Update README.md

README.md

@@ -71,7 +71,7 @@ labels = ["Microsoft <> founder", "Microsoft <> inception date", "Bill Gates <>
 entities = model.predict_entities(text, labels)
 
 for entity in entities:
-    print(entity["label"], “=>”, entity["text"])
+    print(entity["label"], "=>", entity["text"])
 ```
 ### Construct relations extraction pipeline with [utca](https://github.com/Knowledgator/utca)
 First of all, we need import neccessary components of the library and initalize predictor - GLiNER model and construct pipeline that combines NER and realtions extraction:
@@ -202,7 +202,7 @@ labels = ["summary"]
 input_ = prompt+text
 
 threshold = 0.5
-summaries = model.predict_entities(input_, labels, threshold=treashold)
+summaries = model.predict_entities(input_, labels, threshold=threshold)
 
 for summary in summaries:
     print(summary["text"], "=>", summary["score"])