<!-- 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. -->
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
