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gerbejon
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webpage_labeling_classifier

Image Classification
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README.md ADDED
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+ ---
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+ library_name: transformers
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+ base_model: gerbejon/webpage_labeling_classifier
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: webpage_labeling_classifier
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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: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9416466826538769
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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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+ # webpage_labeling_classifier
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+
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+ This model is a fine-tuned version of [gerbejon/webpage_labeling_classifier](https://huggingface.co/gerbejon/webpage_labeling_classifier) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1555
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+ - Accuracy: 0.9416
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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: 20
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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 |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|
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+ | 0.2002 | 0.9968 | 78 | 0.1917 | 0.9281 |
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+ | 0.2191 | 1.9936 | 156 | 0.2132 | 0.9097 |
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+ | 0.2067 | 2.9904 | 234 | 0.2522 | 0.9065 |
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+ | 0.1751 | 4.0 | 313 | 0.1931 | 0.9217 |
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+ | 0.1346 | 4.9968 | 391 | 0.1933 | 0.9241 |
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+ | 0.1448 | 5.9936 | 469 | 0.1816 | 0.9313 |
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+ | 0.1389 | 6.9904 | 547 | 0.2027 | 0.9209 |
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+ | 0.1387 | 8.0 | 626 | 0.1696 | 0.9384 |
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+ | 0.1234 | 8.9968 | 704 | 0.1758 | 0.9345 |
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+ | 0.1196 | 9.9936 | 782 | 0.1848 | 0.9305 |
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+ | 0.1213 | 10.9904 | 860 | 0.1769 | 0.9400 |
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+ | 0.1287 | 12.0 | 939 | 0.1421 | 0.9488 |
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+ | 0.117 | 12.9968 | 1017 | 0.2046 | 0.9241 |
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+ | 0.1433 | 13.9936 | 1095 | 0.1769 | 0.9369 |
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+ | 0.0988 | 14.9904 | 1173 | 0.1494 | 0.9496 |
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+ | 0.1136 | 16.0 | 1252 | 0.1571 | 0.9424 |
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+ | 0.086 | 16.9968 | 1330 | 0.1712 | 0.9384 |
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+ | 0.089 | 17.9936 | 1408 | 0.1437 | 0.9440 |
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+ | 0.0991 | 18.9904 | 1486 | 0.1510 | 0.9448 |
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+ | 0.0824 | 19.9361 | 1560 | 0.1555 | 0.9416 |
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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 2.4.1+cu121
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+ - Datasets 3.0.0
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+ - Tokenizers 0.19.1
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