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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- preprocessed1024_config |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: vit-model |
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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: preprocessed1024_config |
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type: preprocessed1024_config |
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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: |
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accuracy: 0.6011306532663316 |
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- name: F1 |
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type: f1 |
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value: |
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f1: 0.5956396413406886 |
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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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# vit-model |
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This model is a fine-tuned version of [](https://huggingface.co/) on the preprocessed1024_config dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1353 |
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- Accuracy: {'accuracy': 0.6011306532663316} |
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- F1: {'f1': 0.5956396413406886} |
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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: 8 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:| |
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| 1.224 | 1.0 | 796 | 0.9884 | {'accuracy': 0.5276381909547738} | {'f1': 0.40344173017767304} | |
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| 0.96 | 2.0 | 1592 | 0.9255 | {'accuracy': 0.5621859296482412} | {'f1': 0.5134011716404221} | |
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| 0.8878 | 3.0 | 2388 | 0.9308 | {'accuracy': 0.574748743718593} | {'f1': 0.46867195041352344} | |
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| 0.809 | 4.0 | 3184 | 0.8904 | {'accuracy': 0.6067839195979899} | {'f1': 0.5799288651427482} | |
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| 0.7541 | 5.0 | 3980 | 0.8936 | {'accuracy': 0.5954773869346733} | {'f1': 0.5938876317530138} | |
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| 0.6904 | 6.0 | 4776 | 0.8760 | {'accuracy': 0.6118090452261307} | {'f1': 0.6023012293668115} | |
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| 0.6195 | 7.0 | 5572 | 1.0032 | {'accuracy': 0.5917085427135679} | {'f1': 0.5834559014249068} | |
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| 0.5766 | 8.0 | 6368 | 1.0268 | {'accuracy': 0.6023869346733668} | {'f1': 0.5779800559497847} | |
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| 0.4963 | 9.0 | 7164 | 1.0460 | {'accuracy': 0.5992462311557789} | {'f1': 0.5875334711293277} | |
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| 0.4323 | 10.0 | 7960 | 1.1353 | {'accuracy': 0.6011306532663316} | {'f1': 0.5956396413406886} | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.12.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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