Update README.md

README.md

@@ -12,30 +12,35 @@ widget:
 
 ## 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 | 
+
+|**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 | 
+| 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("flair/ner-english-ontonotes-large")
 # make example sentence
-sentence = Sentence(<Insert Text>)
+sentence = Sentence("Hampus Londögård bor i Lund och har levererat denna model idag.")
 # predict NER tags
 tagger.predict(sentence)
 # print sentence
@@ -48,8 +53,13 @@ for entity in sentence.get_spans('ner'):
 ```
 This yields the following output:
 ```
-TODO()
+Span [0,1]: "Hampus Londögård"   [− Labels: PRS (1.0)]
+Span [4]: "Lund"   [− Labels: LOC (1.0)]
+Span [10]: "idag"   [− Labels: TME(1.0)]
 ```
-So, the entities "*TODO*" (labeled as a **EVT**), are found in the sentence "TODO". 
+
+So, the entities "_Hampus Londögård_" (labeled as a **PRS**), "_Lund_" (labeled as a **LOC**), "_idag_" (labeled as a **TME**) are found in the sentence "_Hampus Londögård bor i Lund och har levererat denna model idag._". 
+
 ---
-Please mention londogard if using this models.
+
+**Please mention londogard if using this models.**