Tensorflow LM examples (#12358)

* Tensorflow MLM example

* Add CLM example

* Style fixes, adding missing checkpoint code from the CLM example

* Fix TPU training, avoid massive dataset warnings

* Fix incorrect training length calculation for multi-GPU training

* Fix incorrect training length calculation for multi-GPU training

* Refactors and nitpicks from the review

* Style pass

* Adding README

examples/tensorflow/language-modeling/README.md

+<!---
+Copyright 2021 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.
+-->
+
+# Language modelling examples
+
+This folder contains some scripts showing examples of *language model pre-training* with the 🤗 Transformers library.
+For straightforward use-cases you may be able to use these scripts without modification, although we have also
+included comments in the code to indicate areas that you may need to adapt to your own projects. The two scripts
+have almost identical arguments, but they differ in the type of LM they train - a causal language model (like GPT) or a 
+masked language model (like BERT). Masked language models generally train more quickly and perform better when 
+fine-tuned on new tasks with a task-specific output head, like text classification. However, their ability to generate
+text is weaker than causal language models.
+
+## Pre-training versus fine-tuning
+
+These scripts can be used to both *pre-train* a language model completely from scratch, as well as to *fine-tune*
+a language model on text from your domain of interest. To start with an existing pre-trained language model you
+can use the `--model_name_or_path` argument, or to train from scratch you can use the `--model_type` argument
+to indicate the class of model architecture to initialize.
+
+### Multi-GPU and TPU usage
+
+By default, these scripts use a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
+can also be used by passing the name of the TPU resource with the `--tpu` argument.
+
+## run_mlm.py
+
+This script trains a masked language model.
+
+### Example command
+```
+python run_mlm.py \
+--model_name_or_path distilbert-base-cased \
+--output_dir output \
+--dataset_name wikitext \
+--dataset_config_name wikitext-103-raw-v1
+```
+
+## run_clm.py
+
+This script trains a causal language model.
+
+### Example command
+```
+python run_clm.py \
+--model_name_or_path distilgpt2 \
+--output_dir output \
+--dataset_name wikitext \
+--dataset_config_name wikitext-103-raw-v1
+```