readme: ---
  
  license: apache-2.0
  
  tags:
  
  - token-classification
  
  datasets:
  
  - wikiann
  
  metrics:
  
  - precision
  
  - recall
  
  - f1
  
  - accuracy
  
  model-index:
  
  - name: ner-swedish-wikiann
  
    results:
  
    - task:
  
        name: Token Classification
  
        type: token-classification
  
      dataset:
  
        name: wikiann
  
        type: wikiann
  
      metrics:
  
        - name: Precision
  
          type: precision
  
          value: 0.8331921416757433
  
        - name: Recall
  
          type: recall
  
          value: 0.84243586083126
  
        - name: F1
  
          type: f1
  
          value: 0.8377885044416501
  
        - name: Accuracy
  
          type: accuracy
  
          value: 0.91930707459758
  
  ---
  
  <!-- 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. -->
  
  # ner-swedish-wikiann
  
  This model is a fine-tuned version of [nordic-roberta-wiki](hhttps://huggingface.co/flax-community/nordic-roberta-wiki) trained for NER on the wikiann dataset.
  
  eval F1-Score: **83,78** 
  
  test F1-Score: **83,76** 
  
  ## Model Usage
  
  ```python
  
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann")
  
  model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann")
  
  nlp = pipeline("ner", model=model, tokenizer=tokenizer)
  
  example = "Jag heter Per och jag jobbar på KTH"
  
  nlp(example)
  
  ```
  <!-- 
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  
  - learning_rate: 4.9086903597787154e-05
  
  - train_batch_size: 32
  
  - eval_batch_size: 16
  
  - seed: 42
  
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  
  - lr_scheduler_type: linear
  
  - num_epochs: 5.0
  
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  It achieves the following results on the evaluation set:
  
  - Loss: 0.3156
  
  - Precision: 0.8332
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("birgermoell/ner-swedish-wikiann")
  
  model = AutoModelForTokenClassification.from_pretrained("birgermoell/ner-swedish-wikiann")
  
  nlp = pipeline("ner", model=model, tokenizer=tokenizer)
  
  example = "Jag heter Per och jag jobbar på KTH"
  
  nlp(example)
  
  - F1: 0.8378
  
  - Accuracy: 0.9193
  
  It achieves the following results on the test set:
  
  - Loss: 0.3023
  
  - Precision: 0.8301
  
  - Recall: 0.8452
  
  - F1: 0.8376
  
  - Accuracy: 0.92
  
  ### Framework versions
  
  - Transformers 4.6.1
  
  - Pytorch 1.8.1+cu101
  
  - Datasets 1.6.2
  
  - Tokenizers 0.10.2
   -->
  