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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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- bleu |
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model-index: |
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- name: vit-swin-base-224-gpt2-image-captioning |
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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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# vit-swin-base-224-gpt2-image-captioning |
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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: 0.0001 |
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- Rouge1: 99.2148 |
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- Rouge2: 99.1824 |
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- Rougel: 99.22 |
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- Rougelsum: 99.2169 |
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- Bleu: 96.4656 |
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- Gen Len: 10.4161 |
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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: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:| |
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| 0.622 | 11.36 | 2000 | 0.0330 | 91.0769 | 88.8333 | 90.7025 | 90.7277 | 84.8472 | 10.4161 | |
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| 0.0547 | 22.73 | 4000 | 0.0015 | 99.0694 | 98.9636 | 99.0615 | 99.0613 | 96.1312 | 10.4161 | |
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| 0.0238 | 34.09 | 6000 | 0.0007 | 99.1681 | 99.0942 | 99.167 | 99.1646 | 96.3754 | 10.4161 | |
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| 0.0046 | 45.45 | 8000 | 0.0001 | 99.2225 | 99.1781 | 99.217 | 99.2171 | 96.4412 | 10.4161 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.0 |
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