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Vilt Finetuned 2

This model is a fine-tuned version of dandelin/vilt-b32-mlm on an unknown dataset. It achieves the following results on the evaluation set: Loss: 3.3663 F1: 0.6000 Roc Auc: 0.7866 Accuracy: 0.5735

Fuentehuggingface.co/farishehzad/vilt_finetuned_2/tree/54778fbd118cd412e18ca480d610a80e70d558ae
TipoOtherModelo basedandelin/vilt-b32-mlmVersiónRevision 54778fbCcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
436 MB
Modelo base
dandelin/vilt-b32-mlm
Versión
vRevision 54778fb
Actualizado
19 sept 2026
Descargas
12
Fuente
Hugging Face

Descripción de la fuente

vilt_finetuned_2

This model is a fine-tuned version of dandelin/vilt-b32-mlm on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 3.3663
  • F1: 0.6000
  • Roc Auc: 0.7866
  • Accuracy: 0.5735

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: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training LossEpochStepValidation LossF1Roc AucAccuracy
44.14551.01296.54790.12700.53670.0735
2.96082.02582.76340.43850.69650.3934
2.30463.03872.49190.49480.72040.4412
1.8954.05162.34180.56520.76270.5257
1.47855.06452.64620.57200.77010.5404
1.14916.07742.88050.60740.78840.5772
0.82977.09033.18320.59770.78660.5735
0.72498.010323.26790.60540.79030.5809
2.15549.011613.29260.61190.79400.5846
0.532310.012903.36630.60000.78660.5735

Framework versions

  • Transformers 4.40.0.dev0
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2

Fuentes

1 fuente
Hugging FacePrincipalVersión 54778fbd118cd412e18ca480d610a80e70d558ae
huggingface.co/farishehzad/vilt_finetuned_2/tree/54778fbd118cd412e18ca480d610a80e70d558ae

Verificado 4 sept

Artefactos del modelo

1 artefacto

model.safetensors

safetensors · 436 MB · SHA-256 4bca8711fd06…0480 · Hugging Face

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Extractos de fuentes

2 extractos
Readme4 septhuggingface.co/farishehzad/vilt_finetuned_2

--- license: apache-2.0 base_model: dandelin/vilt-b32-mlm tags: - generated_from_trainer metrics: - f1 - accuracy model-index: - name: vilt_finetuned_2 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_2 This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.3663 - F1: 0.6000 - Roc Auc: 0.7866 - Accuracy: 0.5735 ## 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: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| | 44.1455 | 1.0 | 129 | 6.5479 | 0.1270 | 0.5367 | 0.0735 | | 2.9608 | 2.0 | 258 | 2.7634 | 0.4385 | 0.6965 | 0.3934 | | 2.3046 | 3.0 | 387 | 2.4919 | 0.4948 | 0.7204 | 0.4412 | | 1.895 | 4.0 | 516 | 2.3418 | 0.5652 | 0.7627 | 0.5257 | | 1.4785 | 5.0 | 645 | 2.6462 | 0.5720 | 0.7701 | 0.5404 | | 1.1491 | 6.0 | 774 | 2.8805 | 0.6074 | 0.7884 | 0.5772 | | 0.8297 | 7.0 | 903 | 3.1832 | 0.5977 | 0.7866 | 0.5735 | | 0.7249 | 8.0 | 1032 | 3.2679 | 0.6054 | 0.7903 | 0.5809 | | 2.1554 | 9.0 | 1161 | 3.2926 | 0.6119 | 0.7940 | 0.5846 | | 0.5323 | 10.0 | 1290 | 3.3663 | 0.6000 | 0.7866 | 0.5735 | ### Framework versions - Transformers 4.40.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

Note4 septhuggingface.co/farishehzad/vilt_finetuned_2

Source context: 12 downloads · 0 likes · Pipeline visual-question-answering · Library transformers · Repo farishehzad/vilt_finetuned_2