Ft Vilt B32 Mlm
This model is a fine-tuned version of dandelin/vilt-b32-mlm on the vqa dataset.
Model source
- Kind
- Other
- File size
- 430 MB
- Base model
- dandelin/vilt-b32-mlm
- Version
- vRevision 60dffa8
- Updated
- Sep 25, 2026
- Downloads
- 17
- Tasks
- Captioning
- Source
- Hugging Face
Source description
ft-vilt-b32-mlm
This model is a fine-tuned version of dandelin/vilt-b32-mlm on the vqa dataset.
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: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
Sources
1 source
Sources
1 sourceVerified Sep 11
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer datasets: - vqa model-index: - name: ft-vilt-b32-mlm results: [] --- <!-- 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. --> # ft-vilt-b32-mlm This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the vqa dataset. ## 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: 8 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.0 - Datasets 2.15.0 - Tokenizers 0.15.0
Source context: 17 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo aisuko/ft-vilt-b32-mlm
