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@@ -11,9 +11,9 @@ inference: false
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  ## 4-Language NER in Flair (English, German, Dutch and Spanish)
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- This is the standard 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French.
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- F1-Score: **92,16** (CoNLL-03 English), **87,33** (CoNLL-03 German revised), **88,96** (CoNLL-03 Dutch), **86,65** (CoNLL-03 Spanish)
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  Predicts 4 tags:
@@ -38,7 +38,7 @@ from flair.data import Sentence
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  from flair.models import SequenceTagger
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  # load tagger
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- tagger = SequenceTagger.load("flair/ner-multi")
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  # make example sentence in any of the four languages
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  sentence = Sentence("George Washington ging nach Washington")
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  WordEmbeddings('de'),
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  # contextual string embeddings, forward
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- FlairEmbeddings('multi-forward'),
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  # contextual string embeddings, backward
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- FlairEmbeddings('multi-backward'),
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  ]
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  # embedding stack consists of Flair and GloVe embeddings
@@ -124,7 +124,7 @@ from flair.trainers import ModelTrainer
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  trainer = ModelTrainer(tagger, corpus)
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  # 7. run training
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- trainer.train('resources/taggers/ner-multi',
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  train_with_dev=True,
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  max_epochs=150)
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  ```
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  ### Cite
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- Please cite the following paper when using this model.
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  @inproceedings{akbik2018coling,
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  ## 4-Language NER in Flair (English, German, Dutch and Spanish)
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+ This is the fast 4-class NER model for 4 CoNLL-03 languages that ships with [Flair](https://github.com/flairNLP/flair/). Also kind of works for related languages like French.
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+ F1-Score: **91,51** (CoNLL-03 English), **85,72** (CoNLL-03 German revised), **86,22** (CoNLL-03 Dutch), **85,78** (CoNLL-03 Spanish)
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  Predicts 4 tags:
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  from flair.models import SequenceTagger
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  # load tagger
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+ tagger = SequenceTagger.load("flair/ner-multi-fast")
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  # make example sentence in any of the four languages
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  sentence = Sentence("George Washington ging nach Washington")
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  WordEmbeddings('de'),
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  # contextual string embeddings, forward
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+ FlairEmbeddings('multi-forward-fast'),
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  # contextual string embeddings, backward
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+ FlairEmbeddings('multi-backward-fast'),
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  ]
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  # embedding stack consists of Flair and GloVe embeddings
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  trainer = ModelTrainer(tagger, corpus)
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  # 7. run training
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+ trainer.train('resources/taggers/ner-multi-fast',
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  train_with_dev=True,
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  max_epochs=150)
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  ```
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  ### Cite
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+ Please cite the following papers when using this model.
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+
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+
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+ ```
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+ @misc{akbik2019multilingual,
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+ title={Multilingual sequence labeling with one model},
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+ author={Akbik, Alan and Bergmann, Tanja and Vollgraf, Roland}
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+ booktitle = {{NLDL} 2019, Northern Lights Deep Learning Workshop},
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+ year = {2019}
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+ }
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+ ```
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+
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  ```
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  @inproceedings{akbik2018coling,