docs: add info about ogbv-ml-rest

README.md

@@ -17,29 +17,34 @@ Through extensive qualitative data collection methods and participatory analysis
 The ultimate aim of the project is to envision creative and collective responses to the structural problem of violence experienced online and help build solidarity and shared understanding while empowering users to take back control of their digital experience.
 
 ### Situating machine learning:
-Machine learning based approaches are a commonly used technique to automate decision making when the volume of data is large. To put it briefly, machine learning works by finding patterns in existing data to ascribe a value to future queries. Instead of telling an algorithm what to do, in machine learning, the algorithm figures out what to do based on the data it is fed. The data used to train a machine learning system as well as the algorithm used to classify the data, can encode social beliefs and values. These are perpetuated in the performance of the machine learning systems. 
 
-The moderation decisions of social media platforms often make international news. Some decisions can be attributed to error. Machine learning system, like every prediction system, makes errors. But some decisions reflect the social values in the data and algorithms behind the model. So, what many communities find harmful may not be harmful as per the guidelines set by social media platforms. 
-Machine learning tools can also be designed to reflect the values of those at the forefront of tackling violence, to protect those who will be at the receiving end of the violence. This is precisely the goal of our project. 
+Machine learning based approaches are a commonly used technique to automate decision making when the volume of data is large. To put it briefly, machine learning works by finding patterns in existing data to ascribe a value to future queries. Instead of telling an algorithm what to do, in machine learning, the algorithm figures out what to do based on the data it is fed. The data used to train a machine learning system as well as the algorithm used to classify the data, can encode social beliefs and values. These are perpetuated in the performance of the machine learning systems.
+
+The moderation decisions of social media platforms often make international news. Some decisions can be attributed to error. Machine learning system, like every prediction system, makes errors. But some decisions reflect the social values in the data and algorithms behind the model. So, what many communities find harmful may not be harmful as per the guidelines set by social media platforms.
+Machine learning tools can also be designed to reflect the values of those at the forefront of tackling violence, to protect those who will be at the receiving end of the violence. This is precisely the goal of our project.
 
 ### Repository Structure
 
-| Directory | Description |
-| --- | --- |
+| Directory         | Description                                                                                  |
+| ----------------- | -------------------------------------------------------------------------------------------- |
 | browser extension | a browser extension that helps moderate and mitigate online gender based violence on twitter |
-| annotators | a web app to annotate tweets |
-|slur-replacement| Python notebook that documents our exact and approximate slur replacement techniques |
-| Scrapers | Twitter and Instagram scrapers we used to collect data for training ML models |
+| annotators        | a web app to annotate tweets                                                                 |
+| slur-replacement  | Python notebook that documents our exact and approximate slur replacement techniques         |
+| scrapers          | Twitter and Instagram scrapers we used to collect data for training ML models                |
+| ogbv-ml-rest      | REST API server and OGBV classifier                                                          |
 
 # Contributing
+
 We are currently working wowards the Chrome and Firefox Extension's v0.2.0 Release.
 You can track the project [here](https://github.com/orgs/tattle-made/projects/20/views/3)
 Find an issue or domain that interests you and reach out to us.
 
 There's also a list of [good first issues](https://github.com/tattle-made/OGBV/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) to get started on.
 
 ## Contact
+
 For more details on this project please send an email to one of the following email IDs:
-* [email protected]
-* [email protected]
-* [email protected]
+
+- [email protected]
+- [email protected]
+- [email protected]