initial commit

README.md

@@ -15,6 +15,8 @@ This is the large 4-class NER model for English that ships with [Flair](https://
 
 F1-Score: **94,36** (corrected CoNLL-03)
 
+**! This model only works with Flair version 0.8 (will be released in the next few days) !**
+
 Predicts 4 tags:
 
 | **tag**                        | **meaning** |
@@ -58,8 +60,8 @@ for entity in sentence.get_spans('ner'):
 
 This yields the following output:
 ```
-Span [1,2]: "George Washington"   [− Labels: PER (0.9968)]
-Span [5]: "Washington"   [− Labels: LOC (0.9994)]
+Span [1,2]: "George Washington"   [− Labels: PER (1.0)]
+Span [5]: "Washington"   [− Labels: LOC (1.0)]
 ```
 
 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*".