metadata

license: apache-2.0
base_model: jordyvl/vit-base_rvl-cdip
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: vit-base_rvl_cdip-N1K_aAURC_32
    results: []

vit-base_rvl_cdip-N1K_aAURC_32

This model is a fine-tuned version of jordyvl/vit-base_rvl-cdip on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.5215
Accuracy: 0.888
Brier Loss: 0.1918
Nll: 0.9026
F1 Micro: 0.888
F1 Macro: 0.8883
Ece: 0.0880
Aurc: 0.0205

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: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Brier Loss	Nll	F1 Micro	F1 Macro	Ece	Aurc
0.1629	1.0	500	0.3779	0.8875	0.1721	1.1899	0.8875	0.8877	0.0531	0.0201
0.1234	2.0	1000	0.4074	0.8868	0.1790	1.1333	0.8868	0.8874	0.0647	0.0213
0.0616	3.0	1500	0.4257	0.888	0.1813	1.0677	0.888	0.8879	0.0695	0.0201
0.0303	4.0	2000	0.4595	0.885	0.1869	1.0256	0.885	0.8856	0.0776	0.0222
0.0133	5.0	2500	0.4902	0.8848	0.1922	0.9983	0.8848	0.8849	0.0831	0.0228
0.0083	6.0	3000	0.4941	0.8862	0.1903	0.9464	0.8862	0.8868	0.0850	0.0211
0.0051	7.0	3500	0.5116	0.8875	0.1928	0.9118	0.8875	0.8873	0.0875	0.0207
0.0043	8.0	4000	0.5154	0.8882	0.1910	0.9138	0.8882	0.8887	0.0864	0.0205
0.0041	9.0	4500	0.5221	0.8865	0.1924	0.9101	0.8865	0.8868	0.0896	0.0206
0.0037	10.0	5000	0.5215	0.888	0.1918	0.9026	0.888	0.8883	0.0880	0.0205

Framework versions

Transformers 4.33.3
Pytorch 2.2.0.dev20231002
Datasets 2.7.1
Tokenizers 0.13.3