typo

scoring_scripts/score_focal_seqeval.py

@@ -10,7 +10,7 @@ tokenizer = nlp.tokenizer
 def evaluate_FOCAL_seqeval(references_jsonl, predictions_jsonl, print_reports=False):
     '''
     Computes SEQEVAL scores.
-    1. convert the text into 'word' tokens using defaut spaCy tokenizer
+    1. convert the text into 'word' tokens using default spaCy tokenizer
     2. turn the references and the predictions into IOB2 style labels (one label per token, 'O' by default)
     3. compute f1-scores using SEQEVAL