fix zero shot pipeline docs (#6245)

docs/source/main_classes/pipelines.rst

@@ -20,6 +20,7 @@ There are two categories of pipeline abstractions to be aware about:
     - :class:`~transformers.TextGenerationPipeline`
     - :class:`~transformers.TokenClassificationPipeline`
     - :class:`~transformers.TranslationPipeline`
+    - :class:`~transformers.ZeroShotClassificationPipeline`
 
 The pipeline abstraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -97,6 +98,13 @@ TokenClassificationPipeline
     :special-members: __call__
     :members:
 
+ZeroShotClassificationPipeline
+==========================================
+
+.. autoclass:: transformers.ZeroShotClassificationPipeline
+    :special-members: __call__
+    :members:
+
 
 Parent class: :obj:`Pipeline`
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

src/transformers/__init__.py

@@ -120,6 +120,7 @@ from .pipelines import (
     TextGenerationPipeline,
     TokenClassificationPipeline,
     TranslationPipeline,
+    ZeroShotClassificationPipeline,
     pipeline,
 )
 

src/transformers/pipelines.py

@@ -1033,30 +1033,31 @@ class ZeroShotClassificationPipeline(Pipeline):
         Classify the sequence(s) given as inputs.
 
         Args:
-            sequences (:obj:`str` or obj:`List[str]`):
+            sequences (:obj:`str` or :obj:`List[str]`):
                 The sequence(s) to classify, will be truncated if the model input is too large.
-            candidate_labels (:obj:`str` or obj:`List[str]`):
+            candidate_labels (:obj:`str` or :obj:`List[str]`):
                 The set of possible class labels to classify each sequence into. Can be a single label, a string of
                 comma-separated labels, or a list of labels.
-            hypothesis_template (obj:`str`, `optional`, defaults to :obj:`"This example is {}."`):
+            hypothesis_template (:obj:`str`, `optional`, defaults to :obj:`"This example is {}."`):
                 The template used to turn each label into an NLI-style hypothesis. This template must include a {}
                 or similar syntax for the candidate label to be inserted into the template. For example, the default
                 template is :obj:`"This example is {}."` With the candidate label :obj:`"sports"`, this would be fed
                 into the model like :obj:`"<cls> sequence to classify <sep> This example is sports . <sep>"`. The
                 default template works well in many cases, but it may be worthwhile to experiment with different
                 templates depending on the task setting.
-            multi_class (obj:`bool`, `optional`, defaults to :obj:`False`):
+            multi_class (:obj:`bool`, `optional`, defaults to :obj:`False`):
                 Whether or not multiple candidate labels can be true. If :obj:`False`, the scores are normalized
                 such that the sum of the label likelihoods for each sequence is 1. If :obj:`True`, the labels are
                 considered independent and probabilities are normalized for each candidate by doing a softmax of
                 the entailment score vs. the contradiction score.
+
         Return:
             A :obj:`dict` or a list of :obj:`dict`: Each result comes as a dictionary with the
             following keys:
 
             - **sequence** (:obj:`str`) -- The sequence for which this is the output.
             - **labels** (:obj:`List[str]`) -- The labels sorted by order of likelihood.
-            - **scores** (:obj:` List[float]`) -- The probabilities for each of the labels.
+            - **scores** (:obj:`List[float]`) -- The probabilities for each of the labels.
         """
         outputs = super().__call__(sequences, candidate_labels, hypothesis_template)
         num_sequences = 1 if isinstance(sequences, str) else len(sequences)