This model is a fine-tuned version of microsoft/dit-base-finetuned-rvlcdip on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.4756
Accuracy: 0.936

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: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.5664	0.9577	17	1.3503	0.624
1.1861	1.9577	34	0.9458	0.888
0.7973	2.9577	51	0.6490	0.92
0.7129	3.9577	68	0.5158	0.928
0.5476	4.9577	85	0.4756	0.936

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Pratigya0/dit-base-finetuned-rvlcdip-finetuned-document-classifier

dit-base-finetuned-rvlcdip-finetuned-document-classifier

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Pratigya0/
dit-base-finetuned-rvlcdip-finetuned-document-classifier