Update README.md

README.md

@@ -13,8 +13,7 @@ inference: false
 
 This is the standard 4-class NER model for English that ships with [Flair](https://github.com/flairNLP/flair/).
 
-- F1-Score: **92,98**
-- Trained on: CoNLL-03 
+F1-Score: **92,98** (CoNLL-03)
 
 Predicts 4 tags:
 
@@ -25,6 +24,10 @@ Predicts 4 tags:
 | ORG         | organization name | 
 | MISC         | other name | 
 
+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`)
@@ -59,8 +62,25 @@ Span [1,2]: "George Washington"   [− Labels: PER (0.9968)]
 Span [5]: "Washington"   [− Labels: LOC (0.9994)]
 ```
 
-Indicating that the entities "George Washington" (labeled as a person) and "Washington" (labeled as a location) are found in the sentence "George Washington went to Washington". 
+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*". 
+
+
+---
 
+### Cite
+
+Please cite the following paper when using this model.
+
+```
+@inproceedings{akbik2018coling,
+  title={Contextual String Embeddings for Sequence Labeling},
+  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
+  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
+  pages     = {1638--1649},
+  year      = {2018}
+}
+```
+---
 
 ### Training: Script to train this model