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# 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
{"language": ["no", "nb", "nn"], "license": "cc-by-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "widget": [{"text": "Fl\u00f8yfjelltunnelen p\u00e5 E39 retning sentrum er \u00e5pen for fri ferdsel."}, {"text": "Slik kan du redusere str\u00f8mregningen din"}], "pipeline_tag": "text-classification", "base_model": "NbAiLab/nb-bert-large", "model-index": [{"name": "nb-bert-large-user-needs", "results": []}]}
transformers pytorch safetensors bert text-classification generated_from_trainer no nb nn base_model:NbAiLab/nb-bert-large license:cc-by-4.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #no #nb #nn #base_model-NbAiLab/nb-bert-large #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 

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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #no #nb #nn #base_model-NbAiLab/nb-bert-large #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


* Transformers 4.17.0
* Pytorch 1.10.2+cu113
* Datasets 2.3.2
* Tokenizers 0.12.1
passage: TAGS
#transformers #pytorch #safetensors #bert #text-classification #generated_from_trainer #no #nb #nn #base_model-NbAiLab/nb-bert-large #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### 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### Framework versions


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