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  4. spaCy
PythonVisible projectFresh activityNo linked package signalPartial snapshot

explosion/spaCy

Rank not capturedยทTop 100ยทAll-time starsยทโ—Cached ranking snapshotยทUpdated Aug 15, 2026

๐Ÿ’ซ Industrial-strength Natural Language Processing (NLP) in Python

Compare closest alternativesOpen GitHub

First read

Visible and active, but adoption proof is still thinner

explosion/spaCy has enough public attention and recent movement to stay on the shortlist, but package usage is still partial, so the next step should be source and ecosystem validation rather than a quick yes.

Visible

33.8K public stars in the current GitStar snapshot.

Recognizable in the ecosystem

Active

Last commit Aug 7, 2026.

Fresh activity

Adopted

Treat stars as discovery context until a linked package appears.

No linked package mapping

Confidence

One or more key signals are partial, so GitStar keeps the interpretation conservative.

Partial snapshot

Snapshot facts

  • 33.8K stars
  • 4.7K forks
  • Last commit Aug 7, 2026
  • Package usage not mapped yet

Compare lens

keras-team/keras and ray-project/ray are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.

Signal trail

Trajectory

Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.

Daily momentum
Recent momentum is not captured
Weekly momentum
Recent momentum is not captured
Monthly momentum
Recent momentum is not captured
Last commit
Aug 7, 2026

Package reality

No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.

GitStar expects a package signal here, but no npm or PyPI package is linked to this repository yet.

Hype vs Reality
Insufficient Package Data
Package mapping
No linked package yet
Cross-links
Standalone repo read

Validation note

GitStar can summarize public signals for explosion/spaCy, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.

aiartificial-intelligencecythondata-sciencedeep-learningentity-linkingmachine-learningnamed-entity-recognitionnatural-language-processingneural-networkneural-networksnlpnlp-librarypythonspacytext-classificationtokenization
๐Ÿฆ•Living Fossil๐Ÿ™Polyglot
๐Ÿ›ก๏ธ Editorial Context

GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.

Why this rank

This repository stands out because it combines fresh update and stable visibility.

Fresh updateStable visibility
๐Ÿ“ˆ Momentum & Adoption Signals
Approximate star trajectory

Reconstructed from current stars and cached daily/weekly/monthly deltas.

Now: 33.8K
30d ago7d ago1d agoNow
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix (log-scaled for readability)
Current stars33.8K
๐Ÿงญ Hype vs Reality
Insufficient Package Data

GitStar can see repository momentum, but it does not have a reliable linked package signal yet.

Treat stars and recent movement as discovery context only until npm or PyPI usage is available.

Status
Insufficient Package Data
Package footprint
No linked npm or PyPI usage signal
Method note
This label appears when GitStar cannot find strong enough package telemetry to compare attention against adoption. Compare it against other Python repos before treating stars as a moat.
๐Ÿ“ Read the Hype vs Reality heuristic
Related alternative

keras-team/keras

Shares the learning category footprint with explosion/spaCy, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

ray-project/ray

Shares the learning category footprint with explosion/spaCy, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

AMAI-GmbH/AI-Expert-Roadmap

Shares the learning category footprint with explosion/spaCy, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

OlafenwaMoses/ImageAI

Shares the learning category footprint with explosion/spaCy, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Research links

Cross-links

Compare against related repos

GitStar picked keras + ray as the closest next comparison from the related repository set.

Open compare presetCompare with kerasCompare with ray
Repo utility

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Wider nearby ecosystem

Compare with kerasCompare with ray
Python
64.2K stars
keras-team/keras

Deep Learning for humans

Python
43.5K stars
ray-project/ray

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

JavaScript
31.2K stars
AMAI-GmbH/AI-Expert-Roadmap

Roadmap to becoming an Artificial Intelligence Expert in 2022

Python
8.9K stars
OlafenwaMoses/ImageAI

A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

Python
42.9K stars
deepspeedai/DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Rust
34K stars
qdrant/qdrant

Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

Next step after the validation read

Move into a compare preset, organization view, or the heuristic notes once this first fold tells you whether the repo looks visible, active, adopted, and credible enough to keep researching.
Compare the closest alternativesRead the heuristic

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
GuideMethodologyArticlesWeekly Digest

About This Page

This page provides a quick overview of explosion/spaCy based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.

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