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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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- coco |
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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 the coco dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7923 |
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- Rouge1: 41.8451 |
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- Rouge2: 16.3493 |
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- Rougel: 38.0288 |
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- Rougelsum: 38.049 |
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- Bleu: 10.2776 |
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- Gen Len: 11.2946 |
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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: 64 |
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- eval_batch_size: 64 |
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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: 2 |
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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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| 1.0018 | 0.38 | 2000 | 0.8860 | 38.6537 | 13.8145 | 35.3932 | 35.3935 | 8.2448 | 11.2946 | |
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| 0.8827 | 0.75 | 4000 | 0.8395 | 40.0458 | 14.8829 | 36.5321 | 36.5366 | 9.1169 | 11.2946 | |
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| 0.8378 | 1.13 | 6000 | 0.8140 | 41.2736 | 15.9576 | 37.5504 | 37.5512 | 9.871 | 11.2946 | |
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| 0.7913 | 1.51 | 8000 | 0.8012 | 41.6642 | 16.1987 | 37.8786 | 37.8891 | 10.0786 | 11.2946 | |
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| 0.7794 | 1.89 | 10000 | 0.7933 | 41.9119 | 16.3738 | 38.1062 | 38.1292 | 10.288 | 11.2946 | |
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### Framework versions |
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- Transformers 4.26.0 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.9.0 |
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- Tokenizers 0.13.2 |
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