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ViLT

This model is a fine-tuned version of dandelin/vilt-b32-mlm on the vqav2 dataset.

ArtOtherBasismodelldandelin/vilt-b32-mlmVersionRevision 0824c04CBewertung

Modellquelle

Art
Other
Dateigröße
431 MB
Basismodell
dandelin/vilt-b32-mlm
Version
vRevision 0824c04
Aktualisiert
18. Sept. 2026
Downloads
12
Quelle
Hugging Face

Quellenbeschreibung

ViLT

This model is a fine-tuned version of dandelin/vilt-b32-mlm on the vqa_v2 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Framework versions

  • Transformers 4.37.0
  • Pytorch 2.1.2
  • Datasets 2.1.0
  • Tokenizers 0.15.1

Quellen

1 Quelle
Hugging FacePrimärVersion 0824c04cc27ecee749eb3d1e95b3b1fcf95bda99
huggingface.co/HelloWorld2307/ViLT/tree/0824c04cc27ecee749eb3d1e95b3b1fcf95bda99

Verifiziert 4. Sept.

Modellartefakte

1 Artefakt

model.safetensors

safetensors · 431 MB · SHA-256 8f06d086bad9…cb8d · Hugging Face

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Quellenauszüge

2 Auszüge

--- license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer datasets: - vqa_v2 model-index: - name: ViLT 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. --> # ViLT This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the vqa_v2 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1

Source context: 12 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo HelloWorld2307/ViLT