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bart-base-spelling-nl

Text2Text Generation
Transformers
PyTorch
Safetensors
Dutch
bart
Generated from Trainer
Inference Endpoints
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: bart-base-spelling-nl-2m
    results: []

bart-base-spelling-nl-2m

This model is a fine-tuned version of facebook/bart-base on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0248
  • Cer: 0.0133

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
0.277 0.01 1000 0.2337 0.9206
0.2349 0.01 2000 0.1757 0.9204
0.1929 0.02 3000 0.1482 0.9205
0.1686 0.03 4000 0.1314 0.9202
0.1435 0.03 5000 0.1175 0.9203
0.1505 0.04 6000 0.1086 0.9204
0.1438 0.05 7000 0.0984 0.9203
0.1362 0.05 8000 0.0941 0.9203
0.1207 0.06 9000 0.0890 0.9201
0.108 0.06 10000 0.0850 0.9203
0.1142 0.07 11000 0.0798 0.9201
0.1081 0.08 12000 0.0757 0.9203
0.0987 0.08 13000 0.0739 0.9201
0.1103 0.09 14000 0.0728 0.9202
0.0961 0.1 15000 0.0678 0.9202
0.0976 0.1 16000 0.0672 0.9202
0.0949 0.11 17000 0.0640 0.9202
0.1026 0.12 18000 0.0635 0.9203
0.1049 0.12 19000 0.0618 0.9201
0.0893 0.13 20000 0.0617 0.9201
0.0834 0.14 21000 0.0582 0.9202
0.0815 0.14 22000 0.0584 0.9202
0.0801 0.15 23000 0.0606 0.9202
0.0764 0.15 24000 0.0591 0.9201
0.0779 0.16 25000 0.0556 0.9201
0.0839 0.17 26000 0.0548 0.9202
0.0811 0.17 27000 0.0532 0.9202
0.0817 0.18 28000 0.0537 0.9202
0.0809 0.19 29000 0.0527 0.9201
0.0812 0.19 30000 0.0512 0.9202
0.0741 0.2 31000 0.0507 0.9201
0.0764 0.21 32000 0.0510 0.9201
0.0741 0.21 33000 0.0494 0.9201
0.0736 0.22 34000 0.0499 0.9201
0.0674 0.23 35000 0.0486 0.9202
0.0775 0.23 36000 0.0489 0.9201
0.0772 0.24 37000 0.0484 0.9202
0.073 0.25 38000 0.0487 0.9202
0.0675 0.25 39000 0.0474 0.9200
0.0739 0.26 40000 0.0460 0.9201
0.0694 0.26 41000 0.0478 0.9200
0.0659 0.27 42000 0.0451 0.9201
0.0638 0.28 43000 0.0449 0.9200
0.0704 0.28 44000 0.0447 0.9201
0.0657 0.29 45000 0.0463 0.9201
0.0649 0.3 46000 0.0445 0.9200
0.069 0.3 47000 0.0444 0.9201
0.0655 0.31 48000 0.0433 0.9200
0.0592 0.32 49000 0.0439 0.9201
0.0623 0.32 50000 0.0433 0.9201
0.074 0.33 51000 0.0419 0.9202
0.0602 0.34 52000 0.0410 0.9202
0.0672 0.34 53000 0.0418 0.9202
0.063 0.35 54000 0.0425 0.9200
0.0609 0.35 55000 0.0407 0.9200
0.0583 0.36 56000 0.0399 0.9200
0.0602 0.37 57000 0.0400 0.9201
0.0707 0.37 58000 0.0399 0.9200
0.0628 0.38 59000 0.0401 0.9201
0.0586 0.39 60000 0.0390 0.9201
0.061 0.39 61000 0.0403 0.9199
0.0611 0.4 62000 0.0388 0.9201
0.0569 0.41 63000 0.0379 0.9200
0.0577 0.41 64000 0.0382 0.9200
0.061 0.42 65000 0.0390 0.9202
0.0605 0.43 66000 0.0381 0.9199
0.0566 0.43 67000 0.0382 0.9200
0.0616 0.44 68000 0.0380 0.9200
0.0666 0.45 69000 0.0381 0.9201
0.052 0.45 70000 0.0373 0.9200
0.0576 0.46 71000 0.0376 0.9200
0.0529 0.46 72000 0.0365 0.9200
0.0504 0.47 73000 0.0371 0.9201
0.0499 0.48 74000 0.0373 0.9200
0.0578 0.48 75000 0.0367 0.9200
0.0545 0.49 76000 0.0356 0.9200
0.0527 0.5 77000 0.0358 0.9200
0.0464 0.5 78000 0.0354 0.9201
0.0546 0.51 79000 0.0354 0.9200
0.0536 0.52 80000 0.0346 0.9200
0.0568 0.52 81000 0.0355 0.9199
0.0486 0.53 82000 0.0346 0.9199
0.0571 0.54 83000 0.0338 0.9200
0.0526 0.54 84000 0.0339 0.9200
0.0485 0.55 85000 0.0338 0.9200
0.0489 0.56 86000 0.0345 0.9199
0.0473 0.56 87000 0.0338 0.9201
0.0449 0.57 88000 0.0334 0.9199
0.0516 0.57 89000 0.0331 0.9199
0.0537 0.58 90000 0.0331 0.9199
0.0477 0.59 91000 0.0326 0.9200
0.046 0.59 92000 0.0325 0.9201
0.0545 0.6 93000 0.0326 0.9200
0.0473 0.61 94000 0.0327 0.9201
0.0558 0.61 95000 0.0324 0.9199
0.0428 0.62 96000 0.0317 0.9200
0.0596 0.63 97000 0.0314 0.9200
0.0449 0.63 98000 0.0322 0.9200
0.041 0.64 99000 0.0314 0.9199
0.0464 0.65 100000 0.0319 0.9200
0.0519 0.65 101000 0.0301 0.9199
0.0417 0.66 102000 0.0305 0.9199
0.0456 0.66 103000 0.0308 0.9199
0.046 0.67 104000 0.0315 0.9198
0.0462 0.68 105000 0.0306 0.9199
0.0478 0.68 106000 0.0306 0.9199
0.0456 0.69 107000 0.0308 0.9199
0.0433 0.7 108000 0.0302 0.9200
0.0498 0.7 109000 0.0296 0.9200
0.0438 0.71 110000 0.0300 0.9200
0.0394 0.72 111000 0.0299 0.9198
0.0451 0.72 112000 0.0297 0.9200
0.0413 0.73 113000 0.0295 0.9199
0.0461 0.74 114000 0.0301 0.9198
0.0501 0.74 115000 0.0296 0.9199
0.0387 0.75 116000 0.0293 0.9200
0.0384 0.76 117000 0.0293 0.9199
0.0492 0.76 118000 0.0291 0.9199
0.0415 0.77 119000 0.0288 0.9200
0.0435 0.77 120000 0.0286 0.9199
0.0423 0.78 121000 0.0284 0.9198
0.0437 0.79 122000 0.0286 0.9199
0.0512 0.79 123000 0.0285 0.9200
0.0427 0.8 124000 0.0285 0.9199
0.0461 0.81 125000 0.0287 0.9199
0.0433 0.81 126000 0.0290 0.9198
0.0386 0.82 127000 0.0283 0.9199
0.0407 0.83 128000 0.0282 0.9199
0.0466 0.83 129000 0.0276 0.9199
0.048 0.84 130000 0.0278 0.9201
0.046 0.85 131000 0.0279 0.9199
0.0431 0.85 132000 0.0270 0.9199
0.047 0.86 133000 0.0272 0.9199
0.0466 0.86 134000 0.0266 0.9199
0.04 0.87 135000 0.0267 0.9199
0.038 0.88 136000 0.0271 0.9199
0.0382 0.88 137000 0.0271 0.9199
0.0422 0.89 138000 0.0265 0.9199
0.0464 0.9 139000 0.0265 0.9200
0.0372 0.9 140000 0.0270 0.9200
0.0381 0.91 141000 0.0266 0.9199
0.0359 0.92 142000 0.0267 0.9198
0.0368 0.92 143000 0.0270 0.9199
0.0365 0.93 144000 0.0266 0.9199
0.0413 0.94 145000 0.0268 0.9199
0.0383 0.94 146000 0.0261 0.9199
0.0396 0.95 147000 0.0259 0.9199
0.0405 0.96 148000 0.0260 0.9199
0.0433 0.96 149000 0.0258 0.9199
0.0378 0.97 150000 0.0260 0.9200
0.0337 0.97 151000 0.0258 0.9199
0.0456 0.98 152000 0.0254 0.9199
0.0355 0.99 153000 0.0256 0.9199
0.0396 0.99 154000 0.0253 0.9199
0.0353 1.0 155000 0.0256 0.9199
0.036 1.01 156000 0.0253 0.9200
0.0345 1.01 157000 0.0254 0.9199
0.0321 1.02 158000 0.0248 0.9198
0.0366 1.03 159000 0.0252 0.9200
0.0298 1.03 160000 0.0254 0.9198
0.0316 1.04 161000 0.0250 0.9199
0.0322 1.05 162000 0.0243 0.9199
0.0313 1.05 163000 0.0246 0.9198
0.0329 1.06 164000 0.0247 0.9200
0.0393 1.06 165000 0.0248 0.9198
0.0352 1.07 166000 0.0243 0.9198
0.0319 1.08 167000 0.0244 0.9199
0.0315 1.08 168000 0.0250 0.9198
0.0345 1.09 169000 0.0243 0.9199
0.0341 1.1 170000 0.0247 0.9199
0.0317 1.1 171000 0.0241 0.9199
0.0313 1.11 172000 0.0245 0.9199
0.033 1.12 173000 0.0237 0.9199
0.0339 1.12 174000 0.0237 0.9199
0.0319 1.13 175000 0.0240 0.9199
0.0391 1.14 176000 0.0241 0.9199
0.0325 1.14 177000 0.0239 0.9200
0.0295 1.15 178000 0.0240 0.9199
0.0288 1.16 179000 0.0232 0.9199
0.0347 1.16 180000 0.0234 0.9199
0.029 1.17 181000 0.0234 0.9198
0.0305 1.17 182000 0.0231 0.9199
0.0454 1.18 183000 0.0231 0.9200
0.0339 1.19 184000 0.0234 0.9199
0.0375 1.19 185000 0.0229 0.9199
0.0351 1.2 186000 0.0227 0.9199
0.0305 1.21 187000 0.0230 0.9199
0.0376 1.21 188000 0.0228 0.9199
0.0338 1.22 189000 0.0225 0.9200
0.0315 1.23 190000 0.0229 0.9199
0.0369 1.23 191000 0.0229 0.9199
0.0288 1.24 192000 0.0227 0.9199
0.0344 1.25 193000 0.0225 0.9199
0.0283 1.25 194000 0.0221 0.9199
0.0377 1.26 195000 0.0225 0.9198
0.0395 1.27 196000 0.0225 0.9199
0.0268 1.27 197000 0.0224 0.9199
0.032 1.28 198000 0.0222 0.9199
0.0328 1.28 199000 0.0221 0.9199
0.0278 1.29 200000 0.0220 0.9198
0.029 1.3 201000 0.0221 0.9199
0.0319 1.3 202000 0.0218 0.9199
0.0422 1.31 203000 0.0220 0.9199
0.0301 1.32 204000 0.0215 0.9198
0.0293 1.32 205000 0.0217 0.9198
0.0347 1.33 206000 0.0216 0.9199
0.0288 1.34 207000 0.0215 0.9199
0.0264 1.34 208000 0.0216 0.9199
0.0341 1.35 209000 0.0214 0.9199
0.029 1.36 210000 0.0213 0.9199
0.0281 1.36 211000 0.0218 0.9198
0.033 1.37 212000 0.0212 0.9199
0.0348 1.37 213000 0.0211 0.9199
0.0291 1.38 214000 0.0214 0.9199
0.0353 1.39 215000 0.0212 0.9199
0.0324 1.39 216000 0.0209 0.9199
0.0342 1.4 217000 0.0209 0.9199
0.0293 1.41 218000 0.0212 0.9199
0.0281 1.41 219000 0.0209 0.9199
0.0286 1.42 220000 0.0209 0.9198
0.0297 1.43 221000 0.0205 0.9200
0.0256 1.43 222000 0.0207 0.9199
0.0261 1.44 223000 0.0209 0.9198
0.0274 1.45 224000 0.0204 0.9199
0.0343 1.45 225000 0.0201 0.9199
0.0249 1.46 226000 0.0204 0.9199
0.0267 1.47 227000 0.0202 0.9199
0.0264 1.47 228000 0.0202 0.9199
0.031 1.48 229000 0.0201 0.9199
0.0273 1.48 230000 0.0199 0.9199
0.024 1.49 231000 0.0199 0.9199
0.0295 1.5 232000 0.0198 0.9199
0.0281 1.5 233000 0.0196 0.9199
0.0243 1.51 234000 0.0195 0.9198
0.0258 1.52 235000 0.0197 0.9199
0.0272 1.52 236000 0.0196 0.9198
0.0261 1.53 237000 0.0198 0.9199
0.0222 1.54 238000 0.0198 0.9199
0.0259 1.54 239000 0.0195 0.9199
0.0317 1.55 240000 0.0194 0.9199
0.0266 1.56 241000 0.0191 0.9199
0.0272 1.56 242000 0.0193 0.9199
0.0236 1.57 243000 0.0194 0.9199
0.0266 1.57 244000 0.0193 0.9198
0.027 1.58 245000 0.0195 0.9199
0.0257 1.59 246000 0.0192 0.9199
0.0276 1.59 247000 0.0190 0.9199
0.0238 1.6 248000 0.0188 0.9199
0.0301 1.61 249000 0.0188 0.9199
0.0273 1.61 250000 0.0189 0.9199
0.0246 1.62 251000 0.0187 0.9198
0.0309 1.63 252000 0.0187 0.9198
0.0237 1.63 253000 0.0188 0.9199
0.0234 1.64 254000 0.0184 0.9198
0.0246 1.65 255000 0.0186 0.9198
0.0213 1.65 256000 0.0182 0.9199
0.0251 1.66 257000 0.0182 0.9198
0.0236 1.67 258000 0.0184 0.9198
0.0276 1.67 259000 0.0185 0.9198
0.0233 1.68 260000 0.0182 0.9199
0.0205 1.68 261000 0.0183 0.9198
0.0253 1.69 262000 0.0181 0.9198
0.0221 1.7 263000 0.0180 0.9198
0.0228 1.7 264000 0.0182 0.9199
0.0209 1.71 265000 0.0181 0.9198
0.0319 1.72 266000 0.0179 0.9199
0.0236 1.72 267000 0.0178 0.9199
0.029 1.73 268000 0.0179 0.9198
0.0233 1.74 269000 0.0178 0.9198
0.0248 1.74 270000 0.0176 0.9198
0.0211 1.75 271000 0.0177 0.9198
0.0257 1.76 272000 0.0177 0.9198
0.0247 1.76 273000 0.0175 0.9199
0.0323 1.77 274000 0.0176 0.9199
0.0236 1.77 275000 0.0175 0.9198
0.0202 1.78 276000 0.0176 0.9198
0.0318 1.79 277000 0.0174 0.9199
0.0206 1.79 278000 0.0175 0.9198
0.0245 1.8 279000 0.0174 0.9199
0.0177 1.81 280000 0.0174 0.9199
0.0268 1.81 281000 0.0174 0.9199
0.0209 1.82 282000 0.0172 0.9199
0.0248 1.83 283000 0.0171 0.9198
0.0205 1.83 284000 0.0173 0.9198
0.0231 1.84 285000 0.0172 0.9199
0.0278 1.85 286000 0.0171 0.9198
0.0244 1.85 287000 0.0171 0.9198
0.0223 1.86 288000 0.0169 0.9198
0.0285 1.87 289000 0.0168 0.9198
0.0223 1.87 290000 0.0169 0.9198
0.0231 1.88 291000 0.0169 0.9198
0.0192 1.88 292000 0.0169 0.9198
0.0234 1.89 293000 0.0168 0.9198
0.0223 1.9 294000 0.0168 0.9198
0.0255 1.9 295000 0.0168 0.9198
0.0248 1.91 296000 0.0166 0.9198
0.0216 1.92 297000 0.0166 0.9198
0.0219 1.92 298000 0.0167 0.9198
0.0196 1.93 299000 0.0167 0.9198
0.0175 1.94 300000 0.0166 0.9198
0.0228 1.94 301000 0.0165 0.9198
0.019 1.95 302000 0.0165 0.9198
0.0191 1.96 303000 0.0165 0.9198
0.0249 1.96 304000 0.0165 0.9198
0.0233 1.97 305000 0.0164 0.9198
0.0211 1.97 306000 0.0164 0.9198
0.02 1.98 307000 0.0164 0.9198
0.0191 1.99 308000 0.0164 0.9198
0.0214 1.99 309000 0.0164 0.9198

Framework versions

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