Vilt Finetuned 200
This model is a fine-tuned version of dandelin/vilt-b32-mlm on the None dataset.
Modellquelle
- Art
- Other
- Dateigröße
- 431 MB
- Basismodell
- dandelin/vilt-b32-mlm
- Version
- vRevision 3b166ff
- Aktualisiert
- 23. Okt. 2026
- Downloads
- 11
- Quelle
- Hugging Face
Quellenbeschreibung
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
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 23. Okt.
Modellartefakte
1 Artefakt
Modellartefakte
1 ArtefaktQuellenauszüge
3 Auszüge
Quellenauszüge
3 Auszüge--- 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
