add evaluation

NLU_lab_7-8/evaluation.txt

@@ -0,0 +1,38 @@
+*** This evaluation file was generated automatically by the training script ***
+
+Results:
+- F-score (micro) 0.2609
+- F-score (macro) 0.1509
+- Accuracy 0.1648
+
+By class:
+                   precision    recall  f1-score   support
+
+         quantity     0.3846    0.8333    0.5263         6
+             time     0.3333    0.4286    0.3750         7
+            title     0.3333    0.2222    0.2667         9
+             goal     0.0000    0.0000    0.0000        10
+             area     0.0000    0.0000    0.0000         3
+             name     0.7500    0.6000    0.6667         5
+             date     0.3333    0.3333    0.3333         3
+         interval     0.0000    0.0000    0.0000         1
+             seat     0.0000    0.0000    0.0000         3
+     ticketnumber     0.0000    0.0000    0.0000         3
+           e-mail     0.0000    0.0000    0.0000         3
+            phone     1.0000    1.0000    1.0000         1
+              row     0.0000    0.0000    0.0000         2
+            movie     0.0000    0.0000    0.0000         2
+  reducedQuantity     0.0000    0.0000    0.0000         2
+            seats     0.0000    0.0000    0.0000         0
+     purchaseType     0.0000    0.0000    0.0000         1
+bankAccountNumber     0.0000    0.0000    0.0000         1
+            email     0.0000    0.0000    0.0000         1
+             hour     0.0000    0.0000    0.0000         1
+    seatPlacement     0.0000    0.0000    0.0000         1
+
+        micro avg     0.3000    0.2308    0.2609        65
+        macro avg     0.1493    0.1627    0.1509        65
+     weighted avg     0.2060    0.2308    0.2079        65
+      samples avg     0.1648    0.1648    0.1648        65
+
+2022-05-02 19:47:10,324 ----------------------------------------------------------------------------------------------------

NLU_lab_7-8/main.py

@@ -81,5 +81,17 @@ try:
     model = SequenceTagger.load('slot-model-pl/best-model.pt')
 except:
     model = SequenceTagger.load('slot-model-pl/final-model.pt')
+	
+log_file = open('slot-model-pl/training.log', encoding='utf-8')
+log_lines = log_file.readlines()
+log_file.close()
+with open('slot-model-pl/training.log', encoding='utf-8') as log_file, open('evaluation.txt', 'w', encoding='utf-8') \
+        as eval_file:
+    for num, line in enumerate(log_file):
+        if line == 'Results:\n':
+            lines_to_write_start = num
+    eval_file.write('*** This evaluation file was generated automatically by the training script ***\n\n')
+    for line in log_lines[lines_to_write_start:]:
+        eval_file.write(line)
         
 print(tabulate(predict(model, 'Jeden bilet na imię Jan Kowalski na film Batman'.split())))