update model card README.md
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README.md
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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-mlo-512-birads
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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: 0.4667085427135678
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- name: F1
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type: f1
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value:
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f1: 0.3786054240333243
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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-mlo-512-birads
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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: 1.0864
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- Accuracy: {'accuracy': 0.4667085427135678}
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- F1: {'f1': 0.3786054240333243}
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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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| 1.103 | 1.0 | 796 | 1.0452 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} |
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| 1.0596 | 2.0 | 1592 | 1.0433 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} |
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| 1.0547 | 3.0 | 2388 | 1.0361 | {'accuracy': 0.4748743718592965} | {'f1': 0.21465076660988078} |
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| 1.047 | 4.0 | 3184 | 1.0395 | {'accuracy': 0.46796482412060303} | {'f1': 0.25128840471066954} |
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| 1.0524 | 5.0 | 3980 | 1.0331 | {'accuracy': 0.4648241206030151} | {'f1': 0.298317360340153} |
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| 1.0268 | 6.0 | 4776 | 1.0224 | {'accuracy': 0.47675879396984927} | {'f1': 0.23426509831984135} |
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| 1.0043 | 7.0 | 5572 | 1.0609 | {'accuracy': 0.417713567839196} | {'f1': 0.3663405670841817} |
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| 0.982 | 8.0 | 6368 | 1.0521 | {'accuracy': 0.44221105527638194} | {'f1': 0.3650005046420297} |
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| 0.9315 | 9.0 | 7164 | 1.0473 | {'accuracy': 0.47738693467336685} | {'f1': 0.3727220695970696} |
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| 0.9319 | 10.0 | 7960 | 1.0864 | {'accuracy': 0.4667085427135678} | {'f1': 0.3786054240333243} |
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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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