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Vilt Finetuned 200

This model is a fine-tuned version of dandelin/vilt-b32-mlm on the None dataset.

TipoOtherModelo basedandelin/vilt-b32-mlmVersiónRevision 3b166ffCcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
431 MB
Modelo base
dandelin/vilt-b32-mlm
Versión
vRevision 3b166ff
Actualizado
23 oct 2026
Descargas
11
Fuente
Hugging Face

Descripción de la fuente

vilt_finetuned_200

This model is a fine-tuned version of dandelin/vilt-b32-mlm on the None 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 4.53.1
  • Pytorch 2.7.1+cu118
  • Datasets 3.6.0
  • Tokenizers 0.21.2

Fuentes

1 fuente
Hugging FacePrincipalVersión 3b166ff8ef36bb5d00ce3dc07b6d98da216e04b0
huggingface.co/hiwjddn/vilt_finetuned_200/tree/3b166ff8ef36bb5d00ce3dc07b6d98da216e04b0

Verificado 23 oct

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 431 MB · SHA-256 0af5ebd6f608…8f30 · Hugging Face

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Extractos de fuentes

3 extractos

--- library_name: transformers license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer 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 None 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 4.53.1 - Pytorch 2.7.1+cu118 - Datasets 3.6.0 - Tokenizers 0.21.2

Source context: 11 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo hiwjddn/vilt_finetuned_200

Source context: 4 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo hiwjddn/vilt_finetuned_200