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--- |
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tags: |
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- generated_from_trainer |
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- image-to-text |
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model-index: |
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- name: ViTGPT2_vizwiz |
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results: [] |
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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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# ViTGPT2_vizwiz |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0719 |
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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: 2e-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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- distributed_type: multi-GPU |
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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: 3.0 |
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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 | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 0.1207 | 0.07 | 1000 | 0.0906 | |
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| 0.0916 | 0.14 | 2000 | 0.0861 | |
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| 0.0879 | 0.2 | 3000 | 0.0840 | |
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| 0.0856 | 0.27 | 4000 | 0.0822 | |
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| 0.0834 | 0.34 | 5000 | 0.0806 | |
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| 0.0817 | 0.41 | 6000 | 0.0795 | |
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| 0.0812 | 0.48 | 7000 | 0.0785 | |
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| 0.0808 | 0.55 | 8000 | 0.0779 | |
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| 0.0796 | 0.61 | 9000 | 0.0771 | |
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| 0.0786 | 0.68 | 10000 | 0.0767 | |
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| 0.0774 | 0.75 | 11000 | 0.0762 | |
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| 0.0772 | 0.82 | 12000 | 0.0758 | |
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| 0.0756 | 0.89 | 13000 | 0.0754 | |
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| 0.0759 | 0.96 | 14000 | 0.0750 | |
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| 0.0756 | 1.02 | 15000 | 0.0748 | |
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| 0.0726 | 1.09 | 16000 | 0.0745 | |
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| 0.0727 | 1.16 | 17000 | 0.0745 | |
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| 0.0715 | 1.23 | 18000 | 0.0742 | |
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| 0.0726 | 1.3 | 19000 | 0.0741 | |
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| 0.072 | 1.37 | 20000 | 0.0738 | |
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| 0.0723 | 1.43 | 21000 | 0.0735 | |
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| 0.0715 | 1.5 | 22000 | 0.0734 | |
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| 0.0724 | 1.57 | 23000 | 0.0732 | |
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| 0.0723 | 1.64 | 24000 | 0.0730 | |
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| 0.0718 | 1.71 | 25000 | 0.0729 | |
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| 0.07 | 1.78 | 26000 | 0.0728 | |
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| 0.0702 | 1.84 | 27000 | 0.0726 | |
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| 0.0704 | 1.91 | 28000 | 0.0725 | |
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| 0.0703 | 1.98 | 29000 | 0.0725 | |
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| 0.0686 | 2.05 | 30000 | 0.0726 | |
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| 0.0687 | 2.12 | 31000 | 0.0726 | |
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| 0.0688 | 2.19 | 32000 | 0.0724 | |
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| 0.0677 | 2.25 | 33000 | 0.0724 | |
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| 0.0665 | 2.32 | 34000 | 0.0725 | |
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| 0.0684 | 2.39 | 35000 | 0.0723 | |
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| 0.0678 | 2.46 | 36000 | 0.0722 | |
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| 0.0686 | 2.53 | 37000 | 0.0722 | |
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| 0.067 | 2.59 | 38000 | 0.0721 | |
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| 0.0669 | 2.66 | 39000 | 0.0721 | |
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| 0.0673 | 2.73 | 40000 | 0.0721 | |
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| 0.0673 | 2.8 | 41000 | 0.0720 | |
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| 0.0662 | 2.87 | 42000 | 0.0720 | |
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| 0.0681 | 2.94 | 43000 | 0.0719 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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