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--- |
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base_model: microsoft/dit-base |
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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: doc-img-classification |
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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: 0.3483606557377049 |
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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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# doc-img-classification |
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This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0820 |
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- Accuracy: 0.3484 |
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- Weighted f1: 0.2183 |
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- Micro f1: 0.3484 |
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- Macro f1: 0.2173 |
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- Weighted recall: 0.3484 |
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- Micro recall: 0.3484 |
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- Macro recall: 0.3545 |
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- Weighted precision: 0.4016 |
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- Micro precision: 0.3484 |
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- Macro precision: 0.3764 |
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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: 0.001 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:| |
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| 1.7064 | 0.9855 | 17 | 1.0820 | 0.3484 | 0.2183 | 0.3484 | 0.2173 | 0.3484 | 0.3484 | 0.3545 | 0.4016 | 0.3484 | 0.3764 | |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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