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--- |
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base_model: VK246/IC_ver6G_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_ver6H_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_ver6H_coco_swin_gpt2_50B_1e |
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This model is a fine-tuned version of [VK246/IC_ver6G_coco_swin_gpt2_50A_1e](https://huggingface.co/VK246/IC_ver6G_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: 0.7969 |
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- Cider: 7.3588 |
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- Rouge1: 41.9295 |
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- Rouge2: 16.3455 |
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- Rougel: 37.9811 |
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- Rougelsum: 37.9766 |
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- Bleu-1: 42.8743 |
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- Bleu-2: 24.7756 |
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- Bleu-3: 15.6692 |
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- Bleu-4: 10.4429 |
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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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| 0.6159 | 0.34 | 1000 | 0.8323 | 6.7172 | 41.0274 | 15.5809 | 37.2211 | 37.2045 | 42.2207 | 24.0365 | 15.0562 | 9.9118 | 11.3063 | |
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| 0.6802 | 0.68 | 2000 | 0.7969 | 7.3588 | 41.9295 | 16.3455 | 37.9811 | 37.9766 | 42.8743 | 24.7756 | 15.6692 | 10.4429 | 11.3063 | |
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### Framework versions |
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- Transformers 4.31.0 |
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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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