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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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<!-- 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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# webpage_labeling_classifier |
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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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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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### Training results |
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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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### Framework versions |
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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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