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IC_ver5b_coco_swin_gpt2_01pc_1e
VK246/IC_ver5b_coco_swin_gpt2_01pc_1e
IC_ver5b_coco_swin_gpt2_01pc_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
- Jul 12, 2023
- Updated
- Jul 12, 2023
- SHA
- 2d973f21
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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_ver5b_coco_swin_gpt2_01pc_1e
This model is a fine-tuned version of VK246/IC_ver5a_coco_swin_gpt2_05pc_1e on the coco dataset. It achieves the following results on the evaluation set:
- Loss: 1.1266
- Rouge1: 27.4772
- Rouge2: 5.9305
- Rougel: 25.1138
- Rougelsum: 25.1235
- Bleu: 2.437
- Gen Len: 11.1124
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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|---|---|---|---|---|---|---|---|---|---|
| 1.2093 | 0.42 | 25 | 1.1552 | 22.8898 | 3.6353 | 20.6781 | 20.6737 | 1.1554 | 11.1124 |
| 1.2149 | 0.85 | 50 | 1.1358 | 26.2857 | 5.2765 | 24.0266 | 24.0308 | 2.1954 | 11.1124 |
Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Jul 12, 2023
- Updated
- Jul 12, 2023