ViLT_FT_Balanced_Binary_Abstract_Scenes – AI Model by VladGK | AlphaNeural AI

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VladGK/
ViLT_FT_Balanced_Binary_Abstract_Scenes

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visual-question-answering

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dandelin/vilt-b32-finetuned-vqa

endpoints_compatible

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ViLT_FT_Balanced_Binary_Abstract_Scenes

This model is a fine-tuned version of dandelin/vilt-b32-finetuned-vqa on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.3521

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: 0.0005
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
1.6688	0.17	200	1.6769
1.3841	0.34	400	1.6145
1.3773	0.5	600	1.5574
1.3539	0.67	800	1.5374
1.3458	0.84	1000	1.5044
1.3653	1.01	1200	1.4956
1.3222	1.18	1400	1.4968
1.3362	1.34	1600	1.4855
1.3557	1.51	1800	1.3809
1.3207	1.68	2000	1.3806
1.348	1.85	2200	1.3718
1.3215	2.02	2400	1.3677
1.3299	2.18	2600	1.3793
1.335	2.35	2800	1.3662
1.3033	2.52	3000	1.3628
1.3377	2.69	3200	1.3525
1.3001	2.85	3400	1.3521

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2