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convnextv2-base-22k-224-finetuned-tekno24-highdata-90

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convnextv2
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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: facebook/convnextv2-base-22k-224
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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: convnextv2-base-22k-224-finetuned-tekno24-highdata-90
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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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+ # convnextv2-base-22k-224-finetuned-tekno24-highdata-90
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+
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+ This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.0280
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+ - Accuracy: 0.6129
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+ - F1: 0.6087
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+ - Precision: 0.6161
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+ - Recall: 0.6129
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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: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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: 30
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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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+ | 1.3277 | 0.9908 | 81 | 1.2870 | 0.4147 | 0.3280 | 0.3714 | 0.4147 |
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+ | 1.2024 | 1.9939 | 163 | 1.0890 | 0.4747 | 0.3907 | 0.4944 | 0.4747 |
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+ | 1.2067 | 2.9969 | 245 | 1.0601 | 0.5438 | 0.4965 | 0.5084 | 0.5438 |
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+ | 1.206 | 4.0 | 327 | 1.0143 | 0.5392 | 0.5159 | 0.5180 | 0.5392 |
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+ | 1.1049 | 4.9908 | 408 | 0.9688 | 0.5760 | 0.5451 | 0.5467 | 0.5760 |
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+ | 1.0931 | 5.9939 | 490 | 1.0351 | 0.5622 | 0.5562 | 0.5939 | 0.5622 |
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+ | 1.0752 | 6.9969 | 572 | 0.9370 | 0.5899 | 0.5592 | 0.5730 | 0.5899 |
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+ | 1.03 | 8.0 | 654 | 0.9417 | 0.5760 | 0.5510 | 0.5414 | 0.5760 |
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+ | 0.988 | 8.9908 | 735 | 0.8942 | 0.5991 | 0.5772 | 0.5819 | 0.5991 |
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+ | 0.9692 | 9.9939 | 817 | 0.9091 | 0.6083 | 0.5937 | 0.5981 | 0.6083 |
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+ | 0.9896 | 10.9969 | 899 | 0.8690 | 0.6037 | 0.5905 | 0.5937 | 0.6037 |
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+ | 0.9479 | 12.0 | 981 | 0.8705 | 0.6406 | 0.6268 | 0.6307 | 0.6406 |
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+ | 0.898 | 12.9908 | 1062 | 0.8569 | 0.6498 | 0.6440 | 0.6465 | 0.6498 |
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+ | 0.9101 | 13.9939 | 1144 | 0.8736 | 0.6129 | 0.6091 | 0.6179 | 0.6129 |
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+ | 0.8431 | 14.9969 | 1226 | 0.8684 | 0.6452 | 0.6419 | 0.6447 | 0.6452 |
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+ | 0.8187 | 16.0 | 1308 | 0.9032 | 0.6221 | 0.6199 | 0.6207 | 0.6221 |
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+ | 0.7614 | 16.9908 | 1389 | 0.9013 | 0.6359 | 0.6305 | 0.6434 | 0.6359 |
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+ | 0.725 | 17.9939 | 1471 | 0.9702 | 0.5991 | 0.5975 | 0.6072 | 0.5991 |
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+ | 0.6938 | 18.9969 | 1553 | 0.9598 | 0.6728 | 0.6660 | 0.6840 | 0.6728 |
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+ | 0.6761 | 20.0 | 1635 | 0.9886 | 0.6083 | 0.6112 | 0.6242 | 0.6083 |
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+ | 0.5865 | 20.9908 | 1716 | 0.9367 | 0.6498 | 0.6428 | 0.6432 | 0.6498 |
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+ | 0.5857 | 21.9939 | 1798 | 0.9694 | 0.6313 | 0.6322 | 0.6331 | 0.6313 |
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+ | 0.556 | 22.9969 | 1880 | 1.0212 | 0.6359 | 0.6296 | 0.6574 | 0.6359 |
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+ | 0.4871 | 24.0 | 1962 | 1.0328 | 0.5945 | 0.5879 | 0.5951 | 0.5945 |
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+ | 0.5254 | 24.9908 | 2043 | 1.0132 | 0.5945 | 0.5917 | 0.5968 | 0.5945 |
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+ | 0.5054 | 25.9939 | 2125 | 1.0385 | 0.5945 | 0.5944 | 0.5988 | 0.5945 |
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+ | 0.4706 | 26.9969 | 2207 | 1.0626 | 0.6037 | 0.5983 | 0.6100 | 0.6037 |
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+ | 0.418 | 28.0 | 2289 | 1.0531 | 0.5806 | 0.5774 | 0.5830 | 0.5806 |
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+ | 0.455 | 28.9908 | 2370 | 1.0340 | 0.6083 | 0.6039 | 0.6151 | 0.6083 |
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+ | 0.4414 | 29.7248 | 2430 | 1.0280 | 0.6129 | 0.6087 | 0.6161 | 0.6129 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.44.2
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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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