diff --git a/ot/bregman/_geomloss.py b/ot/bregman/_geomloss.py
index 594d3b8e0..0b1abd1b8 100644
--- a/ot/bregman/_geomloss.py
+++ b/ot/bregman/_geomloss.py
@@ -87,7 +87,7 @@ def empirical_sinkhorn2_geomloss(X_s, X_t, reg, a=None, b=None, metric='sqeuclid
 
     The algorithm used for solving the problem is the Sinkhorn-Knopp matrix
     scaling algorithm as proposed in and computed in log space for
-    better stability and epsilon-scaling. The solution is computed ina lzy way
+    better stability and epsilon-scaling. The solution is computed in a lazy way
     using the Geomloss [60] and the KeOps library [61].
 
     Parameters
diff --git a/ot/solvers.py b/ot/solvers.py
index c4c0c79ed..e4eca9575 100644
--- a/ot/solvers.py
+++ b/ot/solvers.py
@@ -1272,7 +1272,7 @@ def solve_sample(X_a, X_b, a=None, b=None, metric='sqeuclidean', reg=None, reg_t
             if not lazy0:  # store plan if not lazy
                 plan = lazy_plan[:]
 
-        elif method.startswith('geomloss'):  # Geomloss solver for entropi OT
+        elif method.startswith('geomloss'):  # Geomloss solver for entropic OT
 
             split_method = method.split('_')
             if len(split_method) == 2: