readme: ---
  tags:
  - generated_from_trainer
  language:
  - sv
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: BERT_swedish-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        config: sv
        split: train
        args: sv
      metrics:
      - name: Precision
        type: precision
        value: 0.9340386115444618
      - name: Recall
        type: recall
        value: 0.9418907624993855
      - name: F1
        type: f1
        value: 0.9379482534942355
      - name: Accuracy
        type: accuracy
        value: 0.979997105690534
  widget:
  - "Jag heter Peter Petersson och jag jobbar på Skatteverket. Jag bor i Uppsala."
  inference:
    parameters:
      aggregation_strategy: "first"
  ---
  
  <!-- 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. -->
  
  # BERT_swedish-ner
  
  This model is a fine-tuned version of [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1316
  - Precision: 0.9340
  - Recall: 0.9419
  - F1: 0.9379
  - Accuracy: 0.9800
  
  ## Model description
  
  Finetuned the model from [KB/bert-base-swedish-cased](https://huggingface.co/KB/bert-base-swedish-cased) for Swedish NER task. The model can classify three categories:
  - PER (person names)
  - LOC (Location)
  - ORG (Organization)
  
  ## Intended uses & limitations
  
  NER, token classification
  
  ## Training and evaluation data
  
  wikiann-SV dataset
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 12
  - eval_batch_size: 12
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  