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ViTGPT2_vizwiz

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vision-encoder-decoder
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+ ---
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+ tags:
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+ - generated_from_trainer
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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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+
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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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+
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+ # ViTGPT2_vizwiz
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+
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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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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+
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+
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+ ### Framework versions
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+
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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