What is state-of-the-art-result-for-machine-learning-problems?

redditsota/state-of-the-art-result-for-machine-learning-problems — explained in plain English

Analysis updated 2026-06-24

★ 8,909Audience · researcherComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((ML SOTA))
    What it does
      Tracks best results
      Links to papers
      Lists datasets
    Categories
      Supervised learning
      Unsupervised learning
      Reinforcement learning
    NLP tasks
      Translation
      Sentiment
      Question answering
    Status
      Frozen Feb 2019
      Community maintained

mindmap root((ML SOTA)) What it does Tracks best results Links to papers Lists datasets Categories Supervised learning Unsupervised learning Reinforcement learning NLP tasks Translation Sentiment Question answering Status Frozen Feb 2019 Community maintained

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What do people build with it?

USE CASE 1

Check which research paper holds the top score on a specific machine learning benchmark without searching academic papers one by one.

USE CASE 2

Get a snapshot of NLP, computer vision, and reinforcement learning state-of-the-art results as they stood in February 2019.

How does it compare?

	redditsota/state-of-the-art-result-for-machine-learning-problems	lencx/noi	caorushizi/mediago
Stars	8,909	8,908	8,905
Language	—	TypeScript	TypeScript
Setup difficulty	easy	easy	easy
Complexity	1/5	2/5	2/5
Audience	researcher	developer	general

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 5min

So what is it?

This repository is a reference table tracking the best-known results for a wide range of machine learning research problems. The idea is that when a researcher or developer wants to know how well the top methods perform on a particular task, they can look here instead of hunting through academic papers one by one. The tables cover tasks across several broad categories: supervised learning (which includes speech recognition, computer vision, and natural language processing), semi-supervised learning, unsupervised learning, transfer learning, and reinforcement learning. Within each category, the tables list the research paper that achieved a given result, the dataset it was tested on, the metric used to measure performance (such as accuracy, error rate, or BLEU score for translation), a link to source code if available, and the year the result was published. For example, the natural language processing section covers language modeling, machine translation, question answering, sentiment analysis, text classification, and natural language inference, among other tasks. Each of these has its own table showing which papers hold the top spots on which datasets and what scores they achieved. The repository does not contain any code to run. It is a curated document. The maintainer has noted that the last update was in February 2019, so the entries reflect the state of the field as of that date and have not been kept current since. Results in machine learning research move quickly, and benchmarks that were leading in 2019 have typically been surpassed many times over. The project was started as a community effort, with the maintainer encouraging others to submit new results via GitHub issues or a linked Google Form. Anyone new to GitHub could use the form. The README also notes the maintainer was looking for collaborators, particularly in the areas of natural language processing, computer vision, and reinforcement learning.

Copy-paste prompts

Prompt 1

Show me all the top results for natural language inference benchmarks listed in the state-of-the-art ML results table.

Prompt 2

Based on the state-of-the-art results table, which papers achieved the best scores in machine translation as of 2019?

Prompt 3

I want to submit a new result to the ML state-of-the-art table, what information do I need to include for each entry?

Frequently asked questions