diff --git a/java/src/main/java/ai/onnxruntime/TensorInfo.java b/java/src/main/java/ai/onnxruntime/TensorInfo.java
index 69ccb954e8..1c21387b50 100644
--- a/java/src/main/java/ai/onnxruntime/TensorInfo.java
+++ b/java/src/main/java/ai/onnxruntime/TensorInfo.java
@@ -7,6 +7,7 @@ package ai.onnxruntime;
 import java.lang.reflect.Array;
 import java.nio.Buffer;
 import java.util.Arrays;
+import java.util.stream.Collectors;
 
 /** Describes an {@link OnnxTensor}, including it's size, shape and element type. */
 public class TensorInfo implements ValueInfo {
@@ -159,6 +160,12 @@ public class TensorInfo implements ValueInfo {
   /** The shape of the tensor. */
   final long[] shape;
 
+  /** The names of the unbound dimensions. */
+  final String[] dimensionNames;
+
+  /** If there are non-empty dimension names */
+  private final boolean hasNames;
+
   /** The Java type of this tensor. */
   public final OnnxJavaType type;
 
@@ -177,6 +184,9 @@ public class TensorInfo implements ValueInfo {
    */
   TensorInfo(long[] shape, OnnxJavaType type, OnnxTensorType onnxType) {
     this.shape = shape;
+    this.dimensionNames = new String[shape.length];
+    Arrays.fill(dimensionNames, "");
+    this.hasNames = false;
     this.type = type;
     this.onnxType = onnxType;
     this.numElements = elementCount(shape);
@@ -188,10 +198,20 @@ public class TensorInfo implements ValueInfo {
    * <p>Called from JNI.
    *
    * @param shape The tensor shape.
+   * @param names The dimension names.
    * @param typeInt The native type int.
    */
-  TensorInfo(long[] shape, int typeInt) {
+  TensorInfo(long[] shape, String[] names, int typeInt) {
     this.shape = shape;
+    this.dimensionNames = names;
+    boolean hasNames = false;
+    for (String s : names) {
+      if (!s.isEmpty()) {
+        hasNames = true;
+        break;
+      }
+    }
+    this.hasNames = hasNames;
     this.onnxType = OnnxTensorType.mapFromInt(typeInt);
     this.type = OnnxJavaType.mapFromOnnxTensorType(this.onnxType);
     this.numElements = elementCount(shape);
@@ -206,15 +226,42 @@ public class TensorInfo implements ValueInfo {
     return Arrays.copyOf(shape, shape.length);
   }
 
+  /**
+   * Get a copy of the tensor's named dimensions.
+   *
+   * @return A copof the tensor's named dimensions.
+   */
+  public String[] getDimensionNames() {
+    return Arrays.copyOf(dimensionNames, dimensionNames.length);
+  }
+
   @Override
   public String toString() {
-    return "TensorInfo(javaType="
-        + type.toString()
-        + ",onnxType="
-        + onnxType.toString()
-        + ",shape="
-        + Arrays.toString(shape)
-        + ")";
+    String output =
+        "TensorInfo(javaType="
+            + type.toString()
+            + ",onnxType="
+            + onnxType.toString()
+            + ",shape="
+            + Arrays.toString(shape);
+    if (hasNames) {
+      output =
+          output
+              + ",dimNames=["
+              + Arrays.stream(dimensionNames)
+                  .map(
+                      a -> {
+                        if (a.isEmpty()) {
+                          return "\"\"";
+                        } else {
+                          return a;
+                        }
+                      })
+                  .collect(Collectors.joining(","))
+              + "]";
+    }
+    output = output + ")";
+    return output;
   }
 
   /**
diff --git a/java/src/main/native/OrtJniUtil.c b/java/src/main/native/OrtJniUtil.c
index 879ba8a310..7b26291581 100644
--- a/java/src/main/native/OrtJniUtil.c
+++ b/java/src/main/native/OrtJniUtil.c
@@ -342,7 +342,6 @@ jobject convertToTensorInfo(JNIEnv *jniEnv, const OrtApi * api, const OrtTensorT
   if (code != ORT_OK) {
     return NULL;
   }
-  //printf("numDim %d\n",numDim);
   int64_t* dimensions = (int64_t*) malloc(sizeof(int64_t)*numDim);
   code = checkOrtStatus(jniEnv, api, api->GetDimensions(info, dimensions, numDim));
   if (code != ORT_OK) {
@@ -358,12 +357,31 @@ jobject convertToTensorInfo(JNIEnv *jniEnv, const OrtApi * api, const OrtTensorT
   free(dimensions);
   dimensions = NULL;
 
+  // Create the string array for the names.
+  const char** dimensionNames = (const char**) malloc(sizeof(char*)*numDim);
+  if (dimensionNames == NULL) {
+    throwOrtException(jniEnv, 1, "Not enough memory");
+    return NULL;
+  }
+  code = checkOrtStatus(jniEnv, api, api->GetSymbolicDimensions(info, dimensionNames, numDim));
+  if (code != ORT_OK) {
+    // extraction failed, exception has been thrown, return to Java.
+    free(dimensionNames);
+    return NULL;
+  }
+  jclass stringClazz = (*jniEnv)->FindClass(jniEnv, "java/lang/String");
+  jobjectArray names = (*jniEnv)->NewObjectArray(jniEnv, safecast_size_t_to_jsize(numDim), stringClazz, NULL);
+  for (size_t i = 0; i < numDim; i++) {
+    jobject javaName = (*jniEnv)->NewStringUTF(jniEnv, dimensionNames[i]);
+    (*jniEnv)->SetObjectArrayElement(jniEnv, names, safecast_size_t_to_jsize(i), javaName);
+  }
+  free(dimensionNames);
+
   // Create the TensorInfo object
   static const char *tensorInfoClassName = "ai/onnxruntime/TensorInfo";
   jclass clazz = (*jniEnv)->FindClass(jniEnv, tensorInfoClassName);
-  jmethodID tensorInfoConstructor = (*jniEnv)->GetMethodID(jniEnv,clazz, "<init>", "([JI)V");
-  //printf("TensorInfo class %p, methodID %p\n",clazz,tensorInfoConstructor);
-  jobject tensorInfo = (*jniEnv)->NewObject(jniEnv, clazz, tensorInfoConstructor, shape, onnxTypeInt);
+  jmethodID tensorInfoConstructor = (*jniEnv)->GetMethodID(jniEnv,clazz, "<init>", "([J[Ljava/lang/String;I)V");
+  jobject tensorInfo = (*jniEnv)->NewObject(jniEnv, clazz, tensorInfoConstructor, shape, names, onnxTypeInt);
   return tensorInfo;
 }
 
diff --git a/java/src/test/java/ai/onnxruntime/InferenceTest.java b/java/src/test/java/ai/onnxruntime/InferenceTest.java
index f6f9da1829..7fef2dc784 100644
--- a/java/src/test/java/ai/onnxruntime/InferenceTest.java
+++ b/java/src/test/java/ai/onnxruntime/InferenceTest.java
@@ -590,6 +590,12 @@ public class InferenceTest {
         Map<String, NodeInfo> infoMap = session.getInputInfo();
         TensorInfo aInfo = (TensorInfo) infoMap.get("A").getInfo();
         assertArrayEquals(new long[] {-1, 2}, aInfo.shape);
+        assertEquals(2, aInfo.dimensionNames.length);
+        assertEquals("n", aInfo.dimensionNames[0]);
+        assertEquals("", aInfo.dimensionNames[1]);
+        TensorInfo bInfo = (TensorInfo) infoMap.get("B").getInfo();
+        assertEquals(1, bInfo.dimensionNames.length);
+        assertEquals("m", bInfo.dimensionNames[0]);
       }
     }
     // Check that when the options are assigned it overrides the symbolic dimension