readme fix

README.md

@@ -111,8 +111,7 @@ Maximal values (full match) :
 >>> references = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
 >>> results = seqeval.compute(predictions=predictions, references=references)
 >>> print(results)
-{'MISC': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'overall_precision': 1.0, 'overall_recall': 1.0, 'overall_f1': 1.0, 'overall_accuracy': 1.0}
-
+{'MISC': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'overall': {'precision': 1.0, 'recall': 1.0, 'f1-score': 1.0, 'accuracy': 1.0}}
 ```
 
 Minimal values (no match):
@@ -123,7 +122,7 @@ Minimal values (no match):
 >>> references = [['B-MISC', 'O', 'O'], ['I-PER', '0', 'I-PER']]
 >>> results = seqeval.compute(predictions=predictions, references=references)
 >>> print(results)
-{'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}, 'PER': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}, '_': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}, 'overall_precision': 0.0, 'overall_recall': 0.0, 'overall_f1': 0.0, 'overall_accuracy': 0.0}
+{'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}, 'PER': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}, '_': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}, 'overall': {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'accuracy': 0.0}}
 ```
 
 Partial match:
@@ -134,7 +133,7 @@ Partial match:
 >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
 >>> results = seqeval.compute(predictions=predictions, references=references)
 >>> print(results)
-{'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'overall_precision': 0.5, 'overall_recall': 0.5, 'overall_f1': 0.5, 'overall_accuracy': 0.8}
+{'MISC': {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}, 'PER': {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 1}, 'overall': {'precision': 0.5, 'recall': 0.5, 'f1-score': 0.5, 'accuracy': 0.8}}
 ```
 
 ## Limitations and bias

seqeval_with_fbeta.py

@@ -94,7 +94,7 @@ Examples:
     >>> predictions = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
     >>> references = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
     >>> seqeval = evaluate.load("seqeval")
-    >>> results = seqeval.compute(predictions=predictions, references=references, beta=1.0)
+    >>> results = seqeval.compute(predictions=predictions, references=references, beta=1)
     >>> print(list(results.keys()))
     ['MISC', 'PER', 'overall']
     >>> print(results["overall"]["f1"])