Rephrasing the link. (#20253)

* Rephrasing the link.

* Removing `nested_simplify` within doctests.

* Fixup.

src/transformers/pipelines/audio_classification.py

@@ -77,16 +77,11 @@ class AudioClassificationPipeline(Pipeline):
     >>> from transformers import pipeline
 
     >>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks")
-    >>> result = classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
-
-    >>> # Simplify results, different torch versions might alter the scores slightly.
-    >>> from transformers.testing_utils import nested_simplify
-
-    >>> nested_simplify(result)
+    >>> classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
     [{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}]
     ```
 
-    [Using pipelines in a webserver or with a dataset](../pipeline_tutorial)
+    [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
 
 
     This pipeline can currently be loaded from [`pipeline`] using the following task identifier:

src/transformers/pipelines/automatic_speech_recognition.py

@@ -118,7 +118,7 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
     {'text': ' He hoped there would be stew for dinner, turnips and carrots and bruised potatoes and fat mutton pieces to be ladled out in thick, peppered flour fat and sauce.'}
     ```
 
-    [Using pipelines in a webserver or with a dataset](../pipeline_tutorial)
+    [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
 
     Arguments:
         model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):

src/transformers/pipelines/conversational.py

@@ -181,7 +181,7 @@ class ConversationalPipeline(Pipeline):
     "It's a comedy."
     ```
 
-    [Using pipelines in a webserver or with a dataset](../pipeline_tutorial)
+    [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
 
     This conversational pipeline can currently be loaded from [`pipeline`] using the following task identifier:
     `"conversational"`.

src/transformers/pipelines/depth_estimation.py

@@ -36,7 +36,7 @@ class DepthEstimationPipeline(Pipeline):
     torch.Size([1, 384, 384])
     ```
 
-    [Using pipelines in a webserver or with a dataset](../pipeline_tutorial)
+    [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
 
 
     This depth estimation pipeline can currently be loaded from [`pipeline`] using the following task identifier:

src/transformers/pipelines/document_question_answering.py

@@ -120,7 +120,7 @@ class DocumentQuestionAnsweringPipeline(ChunkPipeline):
     '1110212019'
     ```
 
-    [Using pipelines in a webserver or with a dataset](../pipeline_tutorial)
+    [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
 
     This document question answering pipeline can currently be loaded from [`pipeline`] using the following task
     identifier: `"document-question-answering"`.

src/transformers/pipelines/feature_extraction.py

@@ -20,7 +20,7 @@ class FeatureExtractionPipeline(Pipeline):
     torch.Size([1, 8, 768])
     ```
 
-    [Using pipelines in a webserver or with a dataset](../pipeline_tutorial)
+    [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
 
     This feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier:
     `"feature-extraction"`.

src/transformers/pipelines/fill_mask.py

@@ -42,15 +42,11 @@ class FillMaskPipeline(Pipeline):
     >>> from transformers import pipeline
 
     >>> fill_masker = pipeline(model="bert-base-uncased")
-    >>> potential_words = fill_masker("This is a simple [MASK].")
-
-    >>> from transformers.testing_utils import nested_simplify
-
-    >>> nested_simplify(potential_words)  # The scores might vary slightly across pytorch/tensorflow versions.
+    >>> fill_masker("This is a simple [MASK].")
     [{'score': 0.042, 'token': 3291, 'token_str': 'problem', 'sequence': 'this is a simple problem.'}, {'score': 0.031, 'token': 3160, 'token_str': 'question', 'sequence': 'this is a simple question.'}, {'score': 0.03, 'token': 8522, 'token_str': 'equation', 'sequence': 'this is a simple equation.'}, {'score': 0.027, 'token': 2028, 'token_str': 'one', 'sequence': 'this is a simple one.'}, {'score': 0.024, 'token': 3627, 'token_str': 'rule', 'sequence': 'this is a simple rule.'}]
     ```
 
-    [Using pipelines in a webserver or with a dataset](../pipeline_tutorial)
+    [Learn more about the basics of using a pipeline in the [pipeline tutorial]](../pipeline_tutorial)
 
     This mask filling pipeline can currently be loaded from [`pipeline`] using the following task identifier:
     `"fill-mask"`.