readme: ---
  tags:
  - flair
  - token-classification
  - sequence-tagger-model
  language: sv
  datasets:
  - SUC 3.0
  widget:
  - text: "Hampus bor i Skåne och har levererat denna model idag."
  ---
  
  Published with ❤️ from [londogard](https://londogard.com).
  
  ## Swedish NER in Flair (SUC 3.0)
  F1-Score: **85.6** (SUC 3.0)
  
  Predicts 8 tags:
  
  |**Tag**|**Meaning**|
  |---|---|
  | PRS| person name | 
  | ORG        | organisation name| 
  | TME        | time unit | 
  | WRK         | building name | 
  | LOC         | location name | 
  | EVN         | event name | 
  | MSR         | measurement unit | 
  | OBJ         | object (like "Rolls-Royce" is a object in the form of a special car) | 
  
  Based on [Flair embeddings](https://www.aclweb.org/anthology/C18-1139/) and LSTM-CRF.
  
  ---
  
  ### Demo: How to use in Flair
  
  Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
  
  ```python
  from flair.data import Sentence
  from flair.models import SequenceTagger
  # load tagger
  tagger = SequenceTagger.load("londogard/flair-swe-ner")
  # make example sentence
  sentence = Sentence("Hampus bor i Skåne och har levererat denna model idag.")
  # predict NER tags
  tagger.predict(sentence)
  # print sentence
  print(sentence)
  # print predicted NER spans
  print('The following NER tags are found:')
  # iterate over entities and print
  for entity in sentence.get_spans('ner'):
      print(entity)
  ```
  This yields the following output:
  ```
  Span [0]: "Hampus"   [− Labels: PRS (1.0)]
  Span [3]: "Skåne"   [− Labels: LOC (1.0)]
  Span [9]: "idag"   [− Labels: TME(1.0)]
  ```
  
  So, the entities "_Hampus_" (labeled as a **PRS**), "_Skåne_" (labeled as a **LOC**), "_idag_" (labeled as a **TME**) are found in the sentence "_Hampus bor i Skåne och har levererat denna model idag._". 
  
  ---
  
  **Please mention londogard if using this models.**