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IC_ver5a_coco_swin_gpt2_05pc_1e
VK246/IC_ver5a_coco_swin_gpt2_05pc_1e
IC_ver5a_coco_swin_gpt2_05pc_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
- edb5f3f8
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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_ver5a_coco_swin_gpt2_05pc_1e
This model is a fine-tuned version of on the coco dataset. It achieves the following results on the evaluation set:
- Loss: 1.2245
- Rouge1: 22.0956
- Rouge2: 2.7707
- Rougel: 20.6806
- Rougelsum: 20.6915
- Bleu: 1.2101
- 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 |
|---|---|---|---|---|---|---|---|---|---|
| 2.1625 | 0.42 | 25 | 1.2744 | 22.7707 | 2.4514 | 20.6431 | 20.6829 | 0.0 | 11.1124 |
| 1.3438 | 0.85 | 50 | 1.2374 | 21.3271 | 2.4116 | 19.7122 | 19.7385 | 0.7474 | 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