diff --git a/src/lighteval/metrics/harness_compatibility/drop.py b/src/lighteval/metrics/harness_compatibility/drop.py
index 57b9f18e..d6c8ac30 100644
--- a/src/lighteval/metrics/harness_compatibility/drop.py
+++ b/src/lighteval/metrics/harness_compatibility/drop.py
@@ -22,17 +22,30 @@
 
 import re
 import string
+from typing import List, Set, Tuple
 
 import numpy as np
 from scipy.optimize import linear_sum_assignment
 
 
 def drop_metrics(predictions: list[str], formatted_doc, **kwargs):  # noqa: C901
-    """F1 score from bag of words: comes from Harness Drop
+    """F1 score from bag of words: comes from Harness Drop. DROP offers two metrics,
+    a quasi exact match and a numeracy-focused F1 score. Quasi in the sense that it
+    does some normalizations before matching and numeracy-focused in the sense that
+    if there's number mismatch between the target and prediction F1 score is set to 0.
+    F1 score is computed using the intersection of target and prediction's BoW
+    representations with the additional spice that if the answer and/or prediction is
+    comprised of multiple spans, a greedy matching is done between the two sets of spans
+    (based on the very BoW overlap) and the average over F1 of pairs is returned.
+    DROP also accepts multiple answers in which case, the maximum of F1/ Exact Match
+    between prediction and the different answers is taken.
+
+    For more information, please refer to the section 5 of the DROP paper (https://aclanthology.org/N19-1246/).
+
     Todo: this code is really hard to follow, simplify when possible
     """
 
-    def _answer_to_bags(answer):
+    def _answer_to_bags(answer: List[str]) -> Tuple[List[str], List[Set[str]]]:
         if isinstance(answer, (list, tuple)):
             raw_spans = answer
         else:
@@ -45,7 +58,7 @@ def _answer_to_bags(answer):
             token_bags.append(set(normalized_span.split()))
         return normalized_spans, token_bags
 
-    def _get_metrics(predicted, gold):
+    def _get_metrics(predicted: List[str], gold: List[str]):
         """
         Takes a predicted answer and a gold answer (that are both either a string or a list of
         strings), and returns exact match and the DROP F1 metric for the prediction.  If you are
@@ -53,15 +66,17 @@ def _get_metrics(predicted, gold):
         validation, or while training), this is the function you want to call, after using
         :func:`answer_json_to_strings` when reading the gold answer from the released data file.
         """
-        predicted_bags = _answer_to_bags(predicted)
-        gold_bags = _answer_to_bags(gold)
+        pred_normalized_spans, pred_bags = _answer_to_bags(predicted)
+        gold_normalized_spans, gold_bags = _answer_to_bags(gold)
 
-        if set(predicted_bags[0]) == set(gold_bags[0]) and len(predicted_bags[0]) == len(gold_bags[0]):
+        if set(pred_normalized_spans) == set(gold_normalized_spans) and len(gold_normalized_spans) == len(
+            gold_normalized_spans
+        ):
             exact_match = 1.0
         else:
             exact_match = 0.0
 
-        f1_per_bag = _align_bags(predicted_bags[1], gold_bags[1])
+        f1_per_bag = _align_bags(pred_bags, gold_bags)
         f1 = np.mean(f1_per_bag)
         f1 = round(f1, 2)
         return exact_match, f1
@@ -73,7 +88,7 @@ def _is_number(text):
         except ValueError:
             return False
 
-    def _match_numbers_if_present(gold_bag, predicted_bag):
+    def _match_numbers_if_present(gold_bag: Set[str], predicted_bag: Set[str]):
         gold_numbers = set()
         predicted_numbers = set()
         for word in gold_bag:
@@ -86,7 +101,7 @@ def _match_numbers_if_present(gold_bag, predicted_bag):
             return True
         return False
 
-    def _align_bags(predicted, gold):
+    def _align_bags(predicted: List[Set[str]], gold: List[Set[str]]) -> np.array:
         """
         Takes gold and predicted answer sets and first finds the optimal 1-1 alignment
         between them and gets maximum metric values over all the answers.
@@ -136,7 +151,7 @@ def _fix_number(text):
     def _tokenize(text):
         return re.split(" |-", text)
 
-    def _normalize(answer):
+    def _normalize(answer: str):
         tokens = [
             _white_space_fix(_remove_articles(_fix_number(_remove_punc(token.lower())))) for token in _tokenize(answer)
         ]
@@ -147,9 +162,9 @@ def _normalize(answer):
     max_em = 0
     max_f1 = 0
     for gold_answer in formatted_doc.specific["golds_no_preprocessing"]:
+        exact_match, f1_score = _get_metrics(predictions, gold_answer)
         if isinstance(gold_answer, list):
             gold_answer = gold_answer[0]
-        exact_match, f1_score = _get_metrics(predictions, gold_answer)
         if gold_answer.strip():
             max_em = max(max_em, exact_match)
             max_f1 = max(max_f1, f1_score)