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nb-bert-large-user-needs

This model is a fine-tuned version of NbAiLab/nb-bert-large 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: 1.0102
  • Accuracy: 0.8900
  • F1: 0.8859
  • Precision: 0.8883
  • Recall: 0.8900

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: 8
  • 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 1.0 195 0.6790 0.8082 0.7567 0.7679 0.8082
No log 2.0 390 0.5577 0.8465 0.8392 0.8364 0.8465
0.8651 3.0 585 0.5494 0.8338 0.8191 0.8145 0.8338
0.8651 4.0 780 0.5453 0.8517 0.8386 0.8293 0.8517
0.8651 5.0 975 0.8855 0.8491 0.8298 0.8444 0.8491
0.3707 6.0 1170 0.7282 0.8645 0.8526 0.8581 0.8645
0.3707 7.0 1365 0.8797 0.8619 0.8537 0.8573 0.8619
0.1092 8.0 1560 0.9120 0.8491 0.8520 0.8579 0.8491
0.1092 9.0 1755 1.0700 0.8696 0.8615 0.8669 0.8696
0.1092 10.0 1950 1.0599 0.8670 0.8654 0.8701 0.8670
0.0355 11.0 2145 1.0808 0.8670 0.8656 0.8685 0.8670
0.0355 12.0 2340 1.0102 0.8900 0.8859 0.8883 0.8900
0.0002 13.0 2535 1.0236 0.8849 0.8812 0.8824 0.8849
0.0002 14.0 2730 1.0358 0.8875 0.8833 0.8841 0.8875
0.0002 15.0 2925 1.0476 0.8875 0.8833 0.8841 0.8875
0.0001 16.0 3120 1.0559 0.8798 0.8764 0.8776 0.8798
0.0001 17.0 3315 1.0648 0.8798 0.8754 0.8765 0.8798
0.0001 18.0 3510 1.0720 0.8798 0.8754 0.8765 0.8798
0.0001 19.0 3705 1.0796 0.8824 0.8775 0.8783 0.8824
0.0001 20.0 3900 1.0862 0.8798 0.8739 0.8745 0.8798
0.0 21.0 4095 1.0917 0.8798 0.8739 0.8745 0.8798
0.0 22.0 4290 1.0973 0.8798 0.8739 0.8745 0.8798
0.0 23.0 4485 1.1007 0.8798 0.8739 0.8745 0.8798
0.0 24.0 4680 1.1029 0.8798 0.8739 0.8745 0.8798
0.0 25.0 4875 1.1037 0.8798 0.8739 0.8745 0.8798

Framework versions

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