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Vilt Finetuned 200

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

TipoOtherModelo basedandelin/vilt-b32-mlmVersiónRevision 81747e0Unratedcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
431 MB
Modelo base
dandelin/vilt-b32-mlm
Versión
vRevision 81747e0
Actualizado
5 nov 2026
Descargas
10
Fuente
Hugging Face

Descripción de la fuente

vilt_finetuned_200

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1

Fuentes

1 fuente
Hugging FacePrincipalVersión 81747e0eae07aa6358d683900b7dbcc9104990b9
huggingface.co/dmavkgo/vilt_finetuned_200/tree/81747e0eae07aa6358d683900b7dbcc9104990b9

Verificado 5 nov

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Artefactos del modelo

1 artefacto

model.safetensors

safetensors · FP32 (unverified) · 431 MB · SHA-256 ffa93de1812c…46d9 · Hugging Face

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Extractos de fuentes

3 extractos

--- license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer datasets: - vqa model-index: - name: vilt_finetuned_200 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_200 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: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.2 - Tokenizers 0.19.1

Source context: 10 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo dmavkgo/vilt_finetuned_200

Source context: 9 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo dmavkgo/vilt_finetuned_200