Doc check: a bit of clean up (#11224)

docs/source/main_classes/data_collator.rst

-<!--- Copyright 2020 The HuggingFace Team. All rights reserved.
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
 
-     Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance
-     with the License. You may obtain a copy of the License at
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
 
         http://www.apache.org/licenses/LICENSE-2.0
 
-     Unless required by applicable law or agreed to in writing, software distributed under the License is distributed
-     on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for
-     the specific language governing permissions and limitations under the License.
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
 
--->
-
-DataCollator
+Data Collator
 -----------------------------------------------------------------------------------------------------------------------
 
-DataCollators are objects that will form a batch by using a list of elements as input. These lists of elements are of
+Data collators are objects that will form a batch by using a list of dataset elements as input. These elements are of
 the same type as the elements of :obj:`train_dataset` or :obj:`eval_dataset`.
 
-A data collator will default to :func:`transformers.data.data_collator.default_data_collator` if no `tokenizer` has
-been provided. This is a function that takes a list of samples from a Dataset as input and collates them into a batch
-of a dict-like object. The default collator performs special handling of potential keys:
-
-    - ``label``: handles a single value (int or float) per object
-    - ``label_ids``: handles a list of values per object
+To be able to build batches, data collators may apply some processing (like padding). Some of them (like
+:class:`~transformers.DataCollatorForLanguageModeling`) also apply some random data augmentation (like random masking)
+oin the formed batch.
 
-This function does not perform any preprocessing. An example of use can be found in glue and ner.
+Examples of use can be found in the :doc:`example scripts <../examples>` or :doc:`example notebooks <../notebooks>`.
 
 
 Default data collator
@@ -37,47 +33,39 @@ DataCollatorWithPadding
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.data.data_collator.DataCollatorWithPadding
-    :special-members: __call__
     :members:
 
+
 DataCollatorForTokenClassification
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.data.data_collator.DataCollatorForTokenClassification
-    :special-members: __call__
     :members:
 
+
 DataCollatorForSeq2Seq
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.data.data_collator.DataCollatorForSeq2Seq
-    :special-members: __call__
     :members:
 
+
 DataCollatorForLanguageModeling
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.data.data_collator.DataCollatorForLanguageModeling
-    :special-members: __call__
     :members: mask_tokens
 
+
 DataCollatorForWholeWordMask
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.data.data_collator.DataCollatorForWholeWordMask
-    :special-members: __call__
     :members: mask_tokens
 
-DataCollatorForSOP
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autoclass:: transformers.data.data_collator.DataCollatorForSOP
-    :special-members: __call__
-    :members: mask_tokens
 
 DataCollatorForPermutationLanguageModeling
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autoclass:: transformers.data.data_collator.DataCollatorForPermutationLanguageModeling
-    :special-members: __call__
     :members: mask_tokens

utils/check_repo.py

@@ -348,6 +348,8 @@ def find_all_documented_objects():
 DEPRECATED_OBJECTS = [
     "AutoModelWithLMHead",
     "BartPretrainedModel",
+    "DataCollator",
+    "DataCollatorForSOP",
     "GlueDataset",
     "GlueDataTrainingArguments",
     "LineByLineTextDataset",
@@ -385,7 +387,9 @@ DEPRECATED_OBJECTS = [
 UNDOCUMENTED_OBJECTS = [
     "AddedToken",  # This is a tokenizers class.
     "BasicTokenizer",  # Internal, should never have been in the main init.
+    "CharacterTokenizer",  # Internal, should never have been in the main init.
     "DPRPretrainedReader",  # Like an Encoder.
+    "MecabTokenizer",  # Internal, should never have been in the main init.
     "ModelCard",  # Internal type.
     "SqueezeBertModule",  # Internal building block (should have been called SqueezeBertLayer)
     "TFDPRPretrainedReader",  # Like an Encoder.
@@ -403,10 +407,6 @@ UNDOCUMENTED_OBJECTS = [
 
 # This list should be empty. Objects in it should get their own doc page.
 SHOULD_HAVE_THEIR_OWN_PAGE = [
-    # bert-japanese
-    "BertJapaneseTokenizer",
-    "CharacterTokenizer",
-    "MecabTokenizer",
     # Benchmarks
     "PyTorchBenchmark",
     "PyTorchBenchmarkArguments",
@@ -448,11 +448,6 @@ def ignore_undocumented(name):
     # MMBT model does not really work.
     if name.startswith("MMBT"):
         return True
-
-    # NOT DOCUMENTED BUT NOT ON PURPOSE, SHOULD BE FIXED!
-    # All data collators should be documented
-    if name.startswith("DataCollator") or name.endswith("data_collator"):
-        return True
     if name in SHOULD_HAVE_THEIR_OWN_PAGE:
         return True
     return False