Fixing the doctests failures. (#20294)

* Fixing the doctests failures.

* Fixup.

src/transformers/pipelines/__init__.py

@@ -590,15 +590,17 @@ def pipeline(
     >>> from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
 
     >>> # Sentiment analysis pipeline
-    >>> pipeline("sentiment-analysis")
+    >>> analyzer = pipeline("sentiment-analysis")
 
     >>> # Question answering pipeline, specifying the checkpoint identifier
-    >>> pipeline("question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="bert-base-cased")
+    >>> oracle = pipeline(
+    ...     "question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="bert-base-cased"
+    ... )
 
     >>> # Named entity recognition pipeline, passing in a specific model and tokenizer
     >>> model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-large-cased-finetuned-conll03-english")
     >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-    >>> pipeline("ner", model=model, tokenizer=tokenizer)
+    >>> recognizer = pipeline("ner", model=model, tokenizer=tokenizer)
     ```"""
     if model_kwargs is None:
         model_kwargs = {}

src/transformers/pipelines/document_question_answering.py

@@ -112,12 +112,11 @@ class DocumentQuestionAnsweringPipeline(ChunkPipeline):
     >>> from transformers import pipeline
 
     >>> document_qa = pipeline(model="impira/layoutlm-document-qa")
-    >>> result = document_qa(
+    >>> document_qa(
     ...     image="https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png",
     ...     question="What is the invoice number?",
     ... )
-    >>> result[0]["answer"]
-    '1110212019'
+    [{'score': 0.425, 'answer': 'us-001', 'start': 16, 'end': 16}]
     ```
 
     [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)

src/transformers/pipelines/token_classification.py

@@ -95,7 +95,8 @@ class TokenClassificationPipeline(Pipeline):
 
     >>> token_classifier = pipeline(model="Jean-Baptiste/camembert-ner", aggregation_strategy="simple")
     >>> sentence = "Je m'appelle jean-baptiste et je vis à montréal"
-    >>> token_classifier(sentence)
+    >>> tokens = token_classifier(sentence)
+    >>> tokens
     [{'entity_group': 'PER', 'score': 0.9931, 'word': 'jean-baptiste', 'start': 12, 'end': 26}, {'entity_group': 'LOC', 'score': 0.998, 'word': 'montréal', 'start': 38, 'end': 47}]
 
     >>> token = tokens[0]

src/transformers/pipelines/zero_shot_classification.py

@@ -61,7 +61,7 @@ class ZeroShotClassificationPipeline(ChunkPipeline):
     >>> from transformers import pipeline
 
     >>> oracle = pipeline(model="facebook/bart-large-mnli")
-    >>> answers = oracle(
+    >>> oracle(
     ...     "I have a problem with my iphone that needs to be resolved asap!!",
     ...     candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
     ... )