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ViT_bloodmnist_std_45

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  1. README.md +130 -0
  2. config.json +44 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +22 -0
  5. runs/Nov09_07-01-15_53220661683e/events.out.tfevents.1731135678.53220661683e.23.0 +3 -0
  6. runs/Nov09_07-01-15_53220661683e/events.out.tfevents.1731143281.53220661683e.23.1 +3 -0
  7. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - medmnist-v2
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+ metrics:
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+ - accuracy
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+ - f1
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+ model-index:
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+ - name: ViT_bloodmnist_std_45
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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: medmnist-v2
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+ type: medmnist-v2
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+ config: bloodmnist
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+ split: validation
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+ args: bloodmnist
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9064600993861444
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+ - name: F1
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+ type: f1
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+ value: 0.8909233140229111
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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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+ # ViT_bloodmnist_std_45
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the medmnist-v2 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2659
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+ - Accuracy: 0.9065
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+ - F1: 0.8909
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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: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 8
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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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+ - num_epochs: 3
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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 | F1 |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|
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+ | 0.6113 | 0.0595 | 200 | 0.8908 | 0.6846 | 0.5917 |
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+ | 0.3578 | 0.1189 | 400 | 0.5958 | 0.7956 | 0.7548 |
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+ | 0.3118 | 0.1784 | 600 | 0.5688 | 0.7810 | 0.7132 |
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+ | 0.2815 | 0.2378 | 800 | 0.5227 | 0.7961 | 0.7645 |
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+ | 0.266 | 0.2973 | 1000 | 0.6554 | 0.7687 | 0.7229 |
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+ | 0.2353 | 0.3567 | 1200 | 0.3328 | 0.8838 | 0.8615 |
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+ | 0.2297 | 0.4162 | 1400 | 0.4696 | 0.8592 | 0.7990 |
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+ | 0.2267 | 0.4756 | 1600 | 0.4362 | 0.8493 | 0.8117 |
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+ | 0.2266 | 0.5351 | 1800 | 0.3286 | 0.8838 | 0.8407 |
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+ | 0.2047 | 0.5945 | 2000 | 0.3614 | 0.8697 | 0.8382 |
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+ | 0.1948 | 0.6540 | 2200 | 0.3144 | 0.8843 | 0.8546 |
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+ | 0.1953 | 0.7134 | 2400 | 0.3805 | 0.8657 | 0.8180 |
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+ | 0.1728 | 0.7729 | 2600 | 0.3364 | 0.8820 | 0.8339 |
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+ | 0.1658 | 0.8323 | 2800 | 0.2873 | 0.8978 | 0.8743 |
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+ | 0.1594 | 0.8918 | 3000 | 0.3062 | 0.8914 | 0.8580 |
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+ | 0.1649 | 0.9512 | 3200 | 0.3313 | 0.8867 | 0.8577 |
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+ | 0.1508 | 1.0107 | 3400 | 0.2117 | 0.9217 | 0.9133 |
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+ | 0.1062 | 1.0702 | 3600 | 0.2978 | 0.8919 | 0.8756 |
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+ | 0.1091 | 1.1296 | 3800 | 0.2832 | 0.9019 | 0.8831 |
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+ | 0.0993 | 1.1891 | 4000 | 0.3275 | 0.8943 | 0.8718 |
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+ | 0.1001 | 1.2485 | 4200 | 0.3420 | 0.8896 | 0.8568 |
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+ | 0.1092 | 1.3080 | 4400 | 0.2594 | 0.9130 | 0.8909 |
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+ | 0.092 | 1.3674 | 4600 | 0.3181 | 0.8966 | 0.8753 |
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+ | 0.1036 | 1.4269 | 4800 | 0.2721 | 0.9048 | 0.8852 |
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+ | 0.0896 | 1.4863 | 5000 | 0.3795 | 0.8820 | 0.8617 |
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+ | 0.0904 | 1.5458 | 5200 | 0.2382 | 0.9171 | 0.8980 |
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+ | 0.0864 | 1.6052 | 5400 | 0.3845 | 0.8814 | 0.8499 |
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+ | 0.0809 | 1.6647 | 5600 | 0.3189 | 0.8984 | 0.8758 |
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+ | 0.0764 | 1.7241 | 5800 | 0.3952 | 0.8843 | 0.8522 |
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+ | 0.0796 | 1.7836 | 6000 | 0.3656 | 0.8867 | 0.8460 |
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+ | 0.0695 | 1.8430 | 6200 | 0.3266 | 0.8925 | 0.8597 |
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+ | 0.0682 | 1.9025 | 6400 | 0.3247 | 0.8960 | 0.8647 |
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+ | 0.06 | 1.9620 | 6600 | 0.2349 | 0.9223 | 0.9055 |
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+ | 0.0498 | 2.0214 | 6800 | 0.2578 | 0.9176 | 0.8952 |
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+ | 0.0296 | 2.0809 | 7000 | 0.2592 | 0.9211 | 0.9070 |
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+ | 0.0251 | 2.1403 | 7200 | 0.3249 | 0.9048 | 0.8797 |
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+ | 0.02 | 2.1998 | 7400 | 0.2977 | 0.9165 | 0.8973 |
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+ | 0.0274 | 2.2592 | 7600 | 0.3411 | 0.9013 | 0.8730 |
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+ | 0.0241 | 2.3187 | 7800 | 0.3916 | 0.9013 | 0.8752 |
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+ | 0.0253 | 2.3781 | 8000 | 0.2919 | 0.9136 | 0.8939 |
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+ | 0.0197 | 2.4376 | 8200 | 0.3294 | 0.9077 | 0.8835 |
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+ | 0.0209 | 2.4970 | 8400 | 0.3709 | 0.8966 | 0.8652 |
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+ | 0.0175 | 2.5565 | 8600 | 0.3639 | 0.9001 | 0.8733 |
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+ | 0.0191 | 2.6159 | 8800 | 0.3706 | 0.9048 | 0.8790 |
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+ | 0.0167 | 2.6754 | 9000 | 0.3120 | 0.9171 | 0.8993 |
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+ | 0.0224 | 2.7348 | 9200 | 0.3493 | 0.9048 | 0.8799 |
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+ | 0.015 | 2.7943 | 9400 | 0.3398 | 0.9130 | 0.8889 |
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+ | 0.0155 | 2.8537 | 9600 | 0.3707 | 0.9036 | 0.8758 |
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+ | 0.0129 | 2.9132 | 9800 | 0.3467 | 0.9118 | 0.8909 |
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+ | 0.0126 | 2.9727 | 10000 | 0.3470 | 0.9095 | 0.8874 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.45.1
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+ - Pytorch 2.4.0
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+ - Datasets 3.0.1
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+ - Tokenizers 0.20.0
config.json ADDED
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+ {
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+ "_name_or_path": "google/vit-base-patch16-224",
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+ "architectures": [
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+ "ViTForImageClassification"
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+ ],
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+ "attention_probs_dropout_prob": 0.0,
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+ "encoder_stride": 16,
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+ "hidden_act": "gelu",
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+ "hidden_dropout_prob": 0.0,
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+ "hidden_size": 768,
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+ "id2label": {
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+ "0": "basophil",
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+ "1": "eosinophil",
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+ "2": "erythroblast",
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+ "3": "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)",
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+ "4": "lymphocyte",
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+ "5": "monocyte",
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+ "6": "neutrophil",
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+ "7": "platelet"
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+ "initializer_range": 0.02,
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+ "immature granulocytes(myelocytes, metamyelocytes and promyelocytes)": "3",
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+ "lymphocyte": "4",
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+ "monocyte": "5",
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+ "neutrophil": "6",
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+ "platelet": "7"
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+ },
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+ "layer_norm_eps": 1e-12,
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+ "model_type": "vit",
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+ "num_attention_heads": 12,
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+ "num_channels": 3,
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+ "num_hidden_layers": 12,
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+ "patch_size": 16,
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+ "problem_type": "single_label_classification",
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+ "qkv_bias": true,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.45.1"
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+ }
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