Instructions to use aqachun/vilt_finetuned_200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aqachun/vilt_finetuned_200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="aqachun/vilt_finetuned_200")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("aqachun/vilt_finetuned_200") model = AutoModelForVisualQuestionAnswering.from_pretrained("aqachun/vilt_finetuned_200") - Notebooks
- Google Colab
- Kaggle
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 4.33.1
- Pytorch 1.12.1+cu113
- Datasets 2.14.5
- Tokenizers 0.13.3
- Downloads last month
- 13
Model tree for aqachun/vilt_finetuned_200
Base model
dandelin/vilt-b32-mlm