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  This is the large 4-class NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
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- F1-Score: **94,36** (CoNLL-03)
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  **! This model only works with Flair version 0.8 (will be released in the next few days) !**
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  tagger = SequenceTagger.load("flair/ner-german-large")
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  # make example sentence
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- sentence = Sentence("George Washington went to Washington")
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  # predict NER tags
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  tagger.predict(sentence)
@@ -64,7 +64,7 @@ Span [1,2]: "George Washington" [− Labels: PER (1.0)]
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  Span [5]: "Washington" [− Labels: LOC (1.0)]
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  ```
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- So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
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  ---
 
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  This is the large 4-class NER model for German that ships with [Flair](https://github.com/flairNLP/flair/).
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+ F1-Score: **92,31** (CoNLL-03 German revised)
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  **! This model only works with Flair version 0.8 (will be released in the next few days) !**
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  tagger = SequenceTagger.load("flair/ner-german-large")
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  # make example sentence
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+ sentence = Sentence("George Washington ging nach Washington")
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  # predict NER tags
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  tagger.predict(sentence)
 
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  Span [5]: "Washington" [− Labels: LOC (1.0)]
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  ```
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+ So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington ging nach Washington*".
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  ---