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--- |
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language: |
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- 'no' |
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- nb |
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- nn |
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license: cc-by-4.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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widget: |
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- text: Fløyfjelltunnelen på E39 retning sentrum er åpen for fri ferdsel. |
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- text: Slik kan du redusere strømregningen din |
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pipeline_tag: text-classification |
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base_model: NbAiLab/nb-bert-large |
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model-index: |
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- name: nb-bert-large-user-needs |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# nb-bert-large-user-needs |
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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: |
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- Loss: 1.0102 |
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- Accuracy: 0.8900 |
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- F1: 0.8859 |
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- Precision: 0.8883 |
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- Recall: 0.8900 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 25 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 195 | 0.6790 | 0.8082 | 0.7567 | 0.7679 | 0.8082 | |
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| No log | 2.0 | 390 | 0.5577 | 0.8465 | 0.8392 | 0.8364 | 0.8465 | |
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| 0.8651 | 3.0 | 585 | 0.5494 | 0.8338 | 0.8191 | 0.8145 | 0.8338 | |
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| 0.8651 | 4.0 | 780 | 0.5453 | 0.8517 | 0.8386 | 0.8293 | 0.8517 | |
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| 0.8651 | 5.0 | 975 | 0.8855 | 0.8491 | 0.8298 | 0.8444 | 0.8491 | |
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| 0.3707 | 6.0 | 1170 | 0.7282 | 0.8645 | 0.8526 | 0.8581 | 0.8645 | |
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| 0.3707 | 7.0 | 1365 | 0.8797 | 0.8619 | 0.8537 | 0.8573 | 0.8619 | |
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| 0.1092 | 8.0 | 1560 | 0.9120 | 0.8491 | 0.8520 | 0.8579 | 0.8491 | |
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| 0.1092 | 9.0 | 1755 | 1.0700 | 0.8696 | 0.8615 | 0.8669 | 0.8696 | |
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| 0.1092 | 10.0 | 1950 | 1.0599 | 0.8670 | 0.8654 | 0.8701 | 0.8670 | |
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| 0.0355 | 11.0 | 2145 | 1.0808 | 0.8670 | 0.8656 | 0.8685 | 0.8670 | |
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| 0.0355 | 12.0 | 2340 | 1.0102 | 0.8900 | 0.8859 | 0.8883 | 0.8900 | |
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| 0.0002 | 13.0 | 2535 | 1.0236 | 0.8849 | 0.8812 | 0.8824 | 0.8849 | |
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| 0.0002 | 14.0 | 2730 | 1.0358 | 0.8875 | 0.8833 | 0.8841 | 0.8875 | |
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| 0.0002 | 15.0 | 2925 | 1.0476 | 0.8875 | 0.8833 | 0.8841 | 0.8875 | |
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| 0.0001 | 16.0 | 3120 | 1.0559 | 0.8798 | 0.8764 | 0.8776 | 0.8798 | |
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| 0.0001 | 17.0 | 3315 | 1.0648 | 0.8798 | 0.8754 | 0.8765 | 0.8798 | |
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| 0.0001 | 18.0 | 3510 | 1.0720 | 0.8798 | 0.8754 | 0.8765 | 0.8798 | |
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| 0.0001 | 19.0 | 3705 | 1.0796 | 0.8824 | 0.8775 | 0.8783 | 0.8824 | |
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| 0.0001 | 20.0 | 3900 | 1.0862 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | |
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| 0.0 | 21.0 | 4095 | 1.0917 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | |
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| 0.0 | 22.0 | 4290 | 1.0973 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | |
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| 0.0 | 23.0 | 4485 | 1.1007 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | |
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| 0.0 | 24.0 | 4680 | 1.1029 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | |
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| 0.0 | 25.0 | 4875 | 1.1037 | 0.8798 | 0.8739 | 0.8745 | 0.8798 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.10.2+cu113 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
