# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("farzanrahmani/vilt_finetuned_200")
model = AutoModelForMultimodalLM.from_pretrained("farzanrahmani/vilt_finetuned_200")Quick Links
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: 2
Training results
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for farzanrahmani/vilt_finetuned_200
Base model
dandelin/vilt-b32-mlm
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="farzanrahmani/vilt_finetuned_200")