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nb-bert-base-user-needs

This model is a fine-tuned version of NbAiLab/nb-bert-base on a dataset of 2000 articles from Bergens Tidende, published between 06/01/2020 and 02/02/2020. These articles are labelled as one of six classes / user needs, as introduced by the BBC in 2017 It achieves the following results on the evaluation set:

  • Loss: 1.0600
  • Accuracy: 0.8479
  • F1: 0.8319
  • Precision: 0.8315
  • Recall: 0.8479

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: 3e-05
  • train_batch_size: 16
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 25
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy F1 Precision Recall
No log 1.0 98 1.1222 0.6263 0.5185 0.5076 0.6263
No log 2.0 196 1.0066 0.7216 0.6436 0.5899 0.7216
No log 3.0 294 0.8540 0.7577 0.7037 0.6760 0.7577
No log 4.0 392 0.8621 0.7603 0.6998 0.6568 0.7603
No log 5.0 490 0.8062 0.7887 0.7500 0.7449 0.7887
0.91 6.0 588 0.7465 0.8041 0.7660 0.7636 0.8041
0.91 7.0 686 0.6324 0.8247 0.8163 0.8187 0.8247
0.91 8.0 784 0.7333 0.7964 0.7703 0.7740 0.7964
0.91 9.0 882 0.6590 0.8325 0.8208 0.8106 0.8325
0.91 10.0 980 0.9854 0.8196 0.7890 0.7920 0.8196
0.4246 11.0 1078 0.7023 0.8247 0.8054 0.8138 0.8247
0.4246 12.0 1176 0.8995 0.8325 0.8120 0.8068 0.8325
0.4246 13.0 1274 0.8589 0.8299 0.8145 0.8058 0.8299
0.4246 14.0 1372 0.9859 0.8376 0.8151 0.8123 0.8376
0.4246 15.0 1470 0.8452 0.8402 0.8318 0.8341 0.8402
0.1637 16.0 1568 1.1156 0.8351 0.8157 0.8196 0.8351
0.1637 17.0 1666 1.1514 0.8325 0.8122 0.8218 0.8325
0.1637 18.0 1764 1.0092 0.8428 0.8266 0.8320 0.8428
0.1637 19.0 1862 1.0368 0.8351 0.8229 0.8287 0.8351
0.1637 20.0 1960 1.0600 0.8479 0.8319 0.8315 0.8479
0.0391 21.0 2058 1.1046 0.8428 0.8293 0.8269 0.8428
0.0391 22.0 2156 1.1178 0.8454 0.8262 0.8280 0.8454
0.0391 23.0 2254 1.1103 0.8428 0.8268 0.8295 0.8428
0.0391 24.0 2352 1.1179 0.8428 0.8274 0.8313 0.8428
0.0391 25.0 2450 1.1134 0.8402 0.8233 0.8254 0.8402

Framework versions

  • Transformers 4.17.0
  • Pytorch 1.10.2+cu113
  • Datasets 2.3.2
  • Tokenizers 0.12.1
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