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Vilt Finetuned 100

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

ArtModellBasismodelldandelin/vilt-b32-mlmVersionRevision fd9d28bCBewertung

Modellquelle

Art
Modell
Dateigröße
431 MB
Basismodell
dandelin/vilt-b32-mlm
Version
vRevision fd9d28b
Aktualisiert
30. Okt. 2026
Downloads
8
Quelle
Hugging Face

Quellenbeschreibung

vilt_finetuned_100

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: 4
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Framework versions

  • Transformers 4.51.3
  • Pytorch 2.6.0+cu124
  • Datasets 3.6.0
  • Tokenizers 0.21.1

Quellen

1 Quelle
Hugging FacePrimärVersion fd9d28be2e02266365cf11d94e3d2bbe52765412
huggingface.co/bangbrecho/vilt_finetuned_100/tree/fd9d28be2e02266365cf11d94e3d2bbe52765412

Verifiziert 30. Okt.

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Modellartefakte

1 Artefakt

model.safetensors

safetensors · FP32 (tensor header) · 431 MB · SHA-256 35dcaea7b5de…861f · Hugging Face

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Quellenauszüge

3 Auszüge

--- library_name: transformers license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer datasets: - vqa model-index: - name: vilt_finetuned_100 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_finetuned_100 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.51.3 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

Source context: 8 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo bangbrecho/vilt_finetuned_100

Source context: 13 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo bangbrecho/vilt_finetuned_100