初始化项目，由ModelHub XC社区提供模型

Model: ayh015/myLightningOPD
Source: Original Platform

slime/rollout/rm_hub/f1.py

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+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import re
+import string
+from collections import Counter
+
+
+def normalize_answer(s):
+
+    def remove_articles(text):
+        return re.sub(r"\b(a|an|the)\b", " ", text)
+
+    def white_space_fix(text):
+        return " ".join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return "".join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def f1_score(prediction, ground_truth):
+    ZERO_METRIC = (0, 0, 0)
+
+    if prediction is None:
+        return ZERO_METRIC
+
+    normalized_prediction = normalize_answer(prediction)
+    normalized_ground_truth = normalize_answer(ground_truth)
+
+    if normalized_prediction in ["yes", "no", "noanswer"] and normalized_prediction != normalized_ground_truth:
+        return ZERO_METRIC
+    if normalized_ground_truth in ["yes", "no", "noanswer"] and normalized_prediction != normalized_ground_truth:
+        return ZERO_METRIC
+
+    prediction_tokens = normalized_prediction.split()
+    ground_truth_tokens = normalized_ground_truth.split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return ZERO_METRIC
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1, precision, recall