readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-finetuned-ner_swedish_small_set_health_and_standart
    results: []
  ---
  # Named Entity Recognition model for swedish
  This model is a fine-tuned version of [KBLab/bert-base-swedish-cased-ner](https://huggingface.co/KBLab/bert-base-swedish-cased-ner)for only Swedish. It has been fine-tuned on the concatenation of a smaller version of SUC 3.0 and some medical text from the Swedish website 1177.
  
  The model will predict the following entities:
  
  | Tag | Name | Exampel |
  |:-------------:|:-----:|:----:|
  | PER	        |Person   | (e.g.,  Johan  and  Sofia)   |
  | LOC        | Location  | (e.g., Göteborg and Spanien)   | 
  | ORG        | Organisation   | (e.g., Volvo and Skatteverket) \  | 
  | PHARMA_DRUGS        | Medication  |  (e.g., Paracetamol and Omeprazol)|
  | HEALTH        | Illness/Diseases  | (e.g., Cancer, sjuk and diabetes)  | 
  | Relation        | Family members  |  (e.g., Mamma and Farmor)  | 
  
  
  
  <!-- 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-finetuned-ner_swedish_small_set_health_and_standart
  
  It achieves the following results on the evaluation set:
  - Loss: 0.0963
  - Precision: 0.7548
  - Recall: 0.7811
  - F1: 0.7677
  - Accuracy: 0.9756
  
  ## 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: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 219  | 0.1123          | 0.7674    | 0.6567 | 0.7078 | 0.9681   |
  | No log        | 2.0   | 438  | 0.0934          | 0.7643    | 0.7662 | 0.7652 | 0.9738   |
  | 0.1382        | 3.0   | 657  | 0.0963          | 0.7548    | 0.7811 | 0.7677 | 0.9756   |
  
  
  ### Framework versions
  
  - Transformers 4.19.3
  - Pytorch 1.7.1
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  