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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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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: finetuned-vit-doc-text-classifer |
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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: ernie-ai/image-text-examples-ar-cn-latin-notext |
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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: 0.9029850746268657 |
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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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# finetuned-vit-doc-text-classifer |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the ernie-ai/image-text-examples-ar-cn-latin-notext dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3107 |
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- Accuracy: 0.9030 |
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## Model description |
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It is an image classificatin model fine-tuned to predict whether an images contains text and if that text is Latin script, Chinese or Arabic. It also classifies non-text images. |
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## Training and evaluation data |
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Dataset: [ernie-ai/image-text-examples-ar-cn-latin-notext] |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0002 |
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- train_batch_size: 16 |
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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: 8 |
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- mixed_precision_training: Native AMP |
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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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| 0.2719 | 2.08 | 100 | 0.4120 | 0.8657 | |
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| 0.1027 | 4.17 | 200 | 0.3907 | 0.8881 | |
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| 0.0723 | 6.25 | 300 | 0.3107 | 0.9030 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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