Show HN: Cherry2.0 – text classification without ML knowledge needed | Hacker News Reader

Show HN: Cherry2.0 – text classification without ML knowledge needed

github.com

71 pointsWindson11 comments

The pre-built model is a facinating insight to Chinese politics/society.

> Gamble / Porn / Political / Nomal (model='harmful')

Not sure what Nomal is, but 'Political' is considered harmful? I suppose they mean politics has the potential to be harmful. Would be interesting to see what material it was trained on (a cross section of pro and anti-CCP? pro-CCP would still be political, but then why would that need to be classified under a harmful model?)

> Lottery ticket / Finance / Estate / Home / Tech / Society / Sport / Game / Entertainment (model='news')

Interesting bit here is that Lottery Tickets are included in news and yet gambling is included in harmful. Is the lottery not considered gambling in China? Or gambling just has the potential to be harmful, despite also being news?

Looks like "Nomal" is a typo for "normal", based on Google Translate's translation of 正常.

Ah that would make sense. I figured as much, as there would need to be a "non-harmful" category for everything else in that model.

CCP is seen as a stabilizer. "Politics" means disrupters.

> but 'Political' is considered harmful?

It's hard to explain why 'Political' is considered harmful in China. Most of the Chinese companies try to avoid the political text classify problem by using a hashtable to store 'sensitive words'. One example would be we can't search '64g RAM' in Xianyu, which is one of the biggest c2c second-hand online markets developed from Alibaba because 64 indicate June 4th which is a special day in China.

> Would be interesting to see what material it was trained on (a cross section of pro and anti-CCP? pro-CCP would still be political, but then why would that need to be classified under a harmful model?)

Good question. It's quite subjective and no one knows the answer. Companies have to do lots of self-censorships. However, unlike porn or gamble. We don't have a standard here.

> Is the lottery not considered gambling in China?

Lottery ticket is gambling, but in China, we call it 福利彩票 which indicate the money will be used in charity. It's quite different from gambling on football or basketball and Lottery ticket

> June 4th which is a special day in China

Yes, that's one way to put it.

As I understand, the lottery is run by the state. Gambling is strongly discouraged, if not actually illegal in most of China (except Macau)

Hey Windson, this is super cool, can't wait to try this out. Wish I had it 6 months ago at my last job when I was starting to build my own text classification system. I submitted a PR fixing the typos in the english translation, no promises that I got them all though.

Thank you @ngngngng, I will have a look at it soon :D

I have another pre-trained model for spam email classify in English. I didn't add it to cherry because I don't know if anyone need it. It will be great if someone can tell me what kind of pre-trained model they need so I can add it later.

Why do people think any kind of “x for people with no knowledge of x” tool is a worthwhile idea? It reminds me of various cloud vendor ML offerings too. With ML, you _always_ need ML domain knowledge to (at minimum) understand the performance characteristics of the black box you’re using, and (more often) also how to re-train/fine-tune/incrementally update as your use case’s data distribution changes over time or your required performance characteristics change over time.

Like with AWS Rekognition where you’re billed on raw usage, which makes no sense when what matters is going to be the precision & recall & false detection rate on your data distribution (not whatever Amazon’s team trains it on). How many true positives / false positives / etc. will you get on your data?

ML is uniquely poorly suited to be treated as just some API or just some black box library. I really wish people would stop popularizing footgun approaches to this!

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