diff --git a/skl2onnx/common/_topology.py b/skl2onnx/common/_topology.py
index 23c3c1274..1197cb8b4 100644
--- a/skl2onnx/common/_topology.py
+++ b/skl2onnx/common/_topology.py
@@ -87,11 +87,11 @@ def get_default_opset_for_domain(domain):
     if domain == "":
         return main_opset
     if domain == "ai.onnx.ml":
-        if main_opset >= 16:
+        if main_opset >= 18:
             return 3
-        if main_opset < 6:
-            return 1
-        return 2
+        if main_opset >= 6:
+            return 2
+        return 1
     if domain == "ai.onnx.training":
         return 1
     return None
diff --git a/skl2onnx/operator_converters/tfidf_transformer.py b/skl2onnx/operator_converters/tfidf_transformer.py
index fe6c20239..a57288223 100644
--- a/skl2onnx/operator_converters/tfidf_transformer.py
+++ b/skl2onnx/operator_converters/tfidf_transformer.py
@@ -36,24 +36,15 @@ def convert_sklearn_tfidf_transformer(
         # code scikit-learn
         # np.log(X.data, X.data) --> does not apply on null coefficient
         # X.data += 1
-        # ONNX does not support sparse tensors before opset < 11
-        # approximated by X.data += 1 --> np.log(X.data, X.data)
-        if operator.target_opset < 11:
-            plus1 = scope.get_unique_variable_name("plus1")
-            C = operator.inputs[0].type.shape[1]
-            ones = scope.get_unique_variable_name("ones")
-            cst = np.ones((C,), dtype=float_type)
-            container.add_initializer(ones, proto_dtype, [C], cst.flatten())
-            apply_add(scope, data + [ones], plus1, container, broadcast=1)
-            plus1logged = scope.get_unique_variable_name("plus1logged")
-            apply_log(scope, plus1, plus1logged, container)
-            data = [plus1logged]
-        else:
-            # sparse containers have not yet been implemented.
-            raise RuntimeError(
-                "ONNX does not support sparse tensors before opset < 11, "
-                "sublinear_tf must be False."
-            )
+        plus1 = scope.get_unique_variable_name("plus1")
+        C = operator.inputs[0].type.shape[1]
+        ones = scope.get_unique_variable_name("ones")
+        cst = np.ones((C,), dtype=float_type)
+        container.add_initializer(ones, proto_dtype, [C], cst.flatten())
+        apply_add(scope, data + [ones], plus1, container, broadcast=1)
+        plus1logged = scope.get_unique_variable_name("plus1logged")
+        apply_log(scope, plus1, plus1logged, container)
+        data = [plus1logged]
 
     if op.use_idf:
         cst = op.idf_.astype(float_type)
diff --git a/tests/test_issues_2024.py b/tests/test_issues_2024.py
index 5b86475b7..258304e5d 100644
--- a/tests/test_issues_2024.py
+++ b/tests/test_issues_2024.py
@@ -4,11 +4,11 @@
 
 class TestInvestigate(unittest.TestCase):
     def test_issue_1053(self):
-        import onnxruntime as rt
         from sklearn.datasets import load_iris
         from sklearn.linear_model import LogisticRegression
         from sklearn.model_selection import train_test_split
         from sklearn.tree import DecisionTreeClassifier
+        import onnxruntime as rt
         from skl2onnx.common.data_types import FloatTensorType
         from skl2onnx import convert_sklearn
 
@@ -39,6 +39,72 @@ def test_issue_1053(self):
                 ]  # Select a single sample.
                 self.assertEqual(len(pred_onx.tolist()), 1)
 
+    def test_issue_1055(self):
+        import numpy as np
+        from numpy.testing import assert_almost_equal
+        import sklearn.feature_extraction.text
+        import sklearn.linear_model
+        import sklearn.pipeline
+        import onnxruntime as rt
+        import skl2onnx.common.data_types
+
+        lr = sklearn.linear_model.LogisticRegression(
+            C=100,
+            multi_class="multinomial",
+            solver="sag",
+            class_weight="balanced",
+            n_jobs=-1,
+        )
+        tf = sklearn.feature_extraction.text.TfidfVectorizer(
+            token_pattern="\\w+|[^\\w\\s]+",
+            ngram_range=(1, 1),
+            max_df=1.0,
+            min_df=1,
+            sublinear_tf=True,
+        )
+
+        pipe = sklearn.pipeline.Pipeline([("transformer", tf), ("logreg", lr)])
+
+        corpus = [
+            "This is the first document.",
+            "This document is the second document.",
+            "And this is the third one.",
+            "Is this the first document?",
+            "more text",
+            "$words",
+            "I keep writing things",
+            "how many documents now?",
+            "this is a really long setence",
+            "is this a final document?",
+        ]
+        labels = ["1", "2", "1", "2", "1", "2", "1", "2", "1", "2"]
+
+        pipe.fit(corpus, labels)
+
+        onx = skl2onnx.convert_sklearn(
+            pipe,
+            "a model",
+            initial_types=[
+                ("input", skl2onnx.common.data_types.StringTensorType([None, 1]))
+            ],
+            target_opset=19,
+            options={"zipmap": False},
+        )
+        for d in onx.opset_import:
+            if d.domain == "":
+                self.assertEqual(d.version, 19)
+            elif d.domain == "com.microsoft":
+                self.assertEqual(d.version, 1)
+            elif d.domain == "ai.onnx.ml":
+                self.assertEqual(d.version, 1)
+
+        expected = pipe.predict_proba(corpus)
+        sess = rt.InferenceSession(
+            onx.SerializeToString(), providers=["CPUExecutionProvider"]
+        )
+        got = sess.run(None, {"input": np.array(corpus).reshape((-1, 1))})
+        assert_almost_equal(expected, got[1], decimal=2)
+
 
 if __name__ == "__main__":
     unittest.main(verbosity=2)