Move files around; edits

CONTRIBUTING.md

@@ -6,8 +6,8 @@ development.
 
 ## Background reading
 
-Before contributing code, please review our [Style Guide](CODE_STYLE.md) and
-[API Design Guide](API_DESIGN.md).
+Before contributing code, please review our [Style Guide](STYLE_GUIDE.md) and
+[API Design Guide](API_DESIGN_GUIDE.md).
 
 Our [Roadmap](ROADMAP.md) contains an overview of the project goals and our
 current focus areas.

README.md

@@ -26,8 +26,8 @@ We are a new and growing project, and welcome [contributions](CONTRIBUTING.md).
 - [Documentation and Guides](https://keras.io/keras_nlp)
 - [Contributing](CONTRIBUTING.md)
 - [Roadmap](ROADMAP.md)
-- [Style Guide](CODE_STYLE.md)
-- [API Design Guide](API_DESIGN.md)
+- [Style Guide](STYLE_GUIDE.md)
+- [API Design Guide](API_DESIGN_GUIDE.md)
 - [Call for Contributions](https://github.com/keras-team/keras-nlp/issues?q=is%3Aissue+is%3Aopen+label%3A%22contributions+welcome%22)
 
 ## Quick Start

ROADMAP.md

@@ -45,11 +45,11 @@
   core Tensorflow.
 
 - **KerasNLP is not a Transformer only library.**
-  Transformer based models are a key offering for KerasNLP, and these
-  architectures should be easy to train and use within the library. However, we
-  want to support other types of models, such as n-gram or word2vec approaches
-  that might be more suited to some real-world tasks (e.g. low-resource
-  deployments).
+  Transformer based models are a key offering for KerasNLP, and they should be
+  easy to train and use within the library. However, we need to support other
+  types of models, such as n-gram or word2vec approaches that might run more
+  easily on limited hardware. We will always want the most practical tool for
+  the task, regardless of architecture.
 
 ## Focus areas for 2022
 
@@ -110,7 +110,7 @@ performance as reported in publications.
 
 ### Tools for data preprocessing and postprocessing for end-to-end workflows
 
-It should be easy to take trained Keras language model and use it for a wide
+It should be easy to take a trained Keras language model and use it for a wide
 range of real world NLP tasks. We should support classification, text
 generation, summarization, translation, name-entity recognition, and question
 answering. We should have a guide for each of these tasks using KerasNLP by
@@ -121,7 +121,7 @@ end-to-end workflows for each of these tasks.
 
 Currently projects in this area include:
 
-- Utilties for generating sequences of text using greedy or beam search.
+- Utilities for generating sequences of text using greedy or beam search.
 - Metrics for evaluating the quality of generated sequences, such a ROUGE and
   BLEU.
 - Data augmentation preprocessing layers for domains with limited data. These

CODE_STYLE.md → STYLE_GUIDE.md

@@ -21,13 +21,13 @@ from tensorflow import keras
 ```
 
 ❌ `tf.keras.activations.X`<br/>
-✅ `keras.activations.X`
+✔️ `keras.activations.X`
 
 ❌ `layers.X`<br/>
-✅ `keras.layers.X` or `keras_nlp.layers.X`
+✔️ `keras.layers.X` or `keras_nlp.layers.X`
 
 ❌ `Dense(1, activation='softmax')`<br/>
-✅ `keras.layers.Dense(1, activation='softmax')`
+✔️ `keras.layers.Dense(1, activation='softmax')`
 
 For KerasNLP library code, `keras_nlp` will not be directly imported, but
 `keras` should still be as a top-level object used to access library symbols.
@@ -66,7 +66,7 @@ class Linear(keras.layers.Layer):
     model = keras.Model(inputs, outputs)
     ```
 
-    Call the layer on direct input.
+    Call the layer directly on input.
     >>> layer = keras_nlp.layers.Linear(4)
     >>> layer(tf.zeros(8, 2)) == layer.b
     True