Update README.md

README.md

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 # Code Clippy Github Dataset
 ## Dataset Description
-The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totalling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BiqQuery.
+The Code Clippy dataset consists of various public codebases from GitHub in 22 programming languages with 23 extensions totaling about 16 TB of data when uncompressed. The dataset was created from the public GitHub dataset on Google BigQuery.
 ### How to use it
 This dataset is pretty large please use the streaming parameter from the  `datasets` library as seen below:
 ```python
@@ -109,7 +109,7 @@ Each example is also annotated with the license of the associated repository. Th
 The dataset is about ~ 18 TB uncompressed. We are currently working on processing it and applying further filtering.
 ## Dataset Creation
 The dataset was created in two steps:
-1. Files of with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery using the following query:
+1. Files with the extensions given in the list above were retrieved from the GitHub dataset on BigQuery using the following query:
 
 ```sql
 SELECT
@@ -160,12 +160,12 @@ Since this data was collected from public repositories, there exists potential f
 
 ### Social Impact of Dataset
 
-The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discuss are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
+The paper ["Evaluating Large Language Models Trained on Code"](https://arxiv.org/abs/2107.03374) from OpenAI has a good discussion on what the impact of a large language model trained on code could be. Therefore, some parts of their discussion are highlighted here as it pertains to this dataset and models that may be trained from it. **As well as some differences in views from the paper, particularly around legal implications**.
 
 1. **Over-reliance:** A language model trained on large datasets such as this one for the task of autogenerating code may generate plausible solutions that may appear correct, but are not necessarily the correct solution. Not properly evaluating the generated code may cause have negative consequences such as the introduction of bugs, or the introduction of security vulnerabilities. Therefore, it is important that users are aware of the limitations and potential negative consequences of using a language model trained on this dataset.
 2. **Economic and labor market impacts:** Large language models trained on large code datasets such as this one that are capable of generating high-quality code have the potential to automate part of the software development process. This may negatively impact software developers. However, as discussed in the paper, as shown in the Summary Report of software developers from [O*NET OnLine](https://www.onetonline.org/link/summary/15-1252.00), developers don't just write software.
 3. **Security implications:** No filtering or checking of vulnerabilities or buggy code was performed. This means that the dataset may contain code that may be malicious or contain vulnerabilities. Therefore, any model trained on this dataset may generate vulnerable, buggy, or malicious code. In safety critical software, this could lead to software that may work improperly and could result in serious consequences depending on the software. Additionally, a model trained on this dataset may be used to generate malicious code on purpose in order to perform ransomware or other such attacks.
-4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there has been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset.
+4. **Legal implications:** No filtering was performed on licensed code. This means that the dataset may contain restrictive licensed code. As discussed in the paper, public Github repositories may fall under "fair use." However, there have been little to no previous cases of such usages of licensed publicly available code. Therefore, any model trained on this dataset may be required to obey license terms that align with the software it was trained on such as GPL-3.0, which is why we purposefully put this dataset under the GPL-3.0 license. It is unclear the legal ramifications of using a language model trained on this dataset.
 
 
 ### v1.0