initial model commit

README.md

@@ -28,10 +28,10 @@ from flair.data import Sentence
 from flair.models import SequenceTagger
 
 # load tagger
-tagger = SequenceTagger.load("flair/pos-english")
+tagger = SequenceTagger.load("flair/frame-english")
 
 # make example sentence
-sentence = Sentence("I love Berlin.")
+sentence = Sentence("George returned to Berlin to return his hat.")
 
 # predict NER tags
 tagger.predict(sentence)
@@ -40,23 +40,20 @@ tagger.predict(sentence)
 print(sentence)
 
 # print predicted NER spans
-print('The following NER tags are found:')
+print('The following frame tags are found:')
 # iterate over entities and print
-for entity in sentence.get_spans('pos'):
+for entity in sentence.get_spans('frame'):
     print(entity)
 
 ```
 
 This yields the following output:
 ```
-Span [1]: "I"   [− Labels: PRP (1.0)]
-Span [2]: "love"   [− Labels: VBP (1.0)]
-Span [3]: "Berlin"   [− Labels: NNP (0.9999)]
-Span [4]: "."   [− Labels: . (1.0)]
-
+Span [2]: "returned"   [− Labels: return.01 (0.9951)]
+Span [6]: "return"   [− Labels: return.02 (0.6361)]
 ```
 
-So, the word "*I*" is labeled as a **pronoun** (PRP),  "*love*" is labeled as a **verb** (VBP) and "*Berlin*" is labeled as a **proper noun** (NNP) in the sentence "*TheI love Berlin*". 
+So, the word "*returned*" is labeled as **return.01** (as in *go back somewhere*) while "*return*" is labeled as **return.02** (as in *give back something*) in the sentence "*George returned to Berlin to return his hat*". 
 
 
 ---
@@ -75,7 +72,6 @@ corpus = ColumnCorpus(
     "resources/tasks/srl", column_format={1: "text", 11: "frame"}
 )
 
-
 # 2. what tag do we want to predict?
 tag_type = 'frame'
 
@@ -87,9 +83,9 @@ embedding_types = [
 
     BytePairEmbeddings("en"),
 
-    FlairEmbeddings("news-forward-fast"),
+    FlairEmbeddings("news-forward"),
 
-    FlairEmbeddings("news-backward-fast"),
+    FlairEmbeddings("news-backward"),
 ]
 
 # embedding stack consists of Flair and GloVe embeddings