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--- |
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base_model: VK246/IC_ver6K_coco_swin_gpt2_50A_1e |
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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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model-index: |
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- name: IC_ver6L_coco_swin_gpt2_50B_1e |
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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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# IC_ver6L_coco_swin_gpt2_50B_1e |
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This model is a fine-tuned version of [VK246/IC_ver6K_coco_swin_gpt2_50A_1e](https://huggingface.co/VK246/IC_ver6K_coco_swin_gpt2_50A_1e) on the coco dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8371 |
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- Cider: 72.6054 |
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- Rouge1: 41.2906 |
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- Rouge2: 15.8851 |
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- Rougel: 37.3963 |
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- Rougelsum: 37.4014 |
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- Bleu-1: 42.3937 |
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- Bleu-2: 24.3104 |
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- Bleu-3: 15.291 |
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- Bleu-4: 10.0894 |
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- Gen Len: 11.3063 |
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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: 96 |
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- eval_batch_size: 96 |
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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: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Cider | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Gen Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-------:|:-------:|:-------:|:-------:| |
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| 0.4535 | 0.34 | 1000 | 0.9282 | 70.2558 | 40.6455 | 15.1383 | 36.6998 | 36.6999 | 41.838 | 23.6151 | 14.6823 | 9.6083 | 11.3063 | |
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| 0.5716 | 0.68 | 2000 | 0.8371 | 72.6054 | 41.2906 | 15.8851 | 37.3963 | 37.4014 | 42.3937 | 24.3104 | 15.291 | 10.0894 | 11.3063 | |
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### Framework versions |
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- Transformers 4.32.0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.4 |
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- Tokenizers 0.13.3 |
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