| 0.0005 | 83.0 | 4482 | 1.0 | 0.0005 | |
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| 0.0005 | 84.0 | 4536 | 1.0 | 0.0005 | |
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| 0.0047 | 85.0 | 4590 | 1.0 | 0.0007 | |
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| 0.0005 | 86.0 | 4644 | 1.0 | 0.0005 | |
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| 0.0005 | 87.0 | 4698 | 1.0 | 0.0005 | |
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| 0.0004 | 88.0 | 4752 | 1.0 | 0.0004 | |
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| 0.0004 | 89.0 | 4806 | 1.0 | 0.0004 | |
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| 0.0005 | 90.0 | 4860 | 1.0 | 0.0005 | |
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| 0.0004 | 91.0 | 4914 | 1.0 | 0.0005 | |
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| 0.0067 | 92.0 | 4968 | 1.0 | 0.0004 | |
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| 0.0004 | 93.0 | 5022 | 1.0 | 0.0004 | |
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| 0.0004 | 94.0 | 5076 | 1.0 | 0.0004 | |
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| 0.0004 | 95.0 | 5130 | 1.0 | 0.0004 | |
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| 0.0004 | 96.0 | 5184 | 1.0 | 0.0004 | |
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| 0.0004 | 97.0 | 5238 | 1.0 | 0.0004 | |
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| 0.0004 | 98.0 | 5292 | 1.0 | 0.0004 | |
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| 0.0003 | 99.0 | 5346 | 1.0 | 0.0004 | |
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| 0.0003 | 100.0 | 5400 | 1.0 | 0.0003 | |
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| 0.0003 | 101.0 | 5454 | 1.0 | 0.0004 | |
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| 0.0004 | 102.0 | 5508 | 1.0 | 0.0005 | |
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| 0.0004 | 103.0 | 5562 | 1.0 | 0.0005 | |
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| 0.0004 | 104.0 | 5616 | 1.0 | 0.0004 | |
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| 0.0006 | 105.0 | 5670 | 1.0 | 0.0003 | |
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| 0.0005 | 106.0 | 5724 | 1.0 | 0.0003 | |
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| 0.0003 | 107.0 | 5778 | 1.0 | 0.0003 | |
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| 0.0003 | 108.0 | 5832 | 1.0 | 0.0003 | |
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| 0.0003 | 109.0 | 5886 | 1.0 | 0.0003 | |
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| 0.0003 | 110.0 | 5940 | 1.0 | 0.0003 | |
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| 0.0003 | 111.0 | 5994 | 1.0 | 0.0003 | |
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| 0.0003 | 112.0 | 6048 | 1.0 | 0.0003 | |
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| 0.0003 | 113.0 | 6102 | 1.0 | 0.0003 | |
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| 0.0003 | 114.0 | 6156 | 1.0 | 0.0003 | |
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| 0.0003 | 115.0 | 6210 | 1.0 | 0.0003 | |
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| 0.0003 | 116.0 | 6264 | 1.0 | 0.0003 | |
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| 0.0003 | 117.0 | 6318 | 1.0 | 0.0003 | |
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| 0.0003 | 118.0 | 6372 | 1.0 | 0.0003 | |
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| 0.0002 | 119.0 | 6426 | 1.0 | 0.0002 | |
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| 0.0002 | 120.0 | 6480 | 1.0 | 0.0002 | |
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| 0.0002 | 121.0 | 6534 | 1.0 | 0.0002 | |
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| 0.0003 | 122.0 | 6588 | 1.0 | 0.0002 | |
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| 0.0002 | 123.0 | 6642 | 1.0 | 0.0002 | |
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| 0.0002 | 124.0 | 6696 | 1.0 | 0.0002 | |
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| 0.0002 | 125.0 | 6750 | 1.0 | 0.0002 | |
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| 0.0002 | 126.0 | 6804 | 1.0 | 0.0002 | |
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| 0.0712 | 127.0 | 6858 | 1.0 | 0.0002 | |
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| 0.0002 | 128.0 | 6912 | 1.0 | 0.0002 | |
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| 0.0002 | 129.0 | 6966 | 1.0 | 0.0002 | |
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| 0.0002 | 130.0 | 7020 | 1.0 | 0.0002 | |
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| 0.0002 | 131.0 | 7074 | 1.0 | 0.0002 | |
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| 0.0002 | 132.0 | 7128 | 1.0 | 0.0002 | |
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| 0.0002 | 133.0 | 7182 | 1.0 | 0.0002 | |
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| 0.0002 | 134.0 | 7236 | 1.0 | 0.0002 | |
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| 0.0002 | 135.0 | 7290 | 1.0 | 0.0002 | |
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| 0.0003 | 136.0 | 7344 | 1.0 | 0.0002 | |
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| 0.0002 | 137.0 | 7398 | 1.0 | 0.0002 | |
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| 0.0002 | 138.0 | 7452 | 1.0 | 0.0002 | |
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| 0.0028 | 139.0 | 7506 | 1.0 | 0.0002 | |
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| 0.0006 | 140.0 | 7560 | 1.0 | 0.0002 | |
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| 0.0002 | 141.0 | 7614 | 1.0 | 0.0002 | |
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| 0.0002 | 142.0 | 7668 | 1.0 | 0.0002 | |
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| 0.0004 | 143.0 | 7722 | 1.0 | 0.0002 | |
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| 0.0002 | 144.0 | 7776 | 1.0 | 0.0002 | |
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| 0.0002 | 145.0 | 7830 | 1.0 | 0.0002 | |
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| 0.1028 | 146.0 | 7884 | 1.0 | 0.0002 | |
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| 0.0002 | 147.0 | 7938 | 1.0 | 0.0002 | |
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| 0.0002 | 148.0 | 7992 | 1.0 | 0.0002 | |
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| 0.0002 | 149.0 | 8046 | 1.0 | 0.0002 | |
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| 0.0002 | 150.0 | 8100 | 1.0 | 0.0002 | |
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### Framework versions |
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- Transformers 4.36.0.dev0 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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