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finetuned-FER2013

Image Classification
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beit
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  2. model.safetensors +1 -1
README.md CHANGED
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.7133402995471961
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8090
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- - Accuracy: 0.7133
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  ## Model description
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@@ -61,77 +61,57 @@ The following hyperparameters were used during training:
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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: 60
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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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- | 1.7999 | 1.0 | 202 | 1.7896 | 0.2713 |
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- | 1.5165 | 2.0 | 404 | 1.4311 | 0.4758 |
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- | 1.3381 | 3.0 | 606 | 1.2065 | 0.5468 |
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- | 1.2629 | 4.0 | 808 | 1.1050 | 0.5886 |
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- | 1.248 | 5.0 | 1010 | 1.0425 | 0.6054 |
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- | 1.2007 | 6.0 | 1212 | 0.9874 | 0.6339 |
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- | 1.083 | 7.0 | 1414 | 0.9610 | 0.6447 |
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- | 1.1061 | 8.0 | 1616 | 0.9385 | 0.6524 |
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- | 1.0597 | 9.0 | 1818 | 0.9155 | 0.6517 |
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- | 1.0511 | 10.0 | 2020 | 0.9128 | 0.6580 |
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- | 1.012 | 11.0 | 2222 | 0.9048 | 0.6660 |
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- | 1.0479 | 12.0 | 2424 | 0.8821 | 0.6729 |
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- | 0.9993 | 13.0 | 2626 | 0.8770 | 0.6747 |
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- | 0.9784 | 14.0 | 2828 | 0.8672 | 0.6757 |
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- | 1.0439 | 15.0 | 3030 | 0.8766 | 0.6750 |
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- | 0.9782 | 16.0 | 3232 | 0.8658 | 0.6747 |
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- | 0.9664 | 17.0 | 3434 | 0.8596 | 0.6764 |
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- | 1.0132 | 18.0 | 3636 | 0.8491 | 0.6806 |
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- | 0.9703 | 19.0 | 3838 | 0.8538 | 0.6827 |
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- | 0.9399 | 20.0 | 4040 | 0.8452 | 0.6876 |
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- | 0.9299 | 21.0 | 4242 | 0.8420 | 0.6904 |
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- | 0.9815 | 22.0 | 4444 | 0.8417 | 0.6872 |
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- | 0.9029 | 23.0 | 4646 | 0.8379 | 0.6900 |
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- | 0.9142 | 24.0 | 4848 | 0.8336 | 0.6897 |
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- | 0.8695 | 25.0 | 5050 | 0.8312 | 0.6938 |
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- | 0.8791 | 26.0 | 5252 | 0.8323 | 0.6942 |
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- | 0.923 | 27.0 | 5454 | 0.8244 | 0.6956 |
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- | 0.8866 | 28.0 | 5656 | 0.8261 | 0.6970 |
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- | 0.9319 | 29.0 | 5858 | 0.8255 | 0.6991 |
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- | 0.9019 | 30.0 | 6060 | 0.8160 | 0.7050 |
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- | 0.8785 | 31.0 | 6262 | 0.8169 | 0.7071 |
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- | 0.8859 | 32.0 | 6464 | 0.8178 | 0.7039 |
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- | 0.8464 | 33.0 | 6666 | 0.8147 | 0.7092 |
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- | 0.9143 | 34.0 | 6868 | 0.8232 | 0.7029 |
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- | 0.8506 | 35.0 | 7070 | 0.8158 | 0.7032 |
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- | 0.9084 | 36.0 | 7272 | 0.8166 | 0.7057 |
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- | 0.8616 | 37.0 | 7474 | 0.8132 | 0.7088 |
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- | 0.8656 | 38.0 | 7676 | 0.8155 | 0.7046 |
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- | 0.8238 | 39.0 | 7878 | 0.8170 | 0.7064 |
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- | 0.8673 | 40.0 | 8080 | 0.8190 | 0.7092 |
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- | 0.8624 | 41.0 | 8282 | 0.8127 | 0.7095 |
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- | 0.8261 | 42.0 | 8484 | 0.8113 | 0.7113 |
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- | 0.8218 | 43.0 | 8686 | 0.8150 | 0.7095 |
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- | 0.8584 | 44.0 | 8888 | 0.8170 | 0.7071 |
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- | 0.8156 | 45.0 | 9090 | 0.8117 | 0.7119 |
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- | 0.8075 | 46.0 | 9292 | 0.8133 | 0.7116 |
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- | 0.8382 | 47.0 | 9494 | 0.8146 | 0.7088 |
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- | 0.7501 | 48.0 | 9696 | 0.8096 | 0.7113 |
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- | 0.7859 | 49.0 | 9898 | 0.8102 | 0.7081 |
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- | 0.8195 | 50.0 | 10100 | 0.8121 | 0.7085 |
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- | 0.8397 | 51.0 | 10302 | 0.8120 | 0.7099 |
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- | 0.8561 | 52.0 | 10504 | 0.8089 | 0.7126 |
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- | 0.8082 | 53.0 | 10706 | 0.8090 | 0.7133 |
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- | 0.8574 | 54.0 | 10908 | 0.8087 | 0.7106 |
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- | 0.8611 | 55.0 | 11110 | 0.8093 | 0.7092 |
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- | 0.8886 | 56.0 | 11312 | 0.8100 | 0.7092 |
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- | 0.7857 | 57.0 | 11514 | 0.8086 | 0.7133 |
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- | 0.8467 | 58.0 | 11716 | 0.8083 | 0.7119 |
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- | 0.795 | 59.0 | 11918 | 0.8083 | 0.7119 |
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- | 0.7975 | 60.0 | 12120 | 0.8079 | 0.7133 |
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  ### Framework versions
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  - Transformers 4.35.2
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  - Pytorch 2.1.0+cu121
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- - Datasets 2.16.0
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  - Tokenizers 0.15.0
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.7011494252873564
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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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  This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8313
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+ - Accuracy: 0.7011
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  ## Model description
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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: 40
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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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+ | 1.7483 | 1.0 | 202 | 1.7005 | 0.3386 |
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+ | 1.4419 | 2.0 | 404 | 1.3213 | 0.5315 |
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+ | 1.2917 | 3.0 | 606 | 1.1559 | 0.5785 |
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+ | 1.2437 | 4.0 | 808 | 1.0729 | 0.6162 |
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+ | 1.1635 | 5.0 | 1010 | 1.0161 | 0.6311 |
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+ | 1.1087 | 6.0 | 1212 | 0.9862 | 0.6465 |
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+ | 1.0964 | 7.0 | 1414 | 0.9901 | 0.6440 |
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+ | 1.0895 | 8.0 | 1616 | 0.9410 | 0.6555 |
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+ | 1.0384 | 9.0 | 1818 | 0.9221 | 0.6628 |
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+ | 1.0333 | 10.0 | 2020 | 0.9142 | 0.6681 |
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+ | 1.0016 | 11.0 | 2222 | 0.9081 | 0.6681 |
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+ | 0.9503 | 12.0 | 2424 | 0.9013 | 0.6712 |
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+ | 0.9804 | 13.0 | 2626 | 0.8937 | 0.6771 |
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+ | 0.9712 | 14.0 | 2828 | 0.8809 | 0.6830 |
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+ | 1.0151 | 15.0 | 3030 | 0.8704 | 0.6855 |
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+ | 0.9739 | 16.0 | 3232 | 0.8886 | 0.6775 |
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+ | 0.9267 | 17.0 | 3434 | 0.8653 | 0.6855 |
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+ | 0.9428 | 18.0 | 3636 | 0.8633 | 0.6848 |
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+ | 0.9654 | 19.0 | 3838 | 0.8697 | 0.6809 |
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+ | 0.9256 | 20.0 | 4040 | 0.8559 | 0.6855 |
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+ | 0.9345 | 21.0 | 4242 | 0.8533 | 0.6883 |
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+ | 0.9479 | 22.0 | 4444 | 0.8548 | 0.6907 |
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+ | 0.8829 | 23.0 | 4646 | 0.8461 | 0.6851 |
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+ | 0.8999 | 24.0 | 4848 | 0.8399 | 0.6883 |
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+ | 0.9047 | 25.0 | 5050 | 0.8403 | 0.6973 |
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+ | 0.9415 | 26.0 | 5252 | 0.8437 | 0.6952 |
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+ | 0.937 | 27.0 | 5454 | 0.8393 | 0.6931 |
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+ | 0.8692 | 28.0 | 5656 | 0.8331 | 0.6977 |
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+ | 0.9396 | 29.0 | 5858 | 0.8418 | 0.6973 |
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+ | 0.8712 | 30.0 | 6060 | 0.8392 | 0.6921 |
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+ | 0.9426 | 31.0 | 6262 | 0.8324 | 0.7011 |
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+ | 0.884 | 32.0 | 6464 | 0.8325 | 0.6959 |
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+ | 0.8433 | 33.0 | 6666 | 0.8300 | 0.6987 |
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+ | 0.8869 | 34.0 | 6868 | 0.8328 | 0.6963 |
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+ | 0.89 | 35.0 | 7070 | 0.8324 | 0.6973 |
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+ | 0.8639 | 36.0 | 7272 | 0.8317 | 0.6956 |
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+ | 0.8844 | 37.0 | 7474 | 0.8315 | 0.6970 |
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+ | 0.8621 | 38.0 | 7676 | 0.8334 | 0.6991 |
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+ | 0.8942 | 39.0 | 7878 | 0.8350 | 0.6998 |
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+ | 0.8609 | 40.0 | 8080 | 0.8313 | 0.7011 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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  - Transformers 4.35.2
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  - Pytorch 2.1.0+cu121
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+ - Datasets 2.16.1
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  - Tokenizers 0.15.0
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