u/Striking_Care3784

I attempted the OA and ohh boy, I knew I wasn't prepared and I didn't know I was this deep into hell. Attempted like 18 questions and 1 coding question. Everything else was blank for me, i was struggling so badly with the second coding question I just gave up on writing the brute force as well. This was a big slap to my face, already wrapping up my 3rd year and I didn't know I was struggling this bad.

Anyways heads up for anyone attempting, if you have a good enough foundation with statistics, you can do it ( I couldn't ) and I can't say anything about the coding problem, I'm in no position to say it.

Note- failed on three test cases for the first problem as well

Anyways good luck to everyone

So I have a collection of text , basically a description of job responsibilities,and I want to classify them based on their role. Ex- some roles are management heavy while others require more coding. Now first i thought about using jobBert or job2vec for categorisation but the results were not good. The amount of wrong classification was a bit too much. Next I tried topic modelling (tfidf + pca+ k means, embedding +pca + k means , lda ) but it still got a lot of incorrect results. Now, the only option I can see is using some kind of keyword matching( I'm thinking of comparing keywords for both categories and then choosing the one with the majority). I want to use some pre-LLM techniques and as fast as possible. I have been trying to look for some methods but most of the results used LLM one way or another. So I'm looking for an alternative or anything that can improve the results.

So I have a collection of text , basically a description of job responsibilities,and I want to classify them based on their role. Ex- some roles are management heavy while others require more coding. Now first i thought about using jobBert or job2vec for categorisation but the results were not good. The amount of wrong classification was a bit too much. Next I tried topic modelling (tfidf + pca+ k means, embedding +pca + k means , lda ) but it still got a lot of incorrect results. Now, the only option I can see is using some kind of keyword matching( I'm thinking of comparing keywords for both categories and then choosing the one with the majority). I want to use some pre-LLM techniques and as fast as possible. I have been trying to look for some methods but most of the results used LLM one way or another. So I'm looking for an alternative or anything that can improve the results.