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diff --git a/snips_inference_agl/intent_classifier/intent_classifier.py b/snips_inference_agl/intent_classifier/intent_classifier.py
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+++ b/snips_inference_agl/intent_classifier/intent_classifier.py
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+from abc import ABCMeta
+
+from future.utils import with_metaclass
+
+from snips_inference_agl.pipeline.processing_unit import ProcessingUnit
+from snips_inference_agl.common.abc_utils import classproperty
+
+
+class IntentClassifier(with_metaclass(ABCMeta, ProcessingUnit)):
+    """Abstraction which performs intent classification
+
+    A custom intent classifier must inherit this class to be used in a
+    :class:`.ProbabilisticIntentParser`
+    """
+
+    @classproperty
+    def unit_name(cls):  # pylint:disable=no-self-argument
+        return IntentClassifier.registered_name(cls)
+
+    # @abstractmethod
+    def get_intent(self, text, intents_filter):
+        """Performs intent classification on the provided *text*
+
+        Args:
+            text (str): Input
+            intents_filter (str or list of str): When defined, it will find
+                the most likely intent among the list, otherwise it will use
+                the whole list of intents defined in the dataset
+
+        Returns:
+            dict or None: The most likely intent along with its probability or
+            *None* if no intent was found. See
+            :func:`.intent_classification_result` for the output format.
+        """
+        pass
+
+    # @abstractmethod
+    def get_intents(self, text):
+        """Performs intent classification on the provided *text* and returns
+        the list of intents ordered by decreasing probability
+
+        The length of the returned list is exactly the number of intents in the
+        dataset + 1 for the None intent
+
+        .. note::
+
+            The probabilities returned along with each intent are not
+            guaranteed to sum to 1.0. They should be considered as scores
+            between 0 and 1.
+        """
+        pass