Update README.md

README.md

@@ -22,29 +22,6 @@ It achieves the following results on the evaluation set:
 
 ## Model description
 
-User
-Im writing a model card for my machine learning model that I uploaded online, I wrote a rough description but can you fix my explanation, make it as correct and clear as possible, and spread out the descriptions under the three titles mentioned below:## Model description
-
-I needed a topic classifier fine-tuned on subreddit descriptions to distinguish if that subreddit has to do with specific technology companies/stocks.
-
-## Intended uses & limitations
-
-More information needed
-a binary topic classifier for my project.
-## Training and evaluation data
-
-I labeled about a 1,000 descriptions of subreddits I extracted using the Reddit API. 
-My project is regarding the "Magnificent Seven Companies" (Apple, Microsoft, Alphabet, Amazon, Nvidia, Tesla, and Meta), 
-I extracted subreddits based on keyword searches of those companies. The results yielded enough subreddits that were not 
-technology related (technology finance, stock, crypto) in the sense that I needed them to be for my project, so labeling 
-the subreddits resulting from these company keyword searches yielded a relatively even distribution of subreddits labeled
-as 1 (being technology related) and 0 (being not technology related), that I felt confident that 1,000 data points were
-enough to fine tune the model for my purposes. Because i am going to have a large sample size when evaluating data from
-a spread of subreddits, I believe the error will be get evened out by the large number of samples I'm using.
-
-ChatGPT
-Model Description
-
 I developed a topic classifier with the specific purpose of discerning whether a given subreddit is associated with particular technology companies or stocks. The model focuses on identifying subreddits related to the "Magnificent Seven Companies," namely Apple, Microsoft, Alphabet, Amazon, Nvidia, Tesla, and Meta.
 
 ## Intended uses & limitations