Bonito – the Hassle free text classifier in R

Hacker News

For all tech-people and data-scientists out there: I am happy to launch a prototype version of a text classifier in R. You can run the code locally or in a docker environment. I provide you with an example data set but you are welcome to use your own training data and and set your own labels. I am happy if you use the model and give me feedback. I am also interested in what you have used the model for and with what data. A star on Github always helps too!

Visit product View on Hacker NewsSep 23, 2022

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, dock, code · Missing: mac, agents, macos

56%56% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: training · Missing: arr, mrr, revenue

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

16%16% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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