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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ViT-Base-Document-Classifier
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-Document-Classifier
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0415
- Accuracy: 0.9889
- F1: 0.9888
- Precision: 0.9888
- Recall: 0.9888
## 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: 512
- eval_batch_size: 512
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 2048
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0696 | 1.25 | 50 | 0.0566 | 0.9852 | 0.9851 | 0.9852 | 0.9852 |
| 0.0673 | 2.0 | 51 | 0.0549 | 0.9870 | 0.9870 | 0.9870 | 0.9870 |
| 0.0599 | 2.02 | 52 | 0.0545 | 0.9864 | 0.9863 | 0.9863 | 0.9864 |
| 0.0639 | 2.02 | 53 | 0.0551 | 0.9876 | 0.9875 | 0.9875 | 0.9875 |
| 0.0694 | 2.04 | 54 | 0.0539 | 0.9864 | 0.9863 | 0.9863 | 0.9864 |
| 0.0655 | 2.04 | 55 | 0.0528 | 0.9879 | 0.9878 | 0.9878 | 0.9879 |
| 0.0629 | 2.06 | 56 | 0.0519 | 0.9877 | 0.9876 | 0.9876 | 0.9876 |
| 0.0761 | 2.06 | 57 | 0.0532 | 0.9872 | 0.9871 | 0.9871 | 0.9871 |
| 0.0741 | 2.08 | 58 | 0.0524 | 0.9865 | 0.9864 | 0.9864 | 0.9865 |
| 0.0585 | 2.08 | 59 | 0.0519 | 0.9879 | 0.9878 | 0.9878 | 0.9878 |
| 0.0534 | 2.1 | 60 | 0.0504 | 0.9881 | 0.9880 | 0.9880 | 0.9880 |
| 0.056 | 2.1 | 61 | 0.0497 | 0.9876 | 0.9875 | 0.9875 | 0.9875 |
| 0.0588 | 2.12 | 62 | 0.0485 | 0.9878 | 0.9877 | 0.9877 | 0.9877 |
| 0.0554 | 2.12 | 63 | 0.0482 | 0.9872 | 0.9871 | 0.9871 | 0.9872 |
| 0.0674 | 2.13 | 64 | 0.0491 | 0.9870 | 0.9870 | 0.9870 | 0.9869 |
| 0.0613 | 2.15 | 65 | 0.0480 | 0.9877 | 0.9876 | 0.9876 | 0.9876 |
| 0.0688 | 2.15 | 66 | 0.0468 | 0.9877 | 0.9876 | 0.9876 | 0.9876 |
| 0.0677 | 2.17 | 67 | 0.0476 | 0.9874 | 0.9874 | 0.9873 | 0.9874 |
| 0.0598 | 2.17 | 68 | 0.0471 | 0.9874 | 0.9873 | 0.9873 | 0.9873 |
| 0.0658 | 2.19 | 69 | 0.0462 | 0.9877 | 0.9876 | 0.9876 | 0.9876 |
| 0.051 | 2.19 | 70 | 0.0467 | 0.9880 | 0.9879 | 0.9879 | 0.9879 |
| 0.0601 | 2.21 | 71 | 0.0456 | 0.9881 | 0.9880 | 0.9880 | 0.9880 |
| 0.0619 | 2.21 | 72 | 0.0460 | 0.9879 | 0.9878 | 0.9878 | 0.9879 |
| 0.0459 | 2.23 | 73 | 0.0458 | 0.9883 | 0.9882 | 0.9882 | 0.9883 |
| 0.0705 | 2.23 | 74 | 0.0447 | 0.9884 | 0.9883 | 0.9883 | 0.9883 |
| 0.0606 | 2.25 | 75 | 0.0447 | 0.9878 | 0.9878 | 0.9878 | 0.9878 |
| 0.0599 | 3.0 | 76 | 0.0441 | 0.9887 | 0.9886 | 0.9887 | 0.9886 |
| 0.0489 | 3.01 | 77 | 0.0438 | 0.9886 | 0.9885 | 0.9885 | 0.9885 |
| 0.0533 | 3.02 | 78 | 0.0442 | 0.9883 | 0.9882 | 0.9882 | 0.9883 |
| 0.0573 | 3.03 | 79 | 0.0438 | 0.9880 | 0.9879 | 0.9879 | 0.9880 |
| 0.0622 | 3.04 | 80 | 0.0439 | 0.9886 | 0.9885 | 0.9885 | 0.9885 |
| 0.0625 | 3.05 | 81 | 0.0434 | 0.9881 | 0.9880 | 0.9880 | 0.9880 |
| 0.0577 | 3.06 | 82 | 0.0431 | 0.9886 | 0.9885 | 0.9885 | 0.9885 |
| 0.0688 | 3.07 | 83 | 0.0435 | 0.9887 | 0.9886 | 0.9886 | 0.9887 |
| 0.0478 | 3.08 | 84 | 0.0434 | 0.9889 | 0.9888 | 0.9888 | 0.9888 |
| 0.0516 | 3.09 | 85 | 0.0436 | 0.9888 | 0.9887 | 0.9887 | 0.9887 |
| 0.0588 | 3.1 | 86 | 0.0426 | 0.9889 | 0.9888 | 0.9888 | 0.9888 |
| 0.0563 | 3.11 | 87 | 0.0422 | 0.9889 | 0.9888 | 0.9888 | 0.9888 |
| 0.0463 | 3.12 | 88 | 0.0422 | 0.9886 | 0.9886 | 0.9885 | 0.9886 |
| 0.0582 | 3.13 | 89 | 0.0421 | 0.9887 | 0.9886 | 0.9886 | 0.9887 |
| 0.0643 | 3.14 | 90 | 0.0419 | 0.9891 | 0.9890 | 0.9890 | 0.9891 |
| 0.0706 | 3.15 | 91 | 0.0417 | 0.9892 | 0.9891 | 0.9891 | 0.9891 |
| 0.0554 | 3.16 | 92 | 0.0417 | 0.9892 | 0.9891 | 0.9891 | 0.9891 |
| 0.0644 | 3.17 | 93 | 0.0416 | 0.9890 | 0.9890 | 0.9890 | 0.9890 |
| 0.0624 | 3.18 | 94 | 0.0415 | 0.9893 | 0.9892 | 0.9892 | 0.9892 |
| 0.0555 | 3.19 | 95 | 0.0416 | 0.9886 | 0.9886 | 0.9885 | 0.9886 |
| 0.0507 | 3.2 | 96 | 0.0415 | 0.9889 | 0.9888 | 0.9888 | 0.9888 |
| 0.0443 | 3.21 | 97 | 0.0415 | 0.9889 | 0.9888 | 0.9888 | 0.9888 |
| 0.0527 | 3.22 | 98 | 0.0415 | 0.9889 | 0.9888 | 0.9888 | 0.9888 |
| 0.0589 | 3.23 | 99 | 0.0415 | 0.9889 | 0.9888 | 0.9888 | 0.9888 |
| 0.0647 | 3.24 | 100 | 0.0415 | 0.9889 | 0.9888 | 0.9888 | 0.9888 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
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