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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/beit-base-patch16-224-pt22k-ft22k
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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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+ - f1
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+ - precision
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+ - recall
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+ model-index:
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+ - name: beit-base-patch16-224-pt22k-ft22k-finetuned-tekno24
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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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+ # beit-base-patch16-224-pt22k-ft22k-finetuned-tekno24
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+
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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 an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0073
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+ - Accuracy: 0.5785
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+ - F1: 0.5641
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+ - Precision: 0.5641
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+ - Recall: 0.5785
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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: 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: 0.1
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+ - num_epochs: 15
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+ - mixed_precision_training: Native AMP
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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 | Precision | Recall |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | 1.4008 | 0.9855 | 17 | 1.2967 | 0.4059 | 0.3220 | 0.3791 | 0.4059 |
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+ | 1.2363 | 1.9710 | 34 | 1.1309 | 0.5032 | 0.4187 | 0.4871 | 0.5032 |
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+ | 1.1716 | 2.9565 | 51 | 1.0983 | 0.5161 | 0.4385 | 0.4610 | 0.5161 |
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+ | 1.1479 | 4.0 | 69 | 1.0550 | 0.5409 | 0.5014 | 0.5067 | 0.5409 |
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+ | 1.1058 | 4.9855 | 86 | 1.0397 | 0.5500 | 0.4942 | 0.5208 | 0.5500 |
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+ | 1.0656 | 5.9710 | 103 | 1.0558 | 0.5556 | 0.5396 | 0.5486 | 0.5556 |
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+ | 1.0328 | 6.9565 | 120 | 1.0216 | 0.5730 | 0.5465 | 0.5513 | 0.5730 |
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+ | 1.0116 | 8.0 | 138 | 1.0469 | 0.5363 | 0.5187 | 0.5119 | 0.5363 |
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+ | 1.012 | 8.9855 | 155 | 1.0216 | 0.5629 | 0.5226 | 0.5344 | 0.5629 |
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+ | 1.0076 | 9.9710 | 172 | 1.0186 | 0.5675 | 0.5275 | 0.5379 | 0.5675 |
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+ | 0.9714 | 10.9565 | 189 | 1.0205 | 0.5638 | 0.5499 | 0.5549 | 0.5638 |
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+ | 0.9843 | 12.0 | 207 | 1.0117 | 0.5657 | 0.5488 | 0.5495 | 0.5657 |
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+ | 0.9427 | 12.9855 | 224 | 1.0072 | 0.5785 | 0.5643 | 0.5602 | 0.5785 |
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+ | 0.9268 | 13.9710 | 241 | 1.0068 | 0.5785 | 0.5652 | 0.5621 | 0.5785 |
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+ | 0.9525 | 14.7826 | 255 | 1.0073 | 0.5785 | 0.5641 | 0.5641 | 0.5785 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.42.4
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+ - Pytorch 2.4.0+cu121
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+ - Datasets 2.21.0
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+ - Tokenizers 0.19.1
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