readme: ---
  license: cc-by-4.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  - f1
  - precision
  - recall
  widget:
  - text: "Fløyfjelltunnelen på E39 retning sentrum er open for fri ferdsel."
  model-index:
  - name: nb-bert-large-user-needs
    results: []
  ---
  
  <!-- 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](https://huggingface.co/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](https://www.linkedin.com/pulse/five-lessons-i-learned-while-digitally-changing-bbc-world-shishkin/). 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
  