Misc. typos and suggested edits.

README.md

@@ -17,7 +17,7 @@ As the variety of use cases grows, so does the need to support:
 
 - New modalities of data (code, language, speech, visual) 
 - New ways of transforming the data to optimize the performance of the resulting LLMs for each specific use case.
-- Large variety in the scale of data to be processed, from laptop-scale to datacenter-scale
+- A large variety in the scale of data to be processed, from laptop-scale to datacenter-scale
 
 Data Prep Kit offers implementations of commonly needed data transformations, called *modules*, for both Code and Language modalities.
 The goal is to offer high-level APIs for developers to quickly get started in working with their data, without needing expertise in the underlying runtimes and frameworks.
@@ -32,7 +32,7 @@ The goal is to offer high-level APIs for developers to quickly get started in wo
 ## &#x1F4D6; About <a name = "about"></a>
 Data Prep Kit is a toolkit for streamlining data preparation for developers looking to build LLM-enabled applications via fine-tuning or instruction-tuning.
 Data Prep Kit contributes a set of modules that the developer can get started with to easily build data pipelines suitable for their use case.
-These modules have been tested in producing pre-training datasets for the [Granite](https://huggingface.co/instructlab/granite-7b-lab) open models. 
+These modules have been tested while producing pre-training datasets for the Granite open models, [here](https://huggingface.co/instructlab/granite-7b-lab) and [here](https://huggingface.co/ibm-granite). 
 
 The modules are built on common frameworks (for Spark and Ray), called the *data processing library* that allows the developers to build new custom modules that readily scale across a variety of runtimes.
 Eventually, Data Prep Kit will offer consistent APIs and configurations across the following underlying runtimes.
@@ -62,69 +62,71 @@ Contributors are welcome to add new modules as well as add runtime support for e
 
 Features of the toolkit: 
 
-- Aiming to accelerate unstructured data prep burden for the "long tail" of LLM use cases
-- Growing set of module implementations across multiple runtimes and targeting laptop-scale to datacenter-scale processing
-- A growing set of sample pipelines developed for real enterprise use cases
-- [Data processing library](data-processing-lib) to enable contribution of new custom modules targeting new use cases
-- [Kube Flow Pipelines](https://www.kubeflow.org/docs/components/pipelines/v1/introduction/)-based [workflow automation](kfp/doc/simple_transform_pipeline.md) for no-code data prep
+- It aims to accelerate unstructured data prep for the "long tail" of LLM use cases.
+- It offers a growing set of module implementations across multiple runtimes, targeting laptop-scale to datacenter-scale processing.
+- It provides a growing set of sample pipelines developed for real enterprise use cases.
+- It provides the [Data processing library](data-processing-lib) to enable contribution of new custom modules targeting new use cases.
+- It uses [Kube Flow Pipelines](https://www.kubeflow.org/docs/components/pipelines/v1/introduction/)-based [workflow automation](kfp/doc/simple_transform_pipeline.md) for no-code data prep.
 
 Data modalities supported: 
 
-* Code - support for code datasets as downloaded .zip files of GitHub repositories converted to .
+* Code - support for code datasets as downloaded .zip files of GitHub repositories converted to
 [parquet](https://arrow.apache.org/docs/python/parquet.html) files. 
-* Language - Future releases will provide transforms specific to natural language, and like code transforms will operate on parquet files.
+* Language - Future releases will provide transforms specific to natural language, and like the code transformations, will operate on parquet files.
 
 Support for additional data modalities is expected in the future.
 
-### Data Processing Library: 
-Python-based library that has ready-to-use transforms that can be supported across a variety of runtimes.
-We use the popular point [parquet](https://arrow.apache.org/docs/python/parquet.html) format to store the data (code or language). 
+### Data Processing Library 
+A Python-based library that has ready-to-use transforms that can be supported across a variety of runtimes.
+We use the popular [parquet](https://arrow.apache.org/docs/python/parquet.html) format to store the data (code or language). 
 Every parquet file follows a set 
 [schema](tools/ingest2parquet/).
 Data is converted from raw form (e.g., zip files for GitHub repositories) to parquet files by the
 [ingest2parquet](tools/ingest2parquet/) 
 tool that also adds the necessary fields in the schema.  
-A user can use one or more of the [available transforms](transforms) to process their data. 
+A user can then use one or more of the [available transforms](transforms) to process their data. 
 
-#### Transform design: 
+#### Transform Design
 A transform can follow one of the two patterns: annotator or filter.
 
-- **Annotator** An annotator transform adds information during the processing by adding one more column to the parquet file.
+- **Annotator** An annotator transform adds information during the processing by adding one more columns to the parquet files.
 The annotator design also allows a user to verify the results of the processing before the actual filtering of the data.
 
 - **Filter** A filter transform processes the data and outputs the transformed data, e.g., exact deduplication.
-A general purpose [SQL-based filter transform](transforms/universal/filter) enables a powerful mechanism for identifying  columns and rows of interest for downstream processing.
+A general purpose [SQL-based filter transform](transforms/universal/filter) enables a powerful mechanism for identifying columns and rows of interest for downstream processing.
 For a new module to be added, a user can pick the right design based on the processing to be applied. More details [here](transforms). 
 
-#### Scaling of transforms: 
-To enable processing large volumes of data leveraging multi-mode clusters, [Ray](https://docs.ray.io/en/latest/index.html) and [Spark](https://spark.apache.org) wrappers are provided, to readily scale out the Python implementations.
+#### Scaling of Transforms
+To enable processing of large data volumes leveraging multi-mode clusters, [Ray](https://docs.ray.io/en/latest/index.html) and [Spark](https://spark.apache.org) wrappers are provided, to readily scale out the Python implementations.
 A generalized workflow is shown [here](doc/data-processing.md).
 
-#### Bring Your Own Transform: 
-One can add new transforms by bringing in their own Python-based processing logic and using the Data Processing Library to build and contribute transforms.
+#### Bring Your Own Transform 
+One can add new transforms by bringing in Python-based processing logic and using the Data Processing Library to build and contribute transforms.
 More details on the data processing library are [here](data-processing-lib/doc/overview.md). 
 
-#### Automation: 
+#### Automation
 The toolkit also supports transform execution automation based on 
-[Kubeflow pipelines](https://www.kubeflow.org/docs/components/pipelines/v1/introduction/)(KFP) and
-tested on [Kind cluster](https://kind.sigs.k8s.io/). KFP implementation is based on the [KubeRay Operator](https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html)
+[Kubeflow pipelines](https://www.kubeflow.org/docs/components/pipelines/v1/introduction/) (KFP),
+tested on [Kind clusters](https://kind.sigs.k8s.io/). The KFP implementation is based on the [KubeRay Operator](https://docs.ray.io/en/master/cluster/kubernetes/getting-started.html)
 for creating and managing the Ray cluster and [KubeRay API server](https://github.com/ray-project/kuberay/tree/master/apiserver)
 to interact with the KubeRay operator. An additional [framework](kfp/kfp_support_lib) along with several
-[kfp components](kfp/kfp_ray_components) is used to simplify pipeline implementation.
+[kfp components](kfp/kfp_ray_components) is used to simplify the pipeline implementation.
 
 
 ## &#x2699; Setup <a name = "setup"></a>
 
-We tried the project on different hardware/software configurations(see [Apple/Mac considerations](doc/mac.md).)
-We recommend using a laptop with at least 16GB of memory and 8 CPUs for development without KFP, 
-and at least 32GB and preferably 16 CPUs if you plan to run KFP on Kind.
+We tried the project on different hardware/software configurations (see [Apple/Mac considerations](doc/mac.md).)
+We recommend using a laptop with at least 16GB of memory and 8 CPU cores for development without KFP, 
+and at least 32GB and preferably 16 CPU cores if you plan to run KFP on Kind.
 
 ### Prerequisites
 
-Python 3.10 or 3.11 
-[pre-commit](https://pre-commit.com/)
-twine 
-Docker/Podman
+* Python 3.10 or 3.11 
+* Docker/Podman
+
+Two important tools will also be installed using the steps below:
+* [pre-commit](https://pre-commit.com/)
+* [twine](https://twine.readthedocs.io/en/stable/) 
 
 ### Installation Steps
 
@@ -140,49 +142,49 @@ Refer to [Minio install instructions](data-processing-lib/doc/using_s3_transform
 
 ## &#x1F680; Getting Started <a name = "getting_started"></a>
 
-There are various entry points that one can choose based on their use case. Below are a few demos to get you started. 
+There are various entry points that you can choose based on the use case. Below are a few demos to get you started. 
 
-### Run a single transform on local-ray
-Get started by running the noop transform that performs an identity operation by following the 
+### Run a Single Transform on Local Ray
+Get started by running the "noop" transform that performs an identity operation by following the 
 [tutorial](data-processing-lib/doc/simplest-transform-tutorial.md) and associated 
 [noop implementation](transforms/universal/noop). 
 
-### Run a data pipeline on local-ray
+### Run a Data Pipeline on Local Ray
 Get started by building a data pipeline with our example pipeline (link to be added) that can run on a laptop. 
 
-### Build your own sequence of transforms
+### Build Your Own Sequence of Transforms
 Follow the documentation [here](data-processing-lib/doc/overview.md) to build your own pipelines. 
 
-### Automate the pipeline
+### Automate a Pipeline
 The data preprocessing can be automated by running transformers as a KubeFlow pipeline (KFP). 
-See a simple transform pipeline [tutorial](kfp/doc/simple_transform_pipeline.md), and [multi-steps pipeline](kfp/doc/multi_transform_pipeline.md) 
+See this simple transform pipeline [tutorial](kfp/doc/simple_transform_pipeline.md). See [multi-steps pipeline](kfp/doc/multi_transform_pipeline.md) 
 if you want to combine several data transformation steps.
 
-The project facilitates the creation of a local Kind cluster with all the required software and test data. 
-To work with the Kind cluster and KFP, you need to install several pre-required software packages. Please refer to 
-[preinstalled software](./kind/README.md#preinstalled-software) for more details
+The project facilitates the creation of a local [Kind cluster](https://kind.sigs.k8s.io/) with all the required software and test data. 
+To work with the Kind cluster and KFP, you need to install several required software packages. Please refer to 
+[prerequisite software](./kind/README.md#preinstalled-software) for more details.
 
-When you have all packages installed, you can execute 
+When you have all those packages installed, you can execute the following setup command,
 
 ```bash
 make setup
 ```
 from this main package directory or from the `kind` directory.
 
-When you finish working with the cluster, you can destroy it by 
+When you finish working with the cluster, you can destroy it by running,
 ```bash
 make clean
 ```
 
-### How to navigate and use  the repository
-See documentation on [repository structure and its use](doc/repo.md) 
+### How to Navigate and Use the Repository
+See the documentation on [repository structure and its use](doc/repo.md). 
 
 ## &#x1F91D; How to contribute <a name = "contribute_steps"></a>
-See [contribution guide](CONTRIBUTING.md)
+See the [contribution guide](CONTRIBUTING.md)
 
 
 ## &#x2B50; Acknowledgements <a name = "acknowledgement"></a>
-Thanks to the [BigCode Project](https://github.com/bigcode-project) that has been used to build the code quality module. 
+Thanks to the [BigCode Project](https://github.com/bigcode-project), which built the code quality module.