# RedditSota/state-of-the-art-result-for-machine-learning-problems

This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date.  If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.

Repository: https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems
Canonical: https://ross.abutalabs.com/products/state-of-the-art-result-for-machine-learning-problems
License: Apache-2.0
License Family: permissive
Last push: 2019-06-25T14:09:52+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3220, "days_push": 2626, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8892, forks 1293 (observed 2026-08-28T04:10:26.183954+00:00)

## What it is
A curated repository tracking state-of-the-art (SoTA) results across machine learning problems, organized by learning paradigm (supervised, semi-supervised, unsupervised, transfer, reinforcement learning). Each entry lists research papers, datasets, metrics, source code links, and years.

## Use cases
- find the state-of-the-art result for a machine learning benchmark
- look up best performing models on datasets like ImageNet or WikiText
- find research papers that achieved SoTA on a task
- check which model holds the record for language modeling perplexity
- discover source code implementations of top-performing ML papers
- compare benchmark metrics across computer vision and NLP tasks

## When to choose
- you need a quick reference for current SoTA results across many ML tasks
- you want paper, dataset, metric, and code links in one place
- you are surveying progress in NLP, computer vision, speech, or reinforcement learning

## When to avoid
- you need guaranteed up-to-date leaderboards (last update was February 2019)
- you need a runnable tool or library rather than a reference list
- you need exhaustive coverage of niche subfields or recent benchmarks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, nlp, computer-vision, speech-recognition, reinforcement-learning
- domain: machine-learning, deep-learning, computer-vision, artificial-intelligence, awesome-lists
- platform: cross-platform
- tags: state-of-the-art, benchmark-results, research-papers, leaderboard, curated-list, natural-language-processing

## Member repositories
- RedditSota/state-of-the-art-result-for-machine-learning-problems (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:26.183954+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:24:44.212820+00:00, confidence not recorded.
  - readme: https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems (fetched 2026-08-28T04:10:26.183954+00:00, sha 6a840669c1f2)
- Data as of 2026-08-30T08:39:29.467469+00:00.