diff --git a/cebra/integrations/sklearn/metrics.py b/cebra/integrations/sklearn/metrics.py
index 41dc67f..4b3c08e 100644
--- a/cebra/integrations/sklearn/metrics.py
+++ b/cebra/integrations/sklearn/metrics.py
@@ -141,7 +141,7 @@ def goodness_of_fit_score(cebra_model: cebra_sklearn_cebra.CEBRA,
         >>> cebra_model = cebra.CEBRA(max_iterations=10)
         >>> cebra_model.fit(neural_data)
         CEBRA(max_iterations=10)
-        >>> gof = cebra.goodness_of_fit_score(cebra_model, neural_data)
+        >>> gof = cebra.sklearn.metrics.goodness_of_fit_score(cebra_model, neural_data)
     """
     loss = infonce_loss(cebra_model,
                         X,
@@ -172,7 +172,7 @@ def goodness_of_fit_history(model):
         >>> cebra_model = cebra.CEBRA(max_iterations=10)
         >>> cebra_model.fit(neural_data)
         CEBRA(max_iterations=10)
-        >>> gof_history = cebra.goodness_of_fit_history(cebra_model)
+        >>> gof_history = cebra.sklearn.metrics.goodness_of_fit_history(cebra_model)
     """
     infonce = np.array(model.state_dict_["log"]["total"])
     return infonce_to_goodness_of_fit(infonce, model)
@@ -215,7 +215,7 @@ def infonce_to_goodness_of_fit(infonce: Union[float, Iterable[float]],
     num_sessions = model.num_sessions_
     if num_sessions is None:
         num_sessions = 1
-    chance_level = np.log(model.batch_size * (model.num_sessions_ or 1))
+    chance_level = np.log(model.batch_size * num_sessions)
     return (chance_level - infonce) * nats_to_bits