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---
license: apache-2.0
base_model: jordyvl/vit-base_rvl-cdip
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base_rvl_cdip_aurc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base_rvl_cdip_aurc
This model is a fine-tuned version of [jordyvl/vit-base_rvl-cdip](https://huggingface.co/jordyvl/vit-base_rvl-cdip) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2759
- Accuracy: 0.893
- Brier Loss: 0.1798
- Nll: 0.8614
- F1 Micro: 0.893
- F1 Macro: 0.8928
- Ece: 0.0750
- Aurc: 0.0215
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| 0.0303 | 1.0 | 500 | 0.1865 | 0.8795 | 0.1840 | 1.2087 | 0.8795 | 0.8791 | 0.0495 | 0.0241 |
| 0.0262 | 2.0 | 1000 | 0.2146 | 0.8788 | 0.1909 | 1.1956 | 0.8788 | 0.8789 | 0.0603 | 0.0257 |
| 0.0121 | 3.0 | 1500 | 0.2117 | 0.886 | 0.1799 | 1.0878 | 0.886 | 0.8865 | 0.0611 | 0.0230 |
| 0.0057 | 4.0 | 2000 | 0.2279 | 0.8878 | 0.1803 | 1.0108 | 0.8878 | 0.8879 | 0.0678 | 0.0228 |
| 0.0038 | 5.0 | 2500 | 0.2491 | 0.8872 | 0.1827 | 0.9661 | 0.8872 | 0.8877 | 0.0725 | 0.0234 |
| 0.0028 | 6.0 | 3000 | 0.2398 | 0.89 | 0.1806 | 0.9378 | 0.89 | 0.8901 | 0.0725 | 0.0215 |
| 0.0016 | 7.0 | 3500 | 0.2736 | 0.891 | 0.1792 | 0.8975 | 0.891 | 0.8914 | 0.0744 | 0.0221 |
| 0.0014 | 8.0 | 4000 | 0.2357 | 0.8905 | 0.1811 | 0.8993 | 0.8905 | 0.8910 | 0.0764 | 0.0210 |
| 0.001 | 9.0 | 4500 | 0.2714 | 0.8898 | 0.1807 | 0.8650 | 0.8898 | 0.8897 | 0.0783 | 0.0213 |
| 0.0009 | 10.0 | 5000 | 0.2759 | 0.893 | 0.1798 | 0.8614 | 0.893 | 0.8928 | 0.0750 | 0.0215 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.2.0.dev20231002
- Datasets 2.7.1
- Tokenizers 0.13.3
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