---
base_model: Ransaka/sinhala-ocr-model
tags:
- generated_from_trainer
model-index:
- name: sinhala-ocr-model-v2
  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. -->

# sinhala-ocr-model-v2

This model is a fine-tuned version of [Ransaka/sinhala-ocr-model](https://huggingface.co/Ransaka/sinhala-ocr-model) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2306
- Cer: 0.5161

## 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: 8e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 6000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.543         | 3.27  | 500  | 6.2682          | 0.7086 |
| 2.6146        | 6.54  | 1000 | 5.8348          | 0.6390 |
| 1.8448        | 9.8   | 1500 | 5.8076          | 0.6166 |
| 1.3887        | 13.07 | 2000 | 6.0250          | 0.6072 |
| 1.0271        | 16.34 | 2500 | 5.9971          | 0.5707 |
| 0.8891        | 19.61 | 3000 | 5.9803          | 0.5630 |
| 0.6548        | 22.88 | 3500 | 6.0045          | 0.5542 |
| 0.4939        | 26.14 | 4000 | 6.0223          | 0.5354 |
| 0.322         | 29.41 | 4500 | 6.1360          | 0.5233 |
| 0.2459        | 32.68 | 5000 | 6.1166          | 0.5220 |
| 0.123         | 35.95 | 5500 | 6.1740          | 0.5162 |
| 0.1575        | 39.22 | 6000 | 6.2306          | 0.5161 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.16.0
- Tokenizers 0.15.0