diff --git a/onnxscript/ir/serde.py b/onnxscript/ir/serde.py
index 0e611562a..b9c04d711 100644
--- a/onnxscript/ir/serde.py
+++ b/onnxscript/ir/serde.py
@@ -132,6 +132,12 @@ def __array__(self, dtype: Any = None) -> np.ndarray:
     def numpy(self) -> np.ndarray:
         """Return the tensor as a numpy array.
 
+        This is an improved version of onnx.numpy_helper.to_array.
+        It first reads the data using the dtype corresponding to the tensor
+        proto data field, then converts it to the correct dtype and shape.
+        Special cases are bfloat16, complex and int4 where we need to
+        reinterpret the data. Other types can simply be casted.
+
         When the data type is not supported by numpy, the value is the bit representation
         of the dtype:
 
@@ -139,6 +145,16 @@ def numpy(self) -> np.ndarray:
         - ``uint8`` for uint4.
         - ``uint8`` for 8-bit data types like float8.
         - ``uint16`` for bfloat16.
+
+        When the data type is a string, this method returns a numpy array
+        of bytes instead of a numpy array of strings, to follow the ONNX
+        specification.
+
+        External tensors are not supported by this class. Use
+        :class:`onnxscript.ir.ExternalTensor` instead.
+
+        Raises:
+            ValueError: If the data type is UNDEFINED.
         """
         # This is an improved version of onnx.numpy_helper.to_array.
         # It first reads the data using the dtype corresponding to the tensor
@@ -146,9 +162,14 @@ def numpy(self) -> np.ndarray:
         # Special cases are bfloat16, complex and int4 where we need to
         # reinterpret the data. Other types can simply be casted.
         dtype = self.dtype
+        if dtype == _enums.DataType.UNDEFINED:
+            raise ValueError("Cannot convert UNDEFINED tensor to numpy array.")
+
         if self._proto.HasField("raw_data"):
             array = np.frombuffer(self._proto.raw_data, dtype=dtype.numpy().newbyteorder("<"))
             # Cannot return now, because we may need to unpack 4bit tensors
+        if dtype == _enums.DataType.STRING:
+            return np.array(self._proto.string_data).reshape(self._proto.dims)
         elif self._proto.int32_data:
             array = np.array(self._proto.int32_data, dtype=_little_endian_dtype(np.int32))
             if dtype == _enums.DataType.FLOAT16:
@@ -180,7 +201,19 @@ def numpy(self) -> np.ndarray:
             return array.astype(dtype.numpy()).reshape(self._proto.dims)
 
     def tobytes(self) -> bytes:
-        """Return the tensor as a byte string conformed to the ONNX specification, in little endian."""
+        """Return the tensor as a byte string conformed to the ONNX specification, in little endian.
+
+        Raises:
+            ValueError: If the tensor is a string tensor or an external tensor.
+            ValueError: If the tensor is of UNDEFINED data type.
+        """
+        if self._proto.data_location == onnx.TensorProto.EXTERNAL:
+            raise ValueError("Cannot convert external tensor to bytes.")
+        if self.dtype == _enums.DataType.STRING:
+            raise ValueError("Cannot convert string tensor to bytes.")
+        if self.dtype == _enums.DataType.UNDEFINED:
+            raise ValueError("Cannot convert UNDEFINED tensor to bytes.")
+
         if self._proto.HasField("raw_data"):
             return self._proto.raw_data
         if self._proto.float_data: