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Saving best model to hub

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/resnet-50
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5
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+ results: []
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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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+ # resnet101_rvl-cdip-cnn_rvl_cdip-NK1000_kd_NKD_t1.0_g1.5
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+
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+ This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.9013
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+ - Accuracy: 0.7933
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+ - Brier Loss: 0.3080
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+ - Nll: 1.8102
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+ - F1 Micro: 0.7932
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+ - F1 Macro: 0.7937
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+ - Ece: 0.0719
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+ - Aurc: 0.0635
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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: 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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+ - 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: 50
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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 | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
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+ | No log | 1.0 | 250 | 6.0054 | 0.098 | 0.9327 | 9.3196 | 0.0980 | 0.0481 | 0.0462 | 0.8670 |
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+ | 6.0141 | 2.0 | 500 | 5.4713 | 0.2195 | 0.8933 | 5.2235 | 0.2195 | 0.1452 | 0.1046 | 0.7129 |
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+ | 6.0141 | 3.0 | 750 | 4.4006 | 0.4535 | 0.7034 | 3.0178 | 0.4535 | 0.4351 | 0.1373 | 0.3334 |
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+ | 4.5079 | 4.0 | 1000 | 3.8431 | 0.59 | 0.5686 | 2.5843 | 0.59 | 0.5822 | 0.1309 | 0.2072 |
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+ | 4.5079 | 5.0 | 1250 | 3.5315 | 0.6552 | 0.4864 | 2.4330 | 0.6552 | 0.6537 | 0.1048 | 0.1504 |
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+ | 3.5028 | 6.0 | 1500 | 3.2850 | 0.707 | 0.4163 | 2.2375 | 0.707 | 0.7082 | 0.0790 | 0.1111 |
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+ | 3.5028 | 7.0 | 1750 | 3.0974 | 0.7312 | 0.3721 | 2.0933 | 0.7312 | 0.7328 | 0.0452 | 0.0899 |
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+ | 3.0599 | 8.0 | 2000 | 3.0385 | 0.7455 | 0.3561 | 2.0148 | 0.7455 | 0.7456 | 0.0432 | 0.0838 |
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+ | 3.0599 | 9.0 | 2250 | 2.9978 | 0.7565 | 0.3432 | 1.9780 | 0.7565 | 0.7572 | 0.0437 | 0.0777 |
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+ | 2.8562 | 10.0 | 2500 | 2.9853 | 0.7622 | 0.3397 | 1.9176 | 0.7622 | 0.7619 | 0.0495 | 0.0751 |
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+ | 2.8562 | 11.0 | 2750 | 2.9803 | 0.7615 | 0.3385 | 1.9327 | 0.7615 | 0.7627 | 0.0547 | 0.0760 |
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+ | 2.7414 | 12.0 | 3000 | 2.9711 | 0.7658 | 0.3322 | 1.9439 | 0.7658 | 0.7661 | 0.0495 | 0.0740 |
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+ | 2.7414 | 13.0 | 3250 | 2.9618 | 0.771 | 0.3276 | 1.8599 | 0.771 | 0.7718 | 0.0548 | 0.0704 |
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+ | 2.6658 | 14.0 | 3500 | 2.9534 | 0.7762 | 0.3252 | 1.8935 | 0.7762 | 0.7770 | 0.0581 | 0.0699 |
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+ | 2.6658 | 15.0 | 3750 | 2.9568 | 0.776 | 0.3248 | 1.8836 | 0.776 | 0.7776 | 0.0588 | 0.0699 |
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+ | 2.6197 | 16.0 | 4000 | 2.9196 | 0.7812 | 0.3169 | 1.8338 | 0.7812 | 0.7814 | 0.0601 | 0.0655 |
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+ | 2.6197 | 17.0 | 4250 | 2.9267 | 0.7785 | 0.3202 | 1.8430 | 0.7785 | 0.7783 | 0.0647 | 0.0677 |
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+ | 2.5794 | 18.0 | 4500 | 2.9189 | 0.779 | 0.3155 | 1.8279 | 0.779 | 0.7794 | 0.0631 | 0.0661 |
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+ | 2.5794 | 19.0 | 4750 | 2.9324 | 0.7823 | 0.3177 | 1.8508 | 0.7823 | 0.7823 | 0.0665 | 0.0669 |
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+ | 2.5553 | 20.0 | 5000 | 2.9192 | 0.7837 | 0.3146 | 1.8312 | 0.7837 | 0.7840 | 0.0641 | 0.0654 |
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+ | 2.5553 | 21.0 | 5250 | 2.9160 | 0.7817 | 0.3140 | 1.8366 | 0.7817 | 0.7828 | 0.0682 | 0.0658 |
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+ | 2.53 | 22.0 | 5500 | 2.9172 | 0.7837 | 0.3139 | 1.8138 | 0.7837 | 0.7842 | 0.0602 | 0.0652 |
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+ | 2.53 | 23.0 | 5750 | 2.9132 | 0.7875 | 0.3134 | 1.8254 | 0.7875 | 0.7877 | 0.0656 | 0.0646 |
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+ | 2.5127 | 24.0 | 6000 | 2.9108 | 0.7875 | 0.3132 | 1.8367 | 0.7875 | 0.7869 | 0.0669 | 0.0652 |
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+ | 2.5127 | 25.0 | 6250 | 2.9272 | 0.7837 | 0.3139 | 1.8551 | 0.7837 | 0.7843 | 0.0632 | 0.0653 |
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+ | 2.4979 | 26.0 | 6500 | 2.9157 | 0.7867 | 0.3128 | 1.8101 | 0.7868 | 0.7876 | 0.0655 | 0.0647 |
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+ | 2.4979 | 27.0 | 6750 | 2.9031 | 0.785 | 0.3112 | 1.8089 | 0.785 | 0.7856 | 0.0688 | 0.0639 |
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+ | 2.4814 | 28.0 | 7000 | 2.9094 | 0.7875 | 0.3110 | 1.8594 | 0.7875 | 0.7880 | 0.0677 | 0.0646 |
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+ | 2.4814 | 29.0 | 7250 | 2.9110 | 0.7885 | 0.3116 | 1.8150 | 0.7885 | 0.7891 | 0.0696 | 0.0639 |
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+ | 2.4741 | 30.0 | 7500 | 2.9039 | 0.7877 | 0.3091 | 1.8471 | 0.7877 | 0.7887 | 0.0694 | 0.0632 |
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+ | 2.4741 | 31.0 | 7750 | 2.9029 | 0.7907 | 0.3087 | 1.7604 | 0.7907 | 0.7917 | 0.0691 | 0.0633 |
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+ | 2.4626 | 32.0 | 8000 | 2.8983 | 0.7877 | 0.3094 | 1.8191 | 0.7877 | 0.7884 | 0.0677 | 0.0625 |
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+ | 2.4626 | 33.0 | 8250 | 2.9024 | 0.7897 | 0.3088 | 1.8025 | 0.7897 | 0.7905 | 0.0720 | 0.0635 |
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+ | 2.4558 | 34.0 | 8500 | 2.9055 | 0.792 | 0.3070 | 1.7869 | 0.792 | 0.7920 | 0.0667 | 0.0628 |
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+ | 2.4558 | 35.0 | 8750 | 2.9055 | 0.788 | 0.3104 | 1.8349 | 0.788 | 0.7883 | 0.0733 | 0.0645 |
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+ | 2.4481 | 36.0 | 9000 | 2.9061 | 0.7887 | 0.3078 | 1.7840 | 0.7887 | 0.7898 | 0.0676 | 0.0642 |
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+ | 2.4481 | 37.0 | 9250 | 2.9086 | 0.7917 | 0.3102 | 1.7942 | 0.7917 | 0.7923 | 0.0716 | 0.0644 |
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+ | 2.4422 | 38.0 | 9500 | 2.9067 | 0.7897 | 0.3084 | 1.7915 | 0.7897 | 0.7900 | 0.0704 | 0.0637 |
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+ | 2.4422 | 39.0 | 9750 | 2.9080 | 0.7927 | 0.3092 | 1.7951 | 0.7927 | 0.7930 | 0.0709 | 0.0631 |
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+ | 2.4386 | 40.0 | 10000 | 2.9064 | 0.7943 | 0.3084 | 1.8079 | 0.7943 | 0.7949 | 0.0734 | 0.0635 |
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+ | 2.4386 | 41.0 | 10250 | 2.8990 | 0.792 | 0.3056 | 1.7918 | 0.792 | 0.7924 | 0.0699 | 0.0623 |
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+ | 2.4312 | 42.0 | 10500 | 2.9057 | 0.7893 | 0.3090 | 1.7892 | 0.7893 | 0.7901 | 0.0735 | 0.0641 |
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+ | 2.4312 | 43.0 | 10750 | 2.8998 | 0.7923 | 0.3079 | 1.7909 | 0.7923 | 0.7932 | 0.0707 | 0.0630 |
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+ | 2.4294 | 44.0 | 11000 | 2.9108 | 0.7905 | 0.3090 | 1.8220 | 0.7905 | 0.7916 | 0.0773 | 0.0636 |
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+ | 2.4294 | 45.0 | 11250 | 2.9030 | 0.7927 | 0.3086 | 1.8126 | 0.7927 | 0.7932 | 0.0710 | 0.0631 |
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+ | 2.4282 | 46.0 | 11500 | 2.9033 | 0.7915 | 0.3077 | 1.8234 | 0.7915 | 0.7920 | 0.0712 | 0.0631 |
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+ | 2.4282 | 47.0 | 11750 | 2.8975 | 0.7957 | 0.3063 | 1.8070 | 0.7957 | 0.7968 | 0.0702 | 0.0630 |
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+ | 2.4246 | 48.0 | 12000 | 2.9049 | 0.7935 | 0.3085 | 1.8090 | 0.7935 | 0.7944 | 0.0722 | 0.0635 |
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+ | 2.4246 | 49.0 | 12250 | 2.9020 | 0.792 | 0.3075 | 1.8233 | 0.792 | 0.7927 | 0.0700 | 0.0638 |
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+ | 2.4227 | 50.0 | 12500 | 2.9013 | 0.7933 | 0.3080 | 1.8102 | 0.7932 | 0.7937 | 0.0719 | 0.0635 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.33.3
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+ - Pytorch 2.2.0.dev20231002
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+ - Datasets 2.7.1
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+ - Tokenizers 0.13.3
config.json ADDED
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+ {
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+ "_name_or_path": "microsoft/resnet-50",
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+ "architectures": [
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+ "ResNetForImageClassification"
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+ ],
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+ "depths": [
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+ 3,
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+ 4,
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+ 6,
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+ 3
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+ ],
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+ "downsample_in_first_stage": false,
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+ "embedding_size": 64,
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+ "hidden_act": "relu",
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+ "hidden_sizes": [
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+ 256,
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+ 512,
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+ 1024,
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+ 2048
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+ ],
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+ "id2label": {
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+ "0": "letter",
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+ "1": "form",
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+ "2": "email",
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+ "3": "handwritten",
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+ "4": "advertisement",
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+ "5": "scientific_report",
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+ "6": "scientific_publication",
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+ "7": "specification",
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+ "8": "file_folder",
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+ "9": "news_article",
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+ "10": "budget",
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+ "11": "invoice",
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+ "12": "presentation",
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+ "13": "questionnaire",
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+ "14": "resume",
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+ "15": "memo"
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+ },
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+ "label2id": {
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+ "specification": 7
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+ },
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+ "layer_type": "bottleneck",
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+ "model_type": "resnet",
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+ "num_channels": 3,
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+ "out_features": [
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+ "stage4"
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+ ],
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+ "out_indices": [
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+ 4
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+ ],
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+ "problem_type": "single_label_classification",
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+ "stage_names": [
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+ "stem",
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+ "stage1",
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+ "stage2",
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+ "stage3",
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+ "stage4"
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+ ],
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.33.3"
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+ }
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