folder_for uploading tags

Author: aiMojica10

src/pages/MainPage/index.tsx

@@ -64,7 +64,7 @@ export const MainPage: React.FC<Props> = ({data}) => {
           <Typography variant="body1" align="justify">The following tables are complement to the taxonomy presented in the previous chart. These tables are organized in the ML pipeline stages proposed by <a href="https://www.microsoft.com/en-us/research/uploads/prod/2019/03/amershi-icse-2019_Software_Engineering_for_Machine_Learning.pdf">Amershi et al. (2019)</a> (<b><em>Model requirement</em></b>, <b><em> Data collection</em></b>, <b><em> Data cleaning</em></b>, <b><em> Feature engineering</em></b>, <b><em> Data labeling</em></b>, <b><em> Model training</em></b>, <b><em> Model evaluation</em></b>, <b><em> Model deployment</em></b> and <b><em> Model monitoring</em></b>) and an extra stage called <b><em> implementation</em></b>. For each stage, a brief explanation of it is given and a table with the respective practices is presented. In the Table, an indicator per practice is given (this ID match wirh the ID used in the article). In addition to the ID, the taxonomy's categories are presented with the description of the practices.  Furthermore, we present extra resources, the post(s) that is related to the practices, external URL(s) related to the post, and extra urls that help to understand the practices and the ML terminology/concepts associated to them. Kindly note that below each table, you will find an explanation abou the acronyms used in each table.</Typography>
           <br/>
           <Typography variant="h6" align="left">  Model requirement (MR) </Typography>
-          <Typography variant="body1" align="justify"> In this stage, designers decide the functionalities that should be included in an ML system, their usefulness for new or existing products, and the most appropriate type of ML model for the expected system features<a href="https://www.microsoft.com/en-us/research/uploads/prod/2019/03/amershi-icse-2019_Software_Engineering_for_Machine_Learning.pdf">(Amershi et al. (2019))</a>. Four ML best practices were identified for this stage.</Typography>
+          <Typography variant="body1" align="justify"> In this stage, designers decide the functionalities that should be included in an ML system, their usefulness for new or existing products, and the most appropriate type of ML model for the expected system features <a href="https://www.microsoft.com/en-us/research/uploads/prod/2019/03/amershi-icse-2019_Software_Engineering_for_Machine_Learning.pdf">(Amershi et al. (2019))</a>. Four ML best practices were identified for this stage.</Typography>
           <br/>
           <TemplateTable data={TABLE_1} columns={TABLE_1_COLUMNS} tableHeight={540}/>
           <Typography variant="caption" align="justify">CRV: https://stats.stackexchange.com/q<br/>DTSC: https://datascience.stackexchange.com/q<br/>STO: https://stackoverflow.com/q</Typography>
@@ -156,7 +156,7 @@ export const MainPage: React.FC<Props> = ({data}) => {
           <Typography variant="caption" align="justify">CRV: https://stats.stackexchange.com/q<br/>DTSC: https://datascience.stackexchange.com/q<br/>STO: https://stackoverflow.com/q <br/> SE: https://softwareengineering.stackexchange.com/q</Typography>
           <br/>
           <br/>
-          <Typography variant="body1" align="justify"> <b>Taging process:</b> In th following link, you will find the information related to the lagels assigned per each tagger. </Typography>
+          <Typography variant="body1" align="justify"> <b>Taging process:</b> In th following <a href="https://github.com/TheSoftwareDesignLab/ML_best_practices/tree/main/tagging">link</a>, you will find the information related to the labels assigned to each post per each tagger. </Typography>
         </>
         : <TaxPage data={data} onBackClick={() => setOpenTax(false)}/>}
     </Container>