diff --git a/src/pinefarm/external/nnlojet/nnpdf_interface.py b/src/pinefarm/external/nnlojet/nnpdf_interface.py
index e918c07..ee75a96 100755
--- a/src/pinefarm/external/nnlojet/nnpdf_interface.py
+++ b/src/pinefarm/external/nnlojet/nnpdf_interface.py
@@ -125,7 +125,12 @@ def select_selectors(experiment, process):
     The experiment defines the cuts to be applied to each variable.
     The process defines the name of the variables in NNLOJET
     """
-    cuts = {"rapidity": (None, None), "pt": (20.0, None), "inv_mass": (None, None), "mt": (None, None)}
+    cuts = {
+        "rapidity": (None, None),
+        "pt": (20.0, None),
+        "inv_mass": (None, None),
+        "mt": (None, None),
+    }
 
     variables = {"rapidity": [], "pt": [], "inv_mass": [], "mt": []}
 
@@ -267,9 +272,10 @@ def generate_pinecard_from_nnpdf(nnpdf_dataset, scale="mz", output_path="."):
         for i, val in enumerate(unique_m2):
             idx = kin_df["M2"]["mid"] == val
             tmp = _1d_histogram(kin_df[idx], another_v)
+            tmp["name"] = f"{another_v}_bin_{i}"
             tmp["extra_selectors"] = [
                 {
-                    "observable": m_name,
+                    "observable": f"{m_name}",
                     "min": probable_bounds[i],
                     "max": probable_bounds[i + 1],
                 }
@@ -279,7 +285,9 @@ def generate_pinecard_from_nnpdf(nnpdf_dataset, scale="mz", output_path="."):
         # inclusive cross section, just create a big enough histogram
         histograms = [{"observable": "y", "bins": [-10.0, 10.0], "name": "tot"}]
     else:
-        raise NotImplementedError("3D distributions not implemented or process not recognized")
+        raise NotImplementedError(
+            "3D distributions not implemented or process not recognized"
+        )
 
     is_normalized = metadata.theory.operation.lower() == "ratio"
     if is_normalized: