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PiperOrigin-RevId: 438209905

official/nlp/data/README.md

 This directory contains binaries and utils required for input preprocessing,
 tokenization, etc that can be used with model building blocks available in
-NLP modeling library [nlp/modelling]
-(https://github.com/tensorflow/models/tree/master/official/nlp/modeling) to
-train custom models and validate new research ideas.
+NLP modeling library [nlp/modelling](https://github.com/tensorflow/models/tree/master/official/nlp/modeling)
+to train custom models and validate new research ideas.

official/nlp/docs/README.md

+This directory contain guides to help users to train NLP models.
+
+1. [Training guide](train.md) explain the steps to follow for training NLP
+models on GPU and TPU.
+
+2. [Pretrained_models guide](pretrained_models.md) explain how to load
+pre-trained NLP models (baselines and checkpoints) that can be finetuned
+further depending on application.
+
+3. [TF-Hub guide](tfhub.md) explain how to use TF-NLP's
+[export_tfhub](https://github.com/tensorflow/models/blob/master/official/nlp/tools/export_tfhub.py)
+tool to export pre-trained Transformer encoders to SavedModels format that are
+suitable for publication on TF Hub.

official/nlp/docs/train.md

 # Model Garden NLP Common Training Driver
 
-[train.py](https://github.com/tensorflow/models/blob/master/official/nlp/train.py) is the common training driver that supports multiple
+[train.py](https://github.com/tensorflow/models/blob/master/official/nlp/train.py)
+is the common training driver that supports multiple
 NLP tasks (e.g., pre-training, GLUE and SQuAD fine-tuning etc) and multiple
 models (e.g., BERT, ALBERT, MobileBERT etc).
 
 ## Experiment Configuration
 
-[train.py] is driven by configs defined by the [ExperimentConfig](https://github.com/tensorflow/models/blob/master/official/core/config_definitions.py)
+[train.py](https://github.com/tensorflow/models/blob/master/official/nlp/train.py)
+is driven by configs defined by the [ExperimentConfig](https://github.com/tensorflow/models/blob/master/official/core/config_definitions.py)
 including configurations for `task`, `trainer` and `runtime`. The pre-defined
 NLP related [ExperimentConfig](https://github.com/tensorflow/models/blob/master/official/core/config_definitions.py) can be found in
 [configs/experiment_configs.py](https://github.com/tensorflow/models/blob/master/official/nlp/configs/experiment_configs.py).
@@ -78,7 +80,9 @@ setting `task.validation_data.input_path` in `PARAMS`.
 
 ## Run on Cloud TPUs
 
-Next, we will describe how to run the [train.py](https://github.com/tensorflow/models/blob/master/official/nlp/train.py) on Cloud TPUs.
+Next, we will describe how to run
+the [train.py](https://github.com/tensorflow/models/blob/master/official/nlp/train.py)
+on Cloud TPUs.
 
 ### Setup
 First, you need to create a `tf-nightly` TPU with
@@ -99,6 +103,8 @@ pip3 install --user -r official/requirements.txt
 
 ### Fine-tuning Sentence Classification with BERT from TF-Hub
 
+<details>
+
 This example fine-tunes BERT-base from TF-Hub on the the Multi-Genre Natural
 Language Inference (MultiNLI) corpus using TPUs.
 
@@ -163,8 +169,12 @@ python3 train.py \
 You can monitor the training progress in the console and find the output
 models in `$OUTPUT_DIR`.
 
+</details>
+
 ### Fine-tuning SQuAD with a pre-trained BERT checkpoint
 
+<details>
+
 This example fine-tunes a pre-trained BERT checkpoint on the
 Stanford Question Answering Dataset (SQuAD) using TPUs.
 The [SQuAD website](https://rajpurkar.github.io/SQuAD-explorer/) contains
@@ -219,4 +229,6 @@ python3 train.py \
 
 ```
 
+</details>
+
 Note: More examples about pre-training will come soon.

official/nlp/modeling/README.md

@@ -20,8 +20,7 @@ examples.
 * [`losses`](losses) contains common loss computation used in NLP tasks.
 
 Please see the colab
-[nlp_modeling_library_intro.ipynb]
-(https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb)
+[nlp_modeling_library_intro.ipynb](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/colab/nlp/nlp_modeling_library_intro.ipynb)
 for how to build transformer-based NLP models using above primitives.
 
 Besides the pre-defined primitives, it also provides scaffold classes to allow
@@ -44,8 +43,7 @@ custom hidden layer (which will replace the Transformer instantiation in the
 encoder).
 
 Please see the colab
-[customize_encoder.ipynb]
-(https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb)
+[customize_encoder.ipynb](https://colab.sandbox.google.com/github/tensorflow/models/blob/master/official/colab/nlp/customize_encoder.ipynb)
 for how to use scaffold classes to build noval achitectures.
 
 BERT and ALBERT models in this repo are implemented using this library.