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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 9c266b7Ccalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
431 MB
Modelo base
dandelin/vilt-b32-mlm
Versión
vRevision 9c266b7
Actualizado
19 sept 2026
Descargas
15
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: 1
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 1

Training results

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.0.0
  • Datasets 2.1.0
  • Tokenizers 0.14.1

Fuentes

1 fuente
Hugging FacePrincipalVersión 9c266b7c9ba266f6c15db32b3f95afef73d4885f
huggingface.co/tanish1403/vilt_finetuned_200/tree/9c266b7c9ba266f6c15db32b3f95afef73d4885f

Verificado 4 sept

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 431 MB · SHA-256 95c14c611fc8…4da4 · Hugging Face

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Extractos de fuentes

2 extractos

--- 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: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.35.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.14.1

Source context: 15 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo tanish1403/vilt_finetuned_200