Internal change

PiperOrigin-RevId: 470722284

official/nlp/README.md

 # TF-NLP Model Garden
 
 ⚠️ Disclaimer: All datasets hyperlinked from this page are not owned or
-distributed by Google. The dataset is made available by third parties.
-Please review the terms and conditions made available by the third parties
-before using the data.
+distributed by Google. The dataset is made available by third parties. Please
+review the terms and conditions made available by the third parties before using
+the data.
 
-This codebase provides a Natrual Language Processing modeling toolkit written in
+This codebase provides a Natural Language Processing modeling toolkit written in
 [TF2](https://www.tensorflow.org/guide/effective_tf2). It allows researchers and
 developers to reproduce state-of-the-art model results and train custom models
 to experiment new research ideas.
 
 ## Features
 
-* Reusable and modularized modeling building blocks
-* State-of-the-art reproducible
-* Easy to customize and extend
-* End-to-end training
-* Distributed trainable on both GPUs and TPUs
+*   Reusable and modularized modeling building blocks
+*   State-of-the-art reproducible
+*   Easy to customize and extend
+*   End-to-end training
+*   Distributed trainable on both GPUs and TPUs
 
 ## Major components
 
 ### Libraries
 
 We provide modeling library to allow users to train custom models for new
-research ideas. Detailed intructions can be found in READMEs in each folder.
+research ideas. Detailed instructions can be found in READMEs in each folder.
 
 *   [modeling/](modeling): modeling library that provides building blocks
     (e.g.,Layers, Networks, and Models) that can be assembled into
-    transformer-based achitectures .
+    transformer-based architectures.
 *   [data/](data): binaries and utils for input preprocessing, tokenization,
     etc.
 
@@ -35,14 +35,14 @@ research ideas. Detailed intructions can be found in READMEs in each folder.
 
 We provide SoTA model implementations, pre-trained models, training and
 evaluation examples, and command lines. Detail instructions can be found in the
-READMEs for specific papers. Below are some papers implemented in the
-repository and more NLP projects can be found in the
+READMEs for specific papers. Below are some papers implemented in the repository
+and more NLP projects can be found in the
 [`projects`](https://github.com/tensorflow/models/tree/master/official/projects)
 folder:
 
-1.  [BERT](MODEL_GARDEN.md#available-model-configs): [BERT: Pre-training of Deep Bidirectional Transformers for
-    Language Understanding](https://arxiv.org/abs/1810.04805) by Devlin et al.,
-    2018
+1.  [BERT](MODEL_GARDEN.md#available-model-configs): [BERT: Pre-training of Deep
+    Bidirectional Transformers for Language
+    Understanding](https://arxiv.org/abs/1810.04805) by Devlin et al., 2018
 2.  [ALBERT](MODEL_GARDEN.md#available-model-configs):
     [A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942)
     by Lan et al., 2019
@@ -56,9 +56,8 @@ folder:
 ### Common Training Driver
 
 We provide a single common driver [train.py](train.py) to train above SoTA
-models on popluar tasks. Please see [docs/train.md](docs/train.md) for
-more details.
-
+models on popular tasks. Please see [docs/train.md](docs/train.md) for more
+details.
 
 ### Pre-trained models with checkpoints and TF-Hub