---
base_model: kavg/TrOCR-SIN-DeiT
tags:
- generated_from_trainer
model-index:
- name: TrOCR-SIN-DeiT-Handwritten
  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. -->

# TrOCR-SIN-DeiT-Handwritten

This model is a fine-tuned version of [kavg/TrOCR-SIN-DeiT](https://huggingface.co/kavg/TrOCR-SIN-DeiT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9839
- Cer: 0.5253

## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2915        | 3.45  | 100  | 1.8613          | 0.6450 |
| 0.061         | 6.9   | 200  | 1.8118          | 0.5707 |
| 0.0363        | 10.34 | 300  | 2.3998          | 0.6420 |
| 0.0202        | 13.79 | 400  | 2.4144          | 0.6353 |
| 0.0329        | 17.24 | 500  | 2.4393          | 0.6577 |
| 0.0364        | 20.69 | 600  | 1.9231          | 0.5679 |
| 0.004         | 24.14 | 700  | 2.4344          | 0.5866 |
| 0.0167        | 27.59 | 800  | 3.0998          | 0.5744 |
| 0.0269        | 31.03 | 900  | 2.6785          | 0.5804 |
| 0.0151        | 34.48 | 1000 | 2.2443          | 0.5916 |
| 0.0008        | 37.93 | 1100 | 2.1480          | 0.5684 |
| 0.0067        | 41.38 | 1200 | 2.3553          | 0.5625 |
| 0.0198        | 44.83 | 1300 | 2.1915          | 0.5492 |
| 0.0002        | 48.28 | 1400 | 2.0370          | 0.5620 |
| 0.001         | 51.72 | 1500 | 2.4303          | 0.6056 |
| 0.1666        | 55.17 | 1600 | 2.3324          | 0.5627 |
| 0.0001        | 58.62 | 1700 | 2.8753          | 0.5582 |
| 0.0           | 62.07 | 1800 | 2.5749          | 0.5355 |
| 0.0002        | 65.52 | 1900 | 2.8105          | 0.5572 |
| 0.0           | 68.97 | 2000 | 2.5275          | 0.5462 |
| 0.1231        | 72.41 | 2100 | 2.7452          | 0.5477 |
| 0.0           | 75.86 | 2200 | 2.4278          | 0.5403 |
| 0.0           | 79.31 | 2300 | 3.0099          | 0.5487 |
| 0.0           | 82.76 | 2400 | 3.1290          | 0.5467 |
| 0.0           | 86.21 | 2500 | 2.7705          | 0.5263 |
| 0.0           | 89.66 | 2600 | 2.7828          | 0.5275 |
| 0.0           | 93.1  | 2700 | 3.2488          | 0.5345 |
| 0.0           | 96.55 | 2800 | 3.1309          | 0.5273 |
| 0.0           | 100.0 | 2900 | 2.9839          | 0.5253 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1