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README.md

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+---
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+language: da
+datasets:
+- DaNE
+inference: false
+---
+
+# Danish NER in Flair (default model)
+
+This is the standard 4-class NER model for Danish that ships with [Flair](https://github.com/flairNLP/flair/).
+
+F1-Score: **81.78** (DaNER)
+
+Predicts 4 tags:
+
+| **tag**                        | **meaning** |
+|---------------------------------|-----------|
+| PER         | person name | 
+| LOC         | location name | 
+| ORG         | organization name | 
+| MISC         | other name | 
+
+Based on Transformer embeddings 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("flair/ner-danish")
+
+# make example sentence
+sentence = Sentence("Jens Peter Hansen kommer fra Danmark")
+
+# 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 [1,2,3]: "Jens Peter Hansen"   [− Labels: PER (0.9961)]
+Span [6]: "Danmark"   [− Labels: LOC (0.9816)]
+```
+
+So, the entities "*Jens Peter Hansen*" (labeled as a **person**) and "*Danmark*" (labeled as a **location**) are found in the sentence "*Jens Peter Hansen kommer fra Danmark*". 
+
+
+---
+
+### Training: Script to train this model
+
+The model was trained by the [DaNLP project](https://github.com/alexandrainst/danlp) using the [DaNE corpus](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#danish-dependency-treebank-dane-dane). Check their repo for more information.
+
+The following Flair script may be used to train such a model: 
+
+```python
+from flair.data import Corpus
+from flair.datasets import DANE
+from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
+
+# 1. get the corpus
+corpus: Corpus = DANE()
+
+# 2. what tag do we want to predict?
+tag_type = 'ner'
+
+# 3. make the tag dictionary from the corpus
+tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
+
+# 4. initialize each embedding we use
+embedding_types = [
+
+    # GloVe embeddings
+    WordEmbeddings('da'),
+
+    # contextual string embeddings, forward
+    FlairEmbeddings('da-forward'),
+
+    # contextual string embeddings, backward
+    FlairEmbeddings('da-backward'),
+]
+
+# embedding stack consists of Flair and GloVe embeddings
+embeddings = StackedEmbeddings(embeddings=embedding_types)
+
+# 5. initialize sequence tagger
+from flair.models import SequenceTagger
+
+tagger = SequenceTagger(hidden_size=256,
+                        embeddings=embeddings,
+                        tag_dictionary=tag_dictionary,
+                        tag_type=tag_type)
+
+# 6. initialize trainer
+from flair.trainers import ModelTrainer
+
+trainer = ModelTrainer(tagger, corpus)
+
+# 7. run training
+trainer.train('resources/taggers/ner-danish',
+              train_with_dev=True,
+              max_epochs=150)
+```
+
+
+---
+
+### Cite
+
+Please cite the following papers when using this model.
+
+```
+@inproceedings{akbik-etal-2019-flair,
+    title = "{FLAIR}: An Easy-to-Use Framework for State-of-the-Art {NLP}",
+    author = "Akbik, Alan  and
+      Bergmann, Tanja  and
+      Blythe, Duncan  and
+      Rasul, Kashif  and
+      Schweter, Stefan  and
+      Vollgraf, Roland",
+    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
+    year = "2019",
+    url = "https://www.aclweb.org/anthology/N19-4010",
+    pages = "54--59",
+}
+```
+
+And check the [DaNLP project](https://github.com/alexandrainst/danlp) for more information.
+
+---
+
+### Issues?
+
+The Flair issue tracker is available [here](https://github.com/flairNLP/flair/issues/).