Improve documentation coverage for Herbert (#9428)

* first commit

* changed XLMTokenizer to HerbertTokenizer in code example

docs/source/index.rst

@@ -375,6 +375,7 @@ TensorFlow and/or Flax.
     model_doc/flaubert
     model_doc/fsmt
     model_doc/funnel
+    model_doc/herbert
     model_doc/layoutlm
     model_doc/led
     model_doc/longformer

docs/source/model_doc/herbert.rst

+.. 
+    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
+
+        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.
+
+herBERT
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The herBERT model was proposed in `KLEJ: Comprehensive Benchmark for Polish Language Understanding
+<https://www.aclweb.org/anthology/2020.acl-main.111.pdf>`__ by Piotr Rybak, Robert Mroczkowski, Janusz Tracz, and
+Ireneusz Gawlik. It is a BERT-based Language Model trained on Polish Corpora using only MLM objective with dynamic
+masking of whole words.
+
+The abstract from the paper is the following:
+
+*In recent years, a series of Transformer-based models unlocked major improvements in general natural language
+understanding (NLU) tasks. Such a fast pace of research would not be possible without general NLU benchmarks, which
+allow for a fair comparison of the proposed methods. However, such benchmarks are available only for a handful of
+languages. To alleviate this issue, we introduce a comprehensive multi-task benchmark for the Polish language
+understanding, accompanied by an online leaderboard. It consists of a diverse set of tasks, adopted from existing
+datasets for named entity recognition, question-answering, textual entailment, and others. We also introduce a new
+sentiment analysis task for the e-commerce domain, named Allegro Reviews (AR). To ensure a common evaluation scheme and
+promote models that generalize to different NLU tasks, the benchmark includes datasets from varying domains and
+applications. Additionally, we release HerBERT, a Transformer-based model trained specifically for the Polish language,
+which has the best average performance and obtains the best results for three out of nine tasks. Finally, we provide an
+extensive evaluation, including several standard baselines and recently proposed, multilingual Transformer-based
+models.*
+
+Examples of use:
+
+.. code-block::
+
+  from transformers import HerbertTokenizer, RobertaModel
+
+  tokenizer = HerbertTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
+  model = RobertaModel.from_pretrained("allegro/herbert-klej-cased-v1")
+
+  encoded_input = tokenizer.encode("Kto ma lepszą sztukę, ma lepszy rząd – to jasne.", return_tensors='pt')
+  outputs = model(encoded_input)
+
+  # HerBERT can also be loaded using AutoTokenizer and AutoModel:
+  import torch
+  from transformers import AutoModel, AutoTokenizer
+
+  tokenizer = AutoTokenizer.from_pretrained("allegro/herbert-klej-cased-tokenizer-v1")
+  model = AutoModel.from_pretrained("allegro/herbert-klej-cased-v1")
+
+
+The original code can be found `here <https://github.com/allegro/HerBERT>`__.
+
+HerbertTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.HerbertTokenizer
+    :members: 
+
+HerbertTokenizerFast
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.HerbertTokenizerFast
+    :members: 

utils/check_repo.py

@@ -402,9 +402,6 @@ SHOULD_HAVE_THEIR_OWN_PAGE = [
     "BertJapaneseTokenizer",
     "CharacterTokenizer",
     "MecabTokenizer",
-    # Herbert
-    "HerbertTokenizer",
-    "HerbertTokenizerFast",
     # Phoebus
     "PhobertTokenizer",
     # Benchmarks