---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-base-spelling-nl-1m-3
  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. -->

# bart-base-spelling-nl-1m-3

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: 0.0053
- Cer: 0.0117

## 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: 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

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Cer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.0834        | 0.01  | 1000   | 0.0603          | 0.9216 |
| 0.0566        | 0.02  | 2000   | 0.0467          | 0.9217 |
| 0.0534        | 0.03  | 3000   | 0.0436          | 0.9216 |
| 0.0461        | 0.04  | 4000   | 0.0392          | 0.9216 |
| 0.0416        | 0.05  | 5000   | 0.0354          | 0.9216 |
| 0.0433        | 0.06  | 6000   | 0.0336          | 0.9216 |
| 0.045         | 0.08  | 7000   | 0.0315          | 0.9216 |
| 0.0452        | 0.09  | 8000   | 0.0305          | 0.9216 |
| 0.04          | 0.1   | 9000   | 0.0281          | 0.9216 |
| 0.0307        | 0.11  | 10000  | 0.0273          | 0.9216 |
| 0.0382        | 0.12  | 11000  | 0.0269          | 0.9216 |
| 0.036         | 0.13  | 12000  | 0.0254          | 0.9216 |
| 0.0412        | 0.14  | 13000  | 0.0258          | 0.9216 |
| 0.0404        | 0.15  | 14000  | 0.0238          | 0.9216 |
| 0.0265        | 0.16  | 15000  | 0.0239          | 0.9216 |
| 0.029         | 0.17  | 16000  | 0.0235          | 0.9216 |
| 0.0295        | 0.18  | 17000  | 0.0218          | 0.9216 |
| 0.0262        | 0.19  | 18000  | 0.0214          | 0.9216 |
| 0.0274        | 0.21  | 19000  | 0.0222          | 0.9216 |
| 0.0317        | 0.22  | 20000  | 0.0204          | 0.9216 |
| 0.0248        | 0.23  | 21000  | 0.0204          | 0.9216 |
| 0.0258        | 0.24  | 22000  | 0.0195          | 0.9216 |
| 0.0247        | 0.25  | 23000  | 0.0188          | 0.9216 |
| 0.0285        | 0.26  | 24000  | 0.0191          | 0.9215 |
| 0.031         | 0.27  | 25000  | 0.0192          | 0.9216 |
| 0.0267        | 0.28  | 26000  | 0.0188          | 0.9216 |
| 0.0245        | 0.29  | 27000  | 0.0177          | 0.9216 |
| 0.0258        | 0.3   | 28000  | 0.0177          | 0.9216 |
| 0.0235        | 0.31  | 29000  | 0.0169          | 0.9216 |
| 0.0235        | 0.32  | 30000  | 0.0176          | 0.9216 |
| 0.0223        | 0.34  | 31000  | 0.0165          | 0.9216 |
| 0.0219        | 0.35  | 32000  | 0.0167          | 0.9216 |
| 0.0214        | 0.36  | 33000  | 0.0165          | 0.9216 |
| 0.0232        | 0.37  | 34000  | 0.0163          | 0.9216 |
| 0.0192        | 0.38  | 35000  | 0.0162          | 0.9216 |
| 0.0159        | 0.39  | 36000  | 0.0160          | 0.9216 |
| 0.0205        | 0.4   | 37000  | 0.0150          | 0.9216 |
| 0.0197        | 0.41  | 38000  | 0.0152          | 0.9216 |
| 0.0205        | 0.42  | 39000  | 0.0150          | 0.9216 |
| 0.0182        | 0.43  | 40000  | 0.0145          | 0.9216 |
| 0.0204        | 0.44  | 41000  | 0.0139          | 0.9216 |
| 0.0201        | 0.45  | 42000  | 0.0146          | 0.9216 |
| 0.0202        | 0.46  | 43000  | 0.0132          | 0.9215 |
| 0.0219        | 0.48  | 44000  | 0.0146          | 0.9216 |
| 0.0161        | 0.49  | 45000  | 0.0134          | 0.9215 |
| 0.0172        | 0.5   | 46000  | 0.0137          | 0.9216 |
| 0.0199        | 0.51  | 47000  | 0.0133          | 0.9215 |
| 0.0211        | 0.52  | 48000  | 0.0132          | 0.9215 |
| 0.0184        | 0.53  | 49000  | 0.0136          | 0.9216 |
| 0.0191        | 0.54  | 50000  | 0.0129          | 0.9216 |
| 0.017         | 0.55  | 51000  | 0.0127          | 0.9216 |
| 0.0188        | 0.56  | 52000  | 0.0127          | 0.9215 |
| 0.0157        | 0.57  | 53000  | 0.0128          | 0.9216 |
| 0.0158        | 0.58  | 54000  | 0.0127          | 0.9216 |
| 0.0145        | 0.59  | 55000  | 0.0117          | 0.9216 |
| 0.0148        | 0.61  | 56000  | 0.0123          | 0.9216 |
| 0.0153        | 0.62  | 57000  | 0.0115          | 0.9216 |
| 0.0182        | 0.63  | 58000  | 0.0115          | 0.9216 |
| 0.0178        | 0.64  | 59000  | 0.0112          | 0.9215 |
| 0.0187        | 0.65  | 60000  | 0.0113          | 0.9215 |
| 0.0174        | 0.66  | 61000  | 0.0119          | 0.9216 |
| 0.0135        | 0.67  | 62000  | 0.0115          | 0.9215 |
| 0.0167        | 0.68  | 63000  | 0.0112          | 0.9216 |
| 0.0163        | 0.69  | 64000  | 0.0111          | 0.9215 |
| 0.0128        | 0.7   | 65000  | 0.0110          | 0.9215 |
| 0.0178        | 0.71  | 66000  | 0.0113          | 0.9215 |
| 0.0142        | 0.72  | 67000  | 0.0110          | 0.9215 |
| 0.0143        | 0.74  | 68000  | 0.0110          | 0.9215 |
| 0.0168        | 0.75  | 69000  | 0.0106          | 0.9216 |
| 0.0136        | 0.76  | 70000  | 0.0107          | 0.9215 |
| 0.0141        | 0.77  | 71000  | 0.0104          | 0.9215 |
| 0.0217        | 0.78  | 72000  | 0.0115          | 0.9216 |
| 0.012         | 0.79  | 73000  | 0.0105          | 0.9215 |
| 0.0141        | 0.8   | 74000  | 0.0100          | 0.9215 |
| 0.0136        | 0.81  | 75000  | 0.0096          | 0.9215 |
| 0.0106        | 0.82  | 76000  | 0.0104          | 0.9216 |
| 0.0176        | 0.83  | 77000  | 0.0102          | 0.9216 |
| 0.0169        | 0.84  | 78000  | 0.0099          | 0.9215 |
| 0.0118        | 0.85  | 79000  | 0.0102          | 0.9215 |
| 0.0178        | 0.86  | 80000  | 0.0095          | 0.9215 |
| 0.0145        | 0.88  | 81000  | 0.0097          | 0.9216 |
| 0.0154        | 0.89  | 82000  | 0.0099          | 0.9215 |
| 0.0129        | 0.9   | 83000  | 0.0094          | 0.9215 |
| 0.0125        | 0.91  | 84000  | 0.0097          | 0.9215 |
| 0.0147        | 0.92  | 85000  | 0.0093          | 0.9215 |
| 0.0145        | 0.93  | 86000  | 0.0091          | 0.9215 |
| 0.0121        | 0.94  | 87000  | 0.0089          | 0.9215 |
| 0.0125        | 0.95  | 88000  | 0.0094          | 0.9215 |
| 0.0113        | 0.96  | 89000  | 0.0088          | 0.9216 |
| 0.0098        | 0.97  | 90000  | 0.0094          | 0.9216 |
| 0.0137        | 0.98  | 91000  | 0.0089          | 0.9215 |
| 0.0105        | 0.99  | 92000  | 0.0091          | 0.9215 |
| 0.01          | 1.01  | 93000  | 0.0090          | 0.9216 |
| 0.0103        | 1.02  | 94000  | 0.0087          | 0.9216 |
| 0.0103        | 1.03  | 95000  | 0.0091          | 0.9215 |
| 0.0107        | 1.04  | 96000  | 0.0088          | 0.9216 |
| 0.0109        | 1.05  | 97000  | 0.0087          | 0.9215 |
| 0.0102        | 1.06  | 98000  | 0.0090          | 0.9216 |
| 0.0109        | 1.07  | 99000  | 0.0087          | 0.9215 |
| 0.0094        | 1.08  | 100000 | 0.0084          | 0.9215 |
| 0.009         | 1.09  | 101000 | 0.0085          | 0.9215 |
| 0.0085        | 1.1   | 102000 | 0.0084          | 0.9216 |
| 0.0123        | 1.11  | 103000 | 0.0085          | 0.9215 |
| 0.0094        | 1.12  | 104000 | 0.0084          | 0.9215 |
| 0.0076        | 1.14  | 105000 | 0.0081          | 0.9215 |
| 0.0119        | 1.15  | 106000 | 0.0079          | 0.9216 |
| 0.0079        | 1.16  | 107000 | 0.0081          | 0.9216 |
| 0.0108        | 1.17  | 108000 | 0.0080          | 0.9216 |
| 0.01          | 1.18  | 109000 | 0.0077          | 0.9216 |
| 0.0112        | 1.19  | 110000 | 0.0077          | 0.9216 |
| 0.0092        | 1.2   | 111000 | 0.0076          | 0.9215 |
| 0.0097        | 1.21  | 112000 | 0.0077          | 0.9215 |
| 0.0093        | 1.22  | 113000 | 0.0078          | 0.9215 |
| 0.0106        | 1.23  | 114000 | 0.0077          | 0.9215 |
| 0.0107        | 1.24  | 115000 | 0.0076          | 0.9215 |
| 0.0111        | 1.25  | 116000 | 0.0077          | 0.9215 |
| 0.0118        | 1.26  | 117000 | 0.0076          | 0.9215 |
| 0.0088        | 1.28  | 118000 | 0.0076          | 0.9215 |
| 0.01          | 1.29  | 119000 | 0.0076          | 0.9215 |
| 0.0102        | 1.3   | 120000 | 0.0076          | 0.9215 |
| 0.0106        | 1.31  | 121000 | 0.0076          | 0.9215 |
| 0.0099        | 1.32  | 122000 | 0.0077          | 0.9215 |
| 0.0099        | 1.33  | 123000 | 0.0077          | 0.9216 |
| 0.0105        | 1.34  | 124000 | 0.0075          | 0.9216 |
| 0.0082        | 1.35  | 125000 | 0.0074          | 0.9216 |
| 0.0088        | 1.36  | 126000 | 0.0072          | 0.9215 |
| 0.0077        | 1.37  | 127000 | 0.0070          | 0.9215 |
| 0.0063        | 1.38  | 128000 | 0.0074          | 0.9216 |
| 0.0084        | 1.39  | 129000 | 0.0069          | 0.9215 |
| 0.0085        | 1.41  | 130000 | 0.0071          | 0.9215 |
| 0.0107        | 1.42  | 131000 | 0.0067          | 0.9215 |
| 0.0064        | 1.43  | 132000 | 0.0068          | 0.9215 |
| 0.0064        | 1.44  | 133000 | 0.0069          | 0.9215 |
| 0.0139        | 1.45  | 134000 | 0.0067          | 0.9216 |
| 0.0093        | 1.46  | 135000 | 0.0068          | 0.9216 |
| 0.009         | 1.47  | 136000 | 0.0067          | 0.9215 |
| 0.0083        | 1.48  | 137000 | 0.0065          | 0.9216 |
| 0.0108        | 1.49  | 138000 | 0.0064          | 0.9215 |
| 0.0074        | 1.5   | 139000 | 0.0066          | 0.9215 |
| 0.009         | 1.51  | 140000 | 0.0064          | 0.9216 |
| 0.0062        | 1.52  | 141000 | 0.0064          | 0.9215 |
| 0.007         | 1.54  | 142000 | 0.0063          | 0.9215 |
| 0.0082        | 1.55  | 143000 | 0.0062          | 0.9215 |
| 0.0077        | 1.56  | 144000 | 0.0064          | 0.9215 |
| 0.0094        | 1.57  | 145000 | 0.0062          | 0.9215 |
| 0.0085        | 1.58  | 146000 | 0.0063          | 0.9215 |
| 0.0091        | 1.59  | 147000 | 0.0062          | 0.9215 |
| 0.0087        | 1.6   | 148000 | 0.0061          | 0.9215 |
| 0.0066        | 1.61  | 149000 | 0.0062          | 0.9215 |
| 0.0087        | 1.62  | 150000 | 0.0061          | 0.9215 |
| 0.0059        | 1.63  | 151000 | 0.0059          | 0.9215 |
| 0.0086        | 1.64  | 152000 | 0.0059          | 0.9215 |
| 0.0066        | 1.65  | 153000 | 0.0059          | 0.9215 |
| 0.0076        | 1.66  | 154000 | 0.0058          | 0.9215 |
| 0.0073        | 1.68  | 155000 | 0.0060          | 0.9215 |
| 0.0118        | 1.69  | 156000 | 0.0060          | 0.9215 |
| 0.0058        | 1.7   | 157000 | 0.0059          | 0.9215 |
| 0.0093        | 1.71  | 158000 | 0.0058          | 0.9215 |
| 0.0079        | 1.72  | 159000 | 0.0058          | 0.9215 |
| 0.0063        | 1.73  | 160000 | 0.0059          | 0.9215 |
| 0.0065        | 1.74  | 161000 | 0.0056          | 0.9215 |
| 0.0105        | 1.75  | 162000 | 0.0057          | 0.9215 |
| 0.0075        | 1.76  | 163000 | 0.0055          | 0.9215 |
| 0.0069        | 1.77  | 164000 | 0.0056          | 0.9215 |
| 0.0075        | 1.78  | 165000 | 0.0056          | 0.9215 |
| 0.0067        | 1.79  | 166000 | 0.0055          | 0.9215 |
| 0.0069        | 1.81  | 167000 | 0.0056          | 0.9215 |
| 0.0063        | 1.82  | 168000 | 0.0056          | 0.9215 |
| 0.0058        | 1.83  | 169000 | 0.0055          | 0.9215 |
| 0.0058        | 1.84  | 170000 | 0.0054          | 0.9215 |
| 0.0081        | 1.85  | 171000 | 0.0055          | 0.9215 |
| 0.0071        | 1.86  | 172000 | 0.0054          | 0.9215 |
| 0.0077        | 1.87  | 173000 | 0.0054          | 0.9215 |
| 0.0053        | 1.88  | 174000 | 0.0053          | 0.9215 |
| 0.0067        | 1.89  | 175000 | 0.0053          | 0.9215 |
| 0.0066        | 1.9   | 176000 | 0.0053          | 0.9215 |
| 0.0084        | 1.91  | 177000 | 0.0053          | 0.9215 |
| 0.0066        | 1.92  | 178000 | 0.0052          | 0.9215 |
| 0.0057        | 1.94  | 179000 | 0.0053          | 0.9215 |
| 0.0059        | 1.95  | 180000 | 0.0052          | 0.9215 |
| 0.0053        | 1.96  | 181000 | 0.0053          | 0.9215 |
| 0.0056        | 1.97  | 182000 | 0.0052          | 0.9215 |
| 0.0054        | 1.98  | 183000 | 0.0052          | 0.9215 |
| 0.0053        | 1.99  | 184000 | 0.0052          | 0.9215 |
| 0.0066        | 2.0   | 185000 | 0.0052          | 0.9215 |


### Framework versions

- Transformers 4.27.3
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.13.2