My Vqa Model
This model is a fine-tuned version of dandelin/vilt-b32-mlm on an unknown dataset.
Source du modèle
- Type
- Other
- Taille du fichier
- 431 MB
- Modèle de base
- dandelin/vilt-b32-mlm
- Version
- vRevision e9735aa
- Mis à jour
- 23 oct. 2026
- Téléchargements
- 3
- Source
- Hugging Face
Description de la source
my_vqa_model
This model is a fine-tuned version of dandelin/vilt-b32-mlm on an unknown 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
Sources
1 source
Sources
1 sourceVérifié 23 oct.
Artefacts du modèle
1 artefact
Artefacts du modèle
1 artefactExtraits de sources
3 extraits
Extraits de sources
3 extraits--- library_name: transformers license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer model-index: - name: my_vqa_model 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. --> # my_vqa_model This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on an unknown 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 ### Framework versions - Transformers 4.56.1 - Pytorch 2.8.0+cu126 - Datasets 4.0.0 - Tokenizers 0.22.0
Source context: 3 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo Maria-pro/my_vqa_model
Source context: 4 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo Maria-pro/my_vqa_model
