---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-base-spelling-de
  results: []
widget:
- text: "das idst ein neuZr test"
  example_title: "1"
---

<!-- 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. -->

# bart-base-spelling-de

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1065
- Cer: 0.2022

## Model description

This is a proof of concept spelling correction model for german.

## Intended uses & limitations

This is work in progress, be aware that the model can produce artifacts. 
You can test the model using the pipeline interface:

```python
from transformers import pipeline

fix_spelling = pipeline("text2text-generation",model="oliverguhr/spelling-correction-german-base")

print(fix_spelling("das idst ein neuZr test",max_length=2048))
```



## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.2803        | 0.11  | 1000  | 0.1978          | 0.9429 |
| 0.1688        | 0.21  | 2000  | 0.1472          | 0.9426 |
| 0.121         | 0.32  | 3000  | 0.1381          | 0.9424 |
| 0.1722        | 0.43  | 4000  | 0.1340          | 0.9425 |
| 0.1502        | 0.54  | 5000  | 0.1292          | 0.9423 |
| 0.1556        | 0.64  | 6000  | 0.1260          | 0.9424 |
| 0.1624        | 0.75  | 7000  | 0.1246          | 0.9425 |
| 0.1337        | 0.86  | 8000  | 0.1213          | 0.9424 |
| 0.131         | 0.96  | 9000  | 0.1195          | 0.9423 |
| 0.1137        | 1.07  | 10000 | 0.1178          | 0.9424 |
| 0.0958        | 1.18  | 11000 | 0.1166          | 0.9422 |
| 0.1067        | 1.28  | 12000 | 0.1147          | 0.9422 |
| 0.1201        | 1.39  | 13000 | 0.1135          | 0.9423 |
| 0.1115        | 1.5   | 14000 | 0.1111          | 0.9423 |
| 0.1284        | 1.61  | 15000 | 0.1101          | 0.9422 |
| 0.0947        | 1.71  | 16000 | 0.1085          | 0.9422 |
| 0.1081        | 1.82  | 17000 | 0.1073          | 0.9422 |
| 0.099         | 1.93  | 18000 | 0.1065          | 0.9422 |


### Framework versions

- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.1.0
- Tokenizers 0.12.1