Öffentliches RegisterExzellent
Software-GesundheitsberichtSchema 0.11.0 · Metriken 1.13.0 · 2026-07-17 00:14 UTC
huggingface / datasets
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
huggingface/datasets erreicht einen Gesundheitsindex von 85 von 100 und liegt damit im Bereich Exzellent. Am stärksten schneidet es bei Vitality (96/100) ab, am schwächsten bei AI Readiness (42/100). Zuletzt heute aktualisiert. 2 Mitwirkende tragen den Großteil der jüngsten Arbeit.
85
gesamt / 100Software-Gesundheitsindex
Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.
85
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Eigentümerschaft
Hugging FaceOrganisation
65.835 Follower455 öffentliche Reposseit Feb. 2017
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Paket-Ökosysteme
| Registry | Paket | Version | Downloads / Monat | Versionen | Zuletzt veröffentlicht | Tags |
|---|---|---|---|---|---|---|
| PyPI | datasets | 5.0.0 | - | 118 | vor 41 Tagen | datasetsmachinelearning |
Metriken nach Kategorie
Vitalität
Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?
96
Entwicklungsaktivität
94Exzellent
Wie die Bewertung erfolgt
| 36/36 | Push-Aktualität — letzter Push vor 0 Tagen |
| 30.5/36 | Commit-Rhythmus — 44/52 Wochen mit Commits |
| 18/18 | Commit-Volumen — 214 Commits im letzten Jahr |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10 |
Verwendete Eingangsdaten
| commits_last_year | 214 |
| human_commit_share | — |
| days_since_last_push | 0 |
| active_weeks_last_year | 44 |
Release-Disziplin
100Exzellent
Wie die Bewertung erfolgt
| 27/27 | Liefert Releases aus — 100 Releases veröffentlicht |
| 36/36 | Release-Aktualität — letztes Release vor 41 Tagen |
| 27/27 | Release-Rhythmus — ein Release etwa alle 11,1 Tage |
| 0/10 | OpenSSF Scorecard: Signed-Releases — keine Daten |
Verwendete Eingangsdaten
| releases_count | 100 |
| latest_release_tag | 5.0.0 |
| releases_from_tags | nein |
| days_since_latest_release | 41 |
| mean_days_between_releases | 11,1 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.
Community & Verbreitung
Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?
92
Popularität & Verbreitung
99Exzellent
Wie die Bewertung erfolgt
| 60/60 | Stars — 21.723 Stars |
| 25/25 | Forks — 3.301 Forks |
| 13.6/15 | Watcher — 281 Watcher |
Verwendete Eingangsdaten
| forks | 3.301 |
| stars | 21.723 |
| watchers | 281 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Community-Gesundheit
85Exzellent
Wie die Bewertung erfolgt
| 22.5/22.5 | README |
| 22.5/22.5 | Lizenz — anerkannte Lizenz (Apache-2.0) |
| 18/18 | CONTRIBUTING-Leitfaden |
| 13.5/13.5 | Verhaltenskodex |
| 0/7.2 | Issue-Vorlage |
| 0/6.3 | PR-Vorlage |
Verwendete Eingangsdaten
| has_readme | ja |
| has_license | ja |
| has_contributing | ja |
| has_issue_template | nein |
| has_code_of_conduct | ja |
| has_pull_request_template | nein |
Nachhaltigkeit & Governance
Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?
78
Wie die Bewertung erfolgt
| 25.2/54 | Bus-Faktor — 2 Beitragende decken die Hälfte aller Commits ab |
| 14.7/22.5 | Commit-Verteilung — wichtigste beitragende Person verfasste 34 % der Commits |
| 13.5/13.5 | Breite der Beitragenden — 100 Beitragende |
| 10/10 | OpenSSF Scorecard: Contributors — project has 30 contributing companies or organizations |
Verwendete Eingangsdaten
| bus_factor | 2 |
| contributors_sampled | 100 |
| top_contributor_share | 0,345 |
Wie die Bewertung erfolgt
| 34/46.8 | Issue-Lösungsquote — 73 % der Issues geschlossen |
| 32.7/38.3 | PR-Annahme — 3.863/4.524 entschiedene PRs gemergt |
| 9/15 | OpenSSF Scorecard: Code-Review — Found 20/30 approved changesets -- score normalized to 6 |
Verwendete Eingangsdaten
| merged_prs | 3.863 |
| open_issues | 903 |
| closed_issues | 2.418 |
| issue_closed_ratio | 0,728 |
| closed_unmerged_prs | 661 |
Wie die Bewertung erfolgt
| 30/30 | Organisatorische Trägerschaft — im Besitz einer Organisation |
| 0/20 | Verifizierte Domain |
| 25/25 | Reichweite des Inhabers — 65.835 Follower von huggingface |
| 25/25 | Kontohistorie — 455 öffentliche Repos, Kontoalter ca. 9 Jahre |
Verwendete Eingangsdaten
| followers | 65.835 |
| owner_type | Organization |
| is_verified | — |
| owner_login | huggingface |
| public_repos | 455 |
| account_age_days | 3.441 |
Paketpflege
100Exzellent
Wie die Bewertung erfolgt
| 25/25 | Veröffentlicht & auflösbar — 1 Paket(e) auf pypi |
| 35/35 | Veröffentlichungsaktualität — letzte Veröffentlichung vor 41 Tagen |
| 20/20 | Versionshistorie — 118 veröffentlichte Versionen |
| 20/20 | Nicht veraltet — aktiv, nicht veraltet oder zurückgezogen |
Verwendete Eingangsdaten
| packages | datasets |
| ecosystems | pypi |
| any_deprecated | nein |
| min_days_since_publish | 41 |
Engineering-Qualität
Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?
95
Engineering-Praktiken
92Exzellent
Wie die Bewertung erfolgt
| 24/24 | CI-Workflows — 7 Workflow(s) |
| 24/24 | Tests vorhanden |
| 16/16 | Linter-Konfiguration |
| 9.6/9.6 | Pre-Commit-Hooks |
| 0/6.4 | .editorconfig |
| 18/20 | OpenSSF Scorecard: CI-Tests — 28 out of 30 merged PRs checked by a CI test -- score normalized to 9 |
Verwendete Eingangsdaten
| has_ci | ja |
| has_tests | ja |
| has_editorconfig | nein |
| has_linter_config | ja |
| has_precommit_config | ja |
Dokumentation
100Exzellent
Wie die Bewertung erfolgt
| 30/30 | README |
| 25/25 | Dokumentationsverzeichnis |
| 15/15 | Dokumentations-/Homepage-Site — https://huggingface.co/docs/datasets |
| 10/10 | Repository-Beschreibung |
| 10/10 | Topics — 16 Topics |
| 10/10 | Wiki |
Verwendete Eingangsdaten
| topics | nlp, datasets, pytorch, tensorflow, pandas, numpy, natural-language-processing, computer-vision, machine-learning, deep-learning, speech, ai, artificial-intelligence, llm, dataset-hub, huggingface |
| has_wiki | ja |
| homepage | https://huggingface.co/docs/datasets |
| has_readme | ja |
| has_docs_dir | ja |
| has_description | ja |
Sicherheit
Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?
61
Sicherheitslage
61Mittel
Wie die Bewertung erfolgt
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — keine Daten |
| 2.2/2.5 | CI-Tests — 28 out of 30 merged PRs checked by a CI test -- score normalized to 9 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 4.5/7.5 | Code-Review — Found 20/30 approved changesets -- score normalized to 6 |
| 2.5/2.5 | Contributors — project has 30 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 0/7.5 | Dependency-Update-Tool — no update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | Lizenz — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10 |
| 0/5 | Packaging — keine Daten |
| 2.5/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 5 |
| 0/5 | SAST — SAST tool is not run on all commits -- score normalized to 0 |
| 5/5 | Security-Policy — security policy file detected |
| 0/7.5 | Signed-Releases — keine Daten |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 7.5/7.5 | Vulnerabilities — 0 existing vulnerabilities detected |
Verwendete Eingangsdaten
| source | openssf_scorecard |
| checks_evaluated | 15 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 3 |
| scorecard_aggregate | 6,1 |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, packaging, signed_releases. Die verbleibenden Gewichte wurden renormalisiert.
AI Readiness
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.
42
AI-Agentenkontext & Leitlinien
1Kritisch
Wie die Bewertung erfolgt
| 0/45 | Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln |
| 0/15 | Maschinenlesbare Doku (llms.txt) |
| 0/40 | Lesbare Commit-Historie — keine Daten |
Verwendete Eingangsdaten
| has_llms_txt | nein |
| legible_history_share | — |
| agent_instruction_files | — |
| agent_instruction_max_bytes | — |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lesbare Commit-Historie. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
| 18/18 | Bootstrap mit einem Befehl — Makefile |
| 22/22 | Automatisierte Tests |
| 11/11 | Lint-/Format-Konfiguration |
| 0/11 | Statische Typprüfung |
| 0/10 | Reproduzierbare Umgebung |
| 0/10 | Belegte Agentenpraxis — keine Daten |
| 0/8 | Automatisierte Wartung — keine Daten |
| 5/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 5 |
Verwendete Eingangsdaten
| has_nix | nein |
| has_tests | ja |
| lockfiles | — |
| has_dockerfile | nein |
| typed_language | nein |
| bootstrap_files | Makefile |
| has_devcontainer | nein |
| has_linter_config | ja |
| typecheck_configs | — |
| agent_commit_share | — |
| toolchain_manifests | — |
| dependency_bot_commit_share | — |
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis, Automatisierte Wartung. Die verbleibenden Gewichte wurden renormalisiert.
AI-Code-Lesbarkeit für Modelle
52Mittel
Wie die Bewertung erfolgt
| 0/45 | Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration |
| 52.4/55 | Handhabbare Dateigrößen — 11/233 Quelldateien über 60 KB |
Verwendete Eingangsdaten
| primary_language | Python |
| largest_source_bytes | 342.300 |
| source_files_sampled | 233 |
| oversized_source_files | 11 |
Eckdaten
21.723GitHub-Sterne
100Mitwirkende
214Commits, letzte 12 Monate
0Tage seit letztem Push
100Releases
2Bus-Faktor
903offene Issues
PyPIPaket-Ökosysteme
Weitere Details
OpenSSF Scorecard 6.1 / 10
6.1Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).
| 10 | Binary-Artifacts | no binaries found in the repo |
| k. A. | Branch-Protection | internal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md |
| 9 | CI-Tests | 28 out of 30 merged PRs checked by a CI test -- score normalized to 9 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 6 | Code-Review | Found 20/30 approved changesets -- score normalized to 6 |
| 10 | Contributors | project has 30 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 0 | Dependency-Update-Tool | no update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10 |
| k. A. | Packaging | packaging workflow not detected |
| 5 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 5 |
| 0 | SAST | SAST tool is not run on all commits -- score normalized to 0 |
| 10 | Security-Policy | security policy file detected |
| k. A. | Signed-Releases | no releases found |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 10 | Vulnerabilities | 0 existing vulnerabilities detected |
Alle Abhängigkeiten 53
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 53 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
| Registry | Paket | Version | Beziehung |
|---|---|---|---|
| PyPI | absl-py | — | indirekt |
| PyPI | aiohttp | — | indirekt |
| PyPI | decorator | — | indirekt |
| PyPI | dill | — | indirekt |
| PyPI | elasticsearch | — | indirekt |
| PyPI | faiss-cpu | — | indirekt |
| PyPI | filelock | — | indirekt |
| PyPI | fsspec | — | indirekt |
| PyPI | gitpython | 3.1.27 | indirekt |
| PyPI | h5py | — | indirekt |
| PyPI | httpx | — | indirekt |
| PyPI | huggingface-hub | — | indirekt |
| PyPI | ipyniivue | 2.4.2 | indirekt |
| PyPI | jax | — | indirekt |
| PyPI | jaxlib | — | indirekt |
| PyPI | joblib | — | indirekt |
| PyPI | moto | — | indirekt |
| PyPI | multiprocess | — | indirekt |
| PyPI | nibabel | — | indirekt |
| PyPI | numpy | — | indirekt |
| PyPI | packaging | — | indirekt |
| PyPI | pandas | — | indirekt |
| PyPI | pdfplumber | — | indirekt |
| PyPI | pillow | — | indirekt |
| PyPI | polars | — | indirekt |
| PyPI | protobuf | — | indirekt |
| PyPI | py7zr | — | indirekt |
| PyPI | pyarrow | — | indirekt |
| PyPI | pyiceberg | — | indirekt |
| PyPI | pylance | — | indirekt |
| PyPI | pyspark | — | indirekt |
| PyPI | pytest | — | indirekt |
| PyPI | pytest-datadir | — | indirekt |
| PyPI | pytest-xdist | — | indirekt |
| PyPI | python-dotenv | 0.19.2 | indirekt |
| PyPI | pyyaml | — | indirekt |
| PyPI | rarfile | — | indirekt |
| PyPI | requests | — | indirekt |
| PyPI | requests | 2.25.1 | indirekt |
| PyPI | ruff | — | indirekt |
| PyPI | sqlalchemy | — | indirekt |
| PyPI | teich | 0.1.5 | indirekt |
| PyPI | tensorflow | 2.12.0 | indirekt |
| PyPI | tiktoken | — | indirekt |
| PyPI | torch | 2.0.1 | indirekt |
| PyPI | torchcodec | — | indirekt |
| PyPI | torchdata | — | indirekt |
| PyPI | tqdm | — | indirekt |
| PyPI | tqdm | 4.62.3 | indirekt |
| PyPI | transformers | 4.30.1 | indirekt |
| PyPI | trimesh | — | indirekt |
| PyPI | xxhash | — | indirekt |
| PyPI | zstandard | — | indirekt |
JSON-Rohbericht maschinenlesbar
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"direct": false,
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{
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}Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.
Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.11.0 — vollständige Methodik · Metriken-Wiki.
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