# Load model directly
from transformers import AutoProcessor, AutoModelForVisualQuestionAnswering
processor = AutoProcessor.from_pretrained("ChiJuiChen/Lab10_VQA_fulltrain")
model = AutoModelForVisualQuestionAnswering.from_pretrained("ChiJuiChen/Lab10_VQA_fulltrain")Quick Links
Lab10_VQA_fulltrain
This model is a fine-tuned version of dandelin/vilt-b32-mlm on the coco_vqa_small_dataset 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: 32
- 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 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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Model tree for ChiJuiChen/Lab10_VQA_fulltrain
Base model
dandelin/vilt-b32-mlm
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="ChiJuiChen/Lab10_VQA_fulltrain")