Instructions to use vamsidulam/graphcorevqa_03 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vamsidulam/graphcorevqa_03 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="vamsidulam/graphcorevqa_03")# Load model directly from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering processor = AutoProcessor.from_pretrained("vamsidulam/graphcorevqa_03") model = AutoModelForVisualQuestionAnswering.from_pretrained("vamsidulam/graphcorevqa_03") - Notebooks
- Google Colab
- Kaggle
graphcorevqa_03
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
Training results
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
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