eval

Create_lists.py

@@ -26,4 +26,4 @@ df = pd.DataFrame(data)
 
 print(df.head(5))
 
-df.to_csv(r'data.csv',index=False, sep='\t')
+df.to_csv(r'data.tsv',index=False, sep='\t')

Makiety.py

@@ -27,20 +27,14 @@ class ML_NLU:
 
     def conllu2flair(self, sentences, label=None):
         fsentences = []
-
         for sentence in sentences:
             fsentence = Sentence()
-
             for token in sentence:
                 ftoken = Token(token['form'])
-
                 if label:
                     ftoken.add_tag(label, token[label])
-
                 fsentence.add_token(ftoken)
-
             fsentences.append(fsentence)
-
         return SentenceDataset(fsentences)
 
 

eval.py

@@ -0,0 +1,48 @@
+import pandas as pd
+from tabulate import tabulate
+from flair.data import Sentence, Token
+from flair.datasets import SentenceDataset
+from flair.models import SequenceTagger
+
+def conllu2flair(sentences, label=None):
+    fsentences = []
+    for sentence in sentences:
+        fsentence = Sentence()
+        for token in sentence:
+            ftoken = Token(token['form'])
+            if label:
+                ftoken.add_tag(label, token[label])
+            fsentence.add_token(ftoken)
+        fsentences.append(fsentence)
+    return SentenceDataset(fsentences)
+
+def predict(frame_model, sentence):
+    csentence = [{'form': word} for word in sentence]
+    fsentence = conllu2flair([csentence])[0]
+    frame_model.predict(fsentence)
+    possible_intents = {}
+    for token in fsentence:
+        for intent in token.annotation_layers["frame"]:
+            if(intent.value in possible_intents):
+                possible_intents[intent.value] += intent.score
+            else:
+                possible_intents[intent.value] = intent.score
+    return max(possible_intents)
+
+frame_model = SequenceTagger.load('frame-model/final-model.pt')
+data = []
+with open('data.tsv') as f:
+    lines = f.readlines()
+
+for line in lines[1:]:
+    data.append((line.split("\t")[0], line.split("\t")[1]))
+
+correct = 0
+for sentence in data:
+    predicted_intent = predict(frame_model, sentence[0].split())
+    if predicted_intent == sentence[1].replace('\n',''):
+        correct+=1
+    else:
+        print(predicted_intent + " != " + sentence[1].replace('\n',''))
+
+print(f"{correct/len(data)} {correct}/{len(data)}")

train.py

@@ -70,9 +70,9 @@ frame_tagger = SequenceTagger(hidden_size=256, embeddings=embeddings,
 #             train_with_dev=False)
 
             
-frame_trainer = ModelTrainer(frame_tagger, frame_corpus)
+# frame_trainer = ModelTrainer(frame_tagger, frame_corpus)
-frame_trainer.train('frame-model',
+# frame_trainer.train('frame-model',
-            learning_rate=0.1,
+#             learning_rate=0.1,
-            mini_batch_size=32,
+#             mini_batch_size=32,
-            max_epochs=100,
+#             max_epochs=100,
-            train_with_dev=False)
+#             train_with_dev=False)