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IC_ver6e_coco_swin_gpt2_50Apc_1e
VK246/IC_ver6e_coco_swin_gpt2_50Apc_1e
IC_ver6e_coco_swin_gpt2_50Apc_1e is a image-text-to-text model from VK246. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Aug 17, 2023
- Updated
- Aug 17, 2023
- SHA
- 9e213c04
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Base models
Datasets
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
IC_ver6e_coco_swin_gpt2_50Apc_1e
This model is a fine-tuned version of VK246/IC_ver6d_coco_swin_gpt2_50Bpc_1e on the coco dataset. It achieves the following results on the evaluation set:
- Loss: 0.7783
- Cider: 19.1116
- Rouge1: 42.2076
- Rouge2: 16.6791
- Rougel: 38.4352
- Rougelsum: 38.4324
- Bleu-1: 42.9768
- Bleu-2: 25.0535
- Bleu-3: 15.8932
- Bleu-4: 10.5581
- Gen Len: 11.2806
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Cider | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu-1 | Bleu-2 | Bleu-3 | Bleu-4 | Gen Len |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.7299 | 0.17 | 500 | 0.8169 | 15.1223 | 40.4746 | 15.1013 | 36.817 | 36.8166 | 41.7335 | 23.5713 | 14.621 | 9.566 | 11.2806 |
| 0.7243 | 0.34 | 1000 | 0.8063 | 15.7288 | 41.2081 | 15.8926 | 37.4018 | 37.4016 | 42.2656 | 24.2595 | 15.2602 | 10.0788 | 11.2806 |
| 0.7396 | 0.51 | 1500 | 0.7999 | 15.5164 | 41.6231 | 16.1665 | 38.0103 | 38.0119 | 42.0958 | 24.3223 | 15.2851 | 10.0869 | 11.2806 |
| 0.7507 | 0.68 | 2000 | 0.7879 | 15.3421 | 41.9871 | 16.4909 | 38.2491 | 38.2515 | 42.6606 | 24.7464 | 15.6329 | 10.3731 | 11.2806 |
| 0.7712 | 0.85 | 2500 | 0.7820 | 11.751 | 41.9906 | 16.5153 | 38.2624 | 38.2634 | 42.8539 | 24.8663 | 15.7151 | 10.3989 | 11.2806 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Aug 17, 2023
- Updated
- Aug 17, 2023