--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-base-spelling-nl-1m results: [] --- This model is a Dutch fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base). It achieves the following results on the evaluation set: - Loss: 0.0221 - Cer: 0.0145 ## Model description This is a text-to-text fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) trained on spelling correction. It leans on the excellent work by Oliver Guhr ([github](https://github.com/oliverguhr/spelling), [huggingface](https://huggingface.co/oliverguhr/spelling-correction-english-base)). Training was performed on an AWS EC2 instance (g5.xlarge) on a single GPU. ## Intended uses & limitations The intended use for this model is to be a component of the [Valkuil.net](https://valkuil.net) context-sensitive spelling checker. A next version of the model will be trained on more data. ## Training and evaluation data The model was trained on a Dutch dataset composed of 1,500,000 lines of text from three public Dutch sources, downloaded from the [Opus corpus](https://opus.nlpl.eu/): - nl-europarlv7.100k.txt (1,000,000 lines) - nl-opensubtitles2016.100k.txt (1,000,000 lines) - nl-wikipedia.100k.txt (964,203 lines) ## 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 | |:-------------:|:-----:|:------:|:---------------:|:------:| | 0.2824 | 0.01 | 1000 | 0.2129 | 0.9219 | | 0.1971 | 0.02 | 2000 | 0.1600 | 0.9217 | | 0.171 | 0.03 | 3000 | 0.1273 | 0.9217 | | 0.1586 | 0.04 | 4000 | 0.1110 | 0.9216 | | 0.1288 | 0.05 | 5000 | 0.0991 | 0.9214 | | 0.1338 | 0.06 | 6000 | 0.0910 | 0.9215 | | 0.1279 | 0.08 | 7000 | 0.0831 | 0.9215 | | 0.1147 | 0.09 | 8000 | 0.0789 | 0.9215 | | 0.1091 | 0.1 | 9000 | 0.0769 | 0.9216 | | 0.0935 | 0.11 | 10000 | 0.0700 | 0.9214 | | 0.0963 | 0.12 | 11000 | 0.0678 | 0.9215 | | 0.0969 | 0.13 | 12000 | 0.0654 | 0.9214 | | 0.0957 | 0.14 | 13000 | 0.0627 | 0.9215 | | 0.0886 | 0.15 | 14000 | 0.0644 | 0.9215 | | 0.0911 | 0.16 | 15000 | 0.0604 | 0.9215 | | 0.0955 | 0.17 | 16000 | 0.0595 | 0.9215 | | 0.0875 | 0.18 | 17000 | 0.0587 | 0.9213 | | 0.0879 | 0.19 | 18000 | 0.0576 | 0.9214 | | 0.079 | 0.21 | 19000 | 0.0550 | 0.9213 | | 0.0808 | 0.22 | 20000 | 0.0536 | 0.9215 | | 0.0684 | 0.23 | 21000 | 0.0536 | 0.9214 | | 0.0789 | 0.24 | 22000 | 0.0530 | 0.9214 | | 0.088 | 0.25 | 23000 | 0.0524 | 0.9215 | | 0.076 | 0.26 | 24000 | 0.0519 | 0.9214 | | 0.0714 | 0.27 | 25000 | 0.0506 | 0.9213 | | 0.0664 | 0.28 | 26000 | 0.0495 | 0.9213 | | 0.0791 | 0.29 | 27000 | 0.0492 | 0.9215 | | 0.0702 | 0.3 | 28000 | 0.0485 | 0.9215 | | 0.0709 | 0.31 | 29000 | 0.0493 | 0.9213 | | 0.0676 | 0.32 | 30000 | 0.0480 | 0.9214 | | 0.0692 | 0.34 | 31000 | 0.0468 | 0.9215 | | 0.0633 | 0.35 | 32000 | 0.0473 | 0.9213 | | 0.0732 | 0.36 | 33000 | 0.0455 | 0.9213 | | 0.0809 | 0.37 | 34000 | 0.0455 | 0.9214 | | 0.0562 | 0.38 | 35000 | 0.0451 | 0.9214 | | 0.0715 | 0.39 | 36000 | 0.0440 | 0.9214 | | 0.0596 | 0.4 | 37000 | 0.0441 | 0.9214 | | 0.0534 | 0.41 | 38000 | 0.0430 | 0.9213 | | 0.0657 | 0.42 | 39000 | 0.0427 | 0.9214 | | 0.0643 | 0.43 | 40000 | 0.0441 | 0.9212 | | 0.0579 | 0.44 | 41000 | 0.0414 | 0.9213 | | 0.0695 | 0.45 | 42000 | 0.0430 | 0.9212 | | 0.0566 | 0.47 | 43000 | 0.0413 | 0.9212 | | 0.0646 | 0.48 | 44000 | 0.0415 | 0.9213 | | 0.0573 | 0.49 | 45000 | 0.0410 | 0.9212 | | 0.0568 | 0.5 | 46000 | 0.0406 | 0.9213 | | 0.065 | 0.51 | 47000 | 0.0405 | 0.9213 | | 0.063 | 0.52 | 48000 | 0.0396 | 0.9213 | | 0.0654 | 0.53 | 49000 | 0.0397 | 0.9213 | | 0.0506 | 0.54 | 50000 | 0.0391 | 0.9212 | | 0.0573 | 0.55 | 51000 | 0.0382 | 0.9213 | | 0.0569 | 0.56 | 52000 | 0.0381 | 0.9214 | | 0.0597 | 0.57 | 53000 | 0.0381 | 0.9212 | | 0.0543 | 0.58 | 54000 | 0.0374 | 0.9213 | | 0.057 | 0.59 | 55000 | 0.0381 | 0.9213 | | 0.058 | 0.61 | 56000 | 0.0380 | 0.9212 | | 0.0481 | 0.62 | 57000 | 0.0366 | 0.9213 | | 0.0581 | 0.63 | 58000 | 0.0367 | 0.9212 | | 0.0521 | 0.64 | 59000 | 0.0363 | 0.9213 | | 0.0543 | 0.65 | 60000 | 0.0358 | 0.9212 | | 0.0594 | 0.66 | 61000 | 0.0359 | 0.9214 | | 0.0479 | 0.67 | 62000 | 0.0354 | 0.9212 | | 0.0512 | 0.68 | 63000 | 0.0357 | 0.9211 | | 0.0488 | 0.69 | 64000 | 0.0341 | 0.9213 | | 0.0485 | 0.7 | 65000 | 0.0346 | 0.9213 | | 0.052 | 0.71 | 66000 | 0.0343 | 0.9213 | | 0.0427 | 0.72 | 67000 | 0.0341 | 0.9212 | | 0.0502 | 0.74 | 68000 | 0.0343 | 0.9211 | | 0.0434 | 0.75 | 69000 | 0.0337 | 0.9213 | | 0.0579 | 0.76 | 70000 | 0.0337 | 0.9213 | | 0.0534 | 0.77 | 71000 | 0.0330 | 0.9212 | | 0.0437 | 0.78 | 72000 | 0.0334 | 0.9212 | | 0.05 | 0.79 | 73000 | 0.0332 | 0.9213 | | 0.043 | 0.8 | 74000 | 0.0329 | 0.9212 | | 0.0554 | 0.81 | 75000 | 0.0323 | 0.9212 | | 0.0418 | 0.82 | 76000 | 0.0326 | 0.9212 | | 0.0461 | 0.83 | 77000 | 0.0326 | 0.9212 | | 0.0435 | 0.84 | 78000 | 0.0319 | 0.9212 | | 0.0453 | 0.85 | 79000 | 0.0317 | 0.9212 | | 0.0434 | 0.87 | 80000 | 0.0318 | 0.9212 | | 0.0466 | 0.88 | 81000 | 0.0321 | 0.9212 | | 0.0461 | 0.89 | 82000 | 0.0316 | 0.9212 | | 0.0381 | 0.9 | 83000 | 0.0311 | 0.9213 | | 0.0455 | 0.91 | 84000 | 0.0306 | 0.9212 | | 0.0446 | 0.92 | 85000 | 0.0315 | 0.9212 | | 0.0532 | 0.93 | 86000 | 0.0305 | 0.9212 | | 0.052 | 0.94 | 87000 | 0.0305 | 0.9212 | | 0.0353 | 0.95 | 88000 | 0.0305 | 0.9211 | | 0.0469 | 0.96 | 89000 | 0.0304 | 0.9212 | | 0.0387 | 0.97 | 90000 | 0.0303 | 0.9212 | | 0.0478 | 0.98 | 91000 | 0.0302 | 0.9212 | | 0.0395 | 1.0 | 92000 | 0.0299 | 0.9212 | | 0.0387 | 1.01 | 93000 | 0.0290 | 0.9212 | | 0.0356 | 1.02 | 94000 | 0.0287 | 0.9212 | | 0.0381 | 1.03 | 95000 | 0.0295 | 0.9212 | | 0.0386 | 1.04 | 96000 | 0.0284 | 0.9213 | | 0.038 | 1.05 | 97000 | 0.0293 | 0.9212 | | 0.0346 | 1.06 | 98000 | 0.0284 | 0.9212 | | 0.0357 | 1.07 | 99000 | 0.0285 | 0.9212 | | 0.0446 | 1.08 | 100000 | 0.0287 | 0.9211 | | 0.0424 | 1.09 | 101000 | 0.0284 | 0.9213 | | 0.0357 | 1.1 | 102000 | 0.0282 | 0.9211 | | 0.0413 | 1.11 | 103000 | 0.0282 | 0.9211 | | 0.0348 | 1.12 | 104000 | 0.0279 | 0.9212 | | 0.0363 | 1.14 | 105000 | 0.0279 | 0.9212 | | 0.0329 | 1.15 | 106000 | 0.0282 | 0.9211 | | 0.0438 | 1.16 | 107000 | 0.0279 | 0.9212 | | 0.037 | 1.17 | 108000 | 0.0274 | 0.9212 | | 0.0311 | 1.18 | 109000 | 0.0278 | 0.9212 | | 0.0297 | 1.19 | 110000 | 0.0275 | 0.9212 | | 0.0323 | 1.2 | 111000 | 0.0271 | 0.9212 | | 0.0387 | 1.21 | 112000 | 0.0275 | 0.9212 | | 0.0366 | 1.22 | 113000 | 0.0269 | 0.9211 | | 0.0345 | 1.23 | 114000 | 0.0269 | 0.9211 | | 0.0389 | 1.24 | 115000 | 0.0261 | 0.9211 | | 0.0381 | 1.25 | 116000 | 0.0265 | 0.9211 | | 0.0324 | 1.27 | 117000 | 0.0265 | 0.9211 | | 0.0345 | 1.28 | 118000 | 0.0260 | 0.9212 | | 0.032 | 1.29 | 119000 | 0.0260 | 0.9211 | | 0.0359 | 1.3 | 120000 | 0.0259 | 0.9211 | | 0.0347 | 1.31 | 121000 | 0.0259 | 0.9212 | | 0.0334 | 1.32 | 122000 | 0.0253 | 0.9211 | | 0.0297 | 1.33 | 123000 | 0.0260 | 0.9210 | | 0.0333 | 1.34 | 124000 | 0.0251 | 0.9212 | | 0.0303 | 1.35 | 125000 | 0.0254 | 0.9211 | | 0.0292 | 1.36 | 126000 | 0.0250 | 0.9211 | | 0.0318 | 1.37 | 127000 | 0.0250 | 0.9212 | | 0.0284 | 1.38 | 128000 | 0.0250 | 0.9211 | | 0.0311 | 1.4 | 129000 | 0.0248 | 0.9211 | | 0.0323 | 1.41 | 130000 | 0.0248 | 0.9211 | | 0.0253 | 1.42 | 131000 | 0.0244 | 0.9211 | | 0.0287 | 1.43 | 132000 | 0.0246 | 0.9211 | | 0.0351 | 1.44 | 133000 | 0.0240 | 0.9212 | | 0.0363 | 1.45 | 134000 | 0.0238 | 0.9211 | | 0.0264 | 1.46 | 135000 | 0.0240 | 0.9211 | | 0.0304 | 1.47 | 136000 | 0.0242 | 0.9211 | | 0.0325 | 1.48 | 137000 | 0.0236 | 0.9212 | | 0.033 | 1.49 | 138000 | 0.0239 | 0.9211 | | 0.03 | 1.5 | 139000 | 0.0236 | 0.9211 | | 0.0256 | 1.51 | 140000 | 0.0235 | 0.9211 | | 0.0312 | 1.53 | 141000 | 0.0237 | 0.9211 | | 0.0302 | 1.54 | 142000 | 0.0237 | 0.9211 | | 0.0227 | 1.55 | 143000 | 0.0232 | 0.9212 | | 0.0261 | 1.56 | 144000 | 0.0232 | 0.9211 | | 0.0269 | 1.57 | 145000 | 0.0227 | 0.9211 | | 0.0312 | 1.58 | 146000 | 0.0228 | 0.9211 | | 0.0298 | 1.59 | 147000 | 0.0231 | 0.9211 | | 0.0281 | 1.6 | 148000 | 0.0226 | 0.9212 | | 0.029 | 1.61 | 149000 | 0.0227 | 0.9211 | | 0.0324 | 1.62 | 150000 | 0.0225 | 0.9211 | | 0.0251 | 1.63 | 151000 | 0.0223 | 0.9212 | | 0.0278 | 1.64 | 152000 | 0.0223 | 0.9211 | | 0.0284 | 1.65 | 153000 | 0.0224 | 0.9210 | | 0.0254 | 1.67 | 154000 | 0.0220 | 0.9211 | | 0.028 | 1.68 | 155000 | 0.0221 | 0.9210 | | 0.0247 | 1.69 | 156000 | 0.0222 | 0.9211 | | 0.0295 | 1.7 | 157000 | 0.0218 | 0.9211 | | 0.0283 | 1.71 | 158000 | 0.0216 | 0.9211 | | 0.0245 | 1.72 | 159000 | 0.0218 | 0.9211 | | 0.0249 | 1.73 | 160000 | 0.0216 | 0.9211 | | 0.0264 | 1.74 | 161000 | 0.0215 | 0.9211 | | 0.0264 | 1.75 | 162000 | 0.0213 | 0.9211 | | 0.0306 | 1.76 | 163000 | 0.0212 | 0.9211 | | 0.0242 | 1.77 | 164000 | 0.0212 | 0.9212 | | 0.0247 | 1.78 | 165000 | 0.0211 | 0.9211 | | 0.0227 | 1.8 | 166000 | 0.0211 | 0.9210 | | 0.0252 | 1.81 | 167000 | 0.0211 | 0.9211 | | 0.0269 | 1.82 | 168000 | 0.0208 | 0.9211 | | 0.0256 | 1.83 | 169000 | 0.0209 | 0.9211 | | 0.0234 | 1.84 | 170000 | 0.0207 | 0.9211 | | 0.0258 | 1.85 | 171000 | 0.0207 | 0.9211 | | 0.0282 | 1.86 | 172000 | 0.0205 | 0.9210 | | 0.0282 | 1.87 | 173000 | 0.0206 | 0.9210 | | 0.0234 | 1.88 | 174000 | 0.0205 | 0.9211 | | 0.0222 | 1.89 | 175000 | 0.0204 | 0.9211 | | 0.0237 | 1.9 | 176000 | 0.0203 | 0.9211 | | 0.0299 | 1.91 | 177000 | 0.0203 | 0.9211 | | 0.0246 | 1.93 | 178000 | 0.0203 | 0.9211 | | 0.0227 | 1.94 | 179000 | 0.0204 | 0.9211 | | 0.0253 | 1.95 | 180000 | 0.0202 | 0.9211 | | 0.0197 | 1.96 | 181000 | 0.0202 | 0.9211 | | 0.0231 | 1.97 | 182000 | 0.0200 | 0.9211 | | 0.0244 | 1.98 | 183000 | 0.0201 | 0.9211 | | 0.0259 | 1.99 | 184000 | 0.0200 | 0.9211 | ### Framework versions - Transformers 4.27.3 - Pytorch 2.0.0+cu117 - Datasets 2.10.1 - Tokenizers 0.13.2