Adding flair swe ner model

README.md

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+---
+tags:
+- flair
+- token-classification
+- sequence-tagger-model
+language: sv
+datasets:
+- SUC 3.0
+widget:
+- text: "Hampus Londögård bor i Lund och har levererat denna model idag."
+---
+
+## Swedish NER in Flair (SUC 3.0)
+F1-Score: **85.6** (SUC 3.0)
+ B-PRS, I-PRS, B-ORG, I-ORG, B-TME, I-TME, B-WRK, B-LOC, I-LOC, B-EVN, I-EVN, B-MSR, I-MSR, I-WRK, B-OBJ, I-OBJ
+Predicts 8 tags:
+| **tag**                        | **meaning** |
+|---------------------------------|-----------|
+| PRS| cardinal value | 
+| ORG        | organisation name| 
+| TME        | time unit | 
+| WRK         | building name | 
+| LOC         | location name | 
+| EVN         | event name | 
+| MSR         | measurement unit | 
+| OBJ         | object | 
+
+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("flair/ner-english-ontonotes-large")
+# make example sentence
+sentence = Sentence(<Insert Text>)
+# 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:
+```
+TODO()
+```
+So, the entities "*TODO*" (labeled as a **EVT**), are found in the sentence "TODO". 
+---
+Please mention londogard if using this models.

pytorch_model.bin

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