Spacy Dependency Parsing Visualizer

Hacker News

As a frequent user of the Spacy library for NLP ( https://spacy.io/ ), I noticed that it was quite difficult sometimes to know what the various POS tags, dependency labels, and morphological features actually mean . Especially difficult since the different models (e.g. en_core_web_sm) often use different labelling schema. I built this tool to help people get a feel for Spacy dependency parsing. Each POS tag, dependency label and morphological feature links to the relevant documentation to find out more about their meanings. It features a fully parsed version of George Orwell's "Politics and the English Language", with parallel texts in both English and Spanish.

Visit product View on Hacker NewsMar 25, 2023

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2points

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

66%66% 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, user, models · Missing: mac, agents, macos

52%52% 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: io · Missing: https docs, excited, just released

43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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