SOTA NLP Models

Hacker News

SOTA NLP Models

Hi everyone, I needed to break sentences into their individual words and figure out what part of speech each word is. Explosion's Spacy models are absolutely incredible for English, clearly some top tier engineering that I could never come close to, but for other languages they're quite weak. I created my own by taking Spacy outputs, cleaning them up with an LLM, and then fine-tuning a Gemma model on that. The result is extremely good and consistent results for 7 languages. The models are also much cheaper and more consistent than would be possible with ChatGPT. (For example, should "don't" be treated as "don't" or "do", "n't"? ChatGPT will pick one randomly.) It sounds simple, and I'm not going to say it was the most complicated thing ever, but there were quite a few steps involved in getting it right. Getting LLMs to do the cleanup task consistently is very hard. You wouldn't think it but there are often multiple ways to break down a sentence. An interesting part was structuring the model output so it could use the exact same tokens as the input. Most tokens are prefixed by a space, so you want the model's "desired output" to also involve the words prefixed by a space. It makes the task much easier because the model doesn't have to learn the mapping between prefixed and unprefixed tokens. Doing that instantly made my models start performing much better.

Visit product View on Hacker NewsJan 31, 2026

Share card

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →

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

91%91% 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, models, chatgpt · Missing: mac, agents, macos

77%77% 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

39%39% 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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

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

Correct prediction on native model

Similar products

Seed LiveInterpret 2.080%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

The SOTA performance of simultaneous interpretation models

Product Hunt+90API

Goat-7B LLM, a new SOTA among the open-source 7B models50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Goat-7B LLM, a new SOTA among the open-source 7B models

Hacker News3

PostgresML integrates Hugging Face to bring SOTA models into the db62%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

PostgresML integrates Hugging Face to bring SOTA models into the db

Hacker News14

MiniCPM5-1B82%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A new SOTA for compact open models on the edge

Product Hunt+96Open Source

Beta Model Eval Tool – Run SOTA Models on Your CSV57%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Beta Model Eval Tool – Run SOTA Models on Your CSV

Hacker News6

Inference GUIs for 12 SoTA ML models64%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Inference GUIs for 12 SoTA ML models

Hacker News9

Zenith: sota harness for normal models to beat Fable on FrontierSWE58%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Zenith: sota harness for normal models to beat Fable on FrontierSWE

Hacker News8

Validating models in Django 1.844%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Validating models in Django 1.8

Hacker News2

Django-spaghetti-and-meatballs – serving up ERDs from django models50%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Django-spaghetti-and-meatballs – serving up ERDs from django models

Hacker News3

Computed fields for Backbone.Models37%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Computed fields for Backbone.Models

Hacker News3

Browse all products Analyze your launch