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Show HN: Testing HN titles against a neural network

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Re: Show HN: Testing HN titles against a neural network

#211

Post by codesushi42 »

objektif wrote: post #209
codesushi42 wrote: post #195

Earlier quoted context omitted.

This. Exactly this. No sophisticated tokenization. No interesting architecture using attention. And the author is completely clueless about overfitting... and even cross entropy loss. He could have gotten better results just using a bag of words approach. But this ends up on frontpage anyway. Welcome to HN.

What tools would you use to detect overfitting in this case and in general?

My brain.

You will overfit an NN trained on only 1000 examples.

Also a simple train/test split will tell you that. But the author failed to take any time to learn the basics before spewing out this drivel.

Re: Show HN: Testing HN titles against a neural network

#212

Post by Impossible »

I feed it a bunch of articles currently on the front page and tabs I had open that I might submit and got bad on all of them. Then I started typing stereotypical Hackernews click bait and got a pretty solid score.

Paul Graham Rust VC IPO growth Bad: 0.0026 - Good: 0.9976

So far adding any kind of grammatical structure to the random list of keywords that turns it into a title that makes sense completely ruins the score...

Re: Show HN: Testing HN titles against a neural network

#213

Post by freediver »

I spent significant time on binary text classification, specifically with HN titles. You can actually get up to 65-70% post popularity accuracy just by looking at the post title.

I am currently creating a filter that filters HN news and similar sources using a similar classfier. It learns on the fly and the accuracy of guessing my 'taste' is about 75%-80%. Better accuracy for this is explainable by the fact that my interests are more focused and the classifier has easier time predicting posts I would be interested in.

Re: Show HN: Testing HN titles against a neural network

#214

Post by kmod »

freediver wrote: post #213

I spent significant time on binary text classification, specifically with HN titles. You can actually get up to 65-70% post popularity accuracy just by looking at the post title. I am currently creating a filter that filters HN news and similar sources using a similar classfier. It learns on the fly and the accuracy of guessing my 'taste' is about 75%-80%. Better accuracy for this is explainable by the fact that my i…

Cool! I'm really curious about what you're up to since I'm doing something similar. Mine's up at https://www.onlyvetted.com/

Hit me up at kevmod at gmail, would love to hear more.

Re: Show HN: Testing HN titles against a neural network

#216

Post by Avamander »

faizshah wrote: post #190
michjedi wrote: post #131

Current top 10: 1. "Apple introduces 16-inch MacBook Pro, the world’s best pro notebook" Bad: 0.9964 - Good: 0.0038 2. "Developing open-source FPGA tools" Bad: 0.3381 - Good: 0.6652 3. "Show HN: Can a neural network predict if your HN post title will get up votes?" Bad: 0.0598 - Good: 0.9307 4. "How internet ads work" Bad: 1.0000 - Good: 0.0000 5. "More Intel speculative execution vulnerabilities" Bad: 0.7413 - Good:…

"My YC app: Dropbox - Throw away your USB drive" (Bad: 0.9970 - Good: 0.0029)

I think this is a prime example of where AI could go wrong. When people just talk about social media AI curation they don't really understand it. But I personally really wish social media would do less AI curation, who knows what gems we've missed, just because they're maximising for our instant satisfication.

Kinda spooky even, who knows, social media totally might have already killed companies that sounded too different or even just political ideas that differ from mainstream (or sponsored) views?

Re: Show HN: Testing HN titles against a neural network

#218

Post by [deleted] »

GistNoesis wrote: post #154

Congratulations on getting first rank on front page. Congratulations on getting your hands dirty and doing everything yourself like computing gradients manually, badly shuffling (non Fisher-Yates), badly js transpose (double swapping), it is a great way to learn. Congratulations on completing a full pipeline, that's the hard part then it just swapping pieces for better pieces. I advise non-technical readers not to at…

[deleted]

Re: Show HN: Testing HN titles against a neural network

#219

Post by mrburton »

While reading this, I see a lot of negative comments, but here's something to think about.

Machine Learning and AI isn't about producing a perfect solution; that results in things like overfitting. Instead, think of Machine Learning as a human being. No person is going to be prefect and no single person is going to be able to please everyone. Instead, it's about building a solution that generally speaking, provides "good results". Subjective, 100%.

The project just started. Once the OP starts adding new features to the data set and improving the data set itself, I'm sure the results will start getting better and better. At which point, it'll be a good system to "test your subject lines" before posting.

What's the worse it could do? Tell you a subject is great when a bunch say it sucks? I've clicked on many head lines that I thought "sucked" but ended up finding the content very useful.

Great job to the OP and keep it up. This type of work isn't easy but certainly can be fun. Fk all of the negative opinions and keep on keeping on.

Re: Show HN: Testing HN titles against a neural network

#220

Post by mrburton »

michjedi wrote: post #131

Current top 10: 1. "Apple introduces 16-inch MacBook Pro, the world’s best pro notebook" Bad: 0.9964 - Good: 0.0038 2. "Developing open-source FPGA tools" Bad: 0.3381 - Good: 0.6652 3. "Show HN: Can a neural network predict if your HN post title will get up votes?" Bad: 0.0598 - Good: 0.9307 4. "How internet ads work" Bad: 1.0000 - Good: 0.0000 5. "More Intel speculative execution vulnerabilities" Bad: 0.7413 - Good:…

> This project is far from credible. All the things I did were to satisfy my own curiosity. With that being said, the bigger limitation I can see is that I only had access to a few stories. I also cannot validated the neural network prediction, cause in order for me to do that, I would have to write a content, come up with a title and then post it choosing words that triggers a good value on the neural network and post that history on a Friday noon, to see if my story succeed.

This is from the Github project.

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