diff --git a/src/transformers/pipelines/zero_shot_classification.py b/src/transformers/pipelines/zero_shot_classification.py
index f98c87166ca0..12653ba6e039 100644
--- a/src/transformers/pipelines/zero_shot_classification.py
+++ b/src/transformers/pipelines/zero_shot_classification.py
@@ -46,13 +46,36 @@ def __call__(self, sequences, labels, hypothesis_template):
 class ZeroShotClassificationPipeline(ChunkPipeline):
     """
     NLI-based zero-shot classification pipeline using a `ModelForSequenceClassification` trained on NLI (natural
-    language inference) tasks.
+    language inference) tasks. Equivalent of `text-classification` pipelines, but these models don't require a
+    hardcoded number of potential classes, they can be chosen at runtime. It usually means it's slower but it is
+    **much** more flexible.
 
     Any combination of sequences and labels can be passed and each combination will be posed as a premise/hypothesis
     pair and passed to the pretrained model. Then, the logit for *entailment* is taken as the logit for the candidate
     label being valid. Any NLI model can be used, but the id of the *entailment* label must be included in the model
     config's :attr:*~transformers.PretrainedConfig.label2id*.
 
+    Example:
+
+    ```python
+    >>> from transformers import pipeline
+
+    >>> oracle = pipeline(model="facebook/bart-large-mnli")
+    >>> answers = oracle(
+    ...     "I have a problem with my iphone that needs to be resolved asap!!",
+    ...     candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
+    ... )
+    {'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['urgent', 'phone', 'computer', 'not urgent', 'tablet'], 'scores': [0.504, 0.479, 0.013, 0.003, 0.002]}
+
+    >>> oracle(
+    ...     "I have a problem with my iphone that needs to be resolved asap!!",
+    ...     candidate_labels=["english", "german"],
+    ... )
+    {'sequence': 'I have a problem with my iphone that needs to be resolved asap!!', 'labels': ['english', 'german'], 'scores': [0.814, 0.186]}
+    ```
+
+    [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
+
     This NLI pipeline can currently be loaded from [`pipeline`] using the following task identifier:
     `"zero-shot-classification"`.