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VIT_ara_gpt2
Ahmed007/VIT_ara_gpt2
VIT_ara_gpt2 is a image-text-to-text model from Ahmed007. 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
- Feb 16, 2024
- Updated
- Feb 16, 2024
- SHA
- 94e2bd1a
Try a prompt
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. -->
VIT_ara_gpt2
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 3.3934
- eval_rouge2_precision: 0.0
- eval_rouge2_recall: 0.0
- eval_rouge2_fmeasure: 0.0
- eval_runtime: 1818.7738
- eval_samples_per_second: 1.329
- eval_steps_per_second: 0.042
- epoch: 1.0
- step: 1360
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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1024
- num_epochs: 3
Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- Created
- Feb 16, 2024
- Updated
- Feb 16, 2024