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update model card README.md
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README.md
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---
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license: apache-2.0
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base_model: google/vit-base-patch16-224
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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: vit-base-patch16-224-finetuned-hateful-meme-restructured-balanced
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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: validation
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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: 0.556
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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-base-patch16-224-finetuned-hateful-meme-restructured-balanced
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.7145
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- Accuracy: 0.556
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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: 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: 10
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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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| 0.7016 | 0.98 | 47 | 0.7243 | 0.512 |
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| 0.6676 | 1.99 | 95 | 0.7139 | 0.544 |
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| 0.626 | 2.99 | 143 | 0.7145 | 0.556 |
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| 0.6042 | 4.0 | 191 | 0.7342 | 0.556 |
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| 0.5672 | 4.98 | 238 | 0.7481 | 0.548 |
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| 0.5339 | 5.99 | 286 | 0.7458 | 0.532 |
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| 0.5266 | 6.99 | 334 | 0.7662 | 0.536 |
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| 0.5102 | 8.0 | 382 | 0.7832 | 0.544 |
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| 0.4808 | 8.98 | 429 | 0.7898 | 0.53 |
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| 0.4698 | 9.84 | 470 | 0.7844 | 0.534 |
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### Framework versions
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- Transformers 4.31.0
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- Pytorch 2.0.1+cu117
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- Datasets 2.13.1
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- Tokenizers 0.13.3
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