Публічний реєстр
Звіт про здоров'я програмного забезпеченнясхема 0.15.0 · метрики 1.13.0 · 2026-07-19 17:59 UTC

embeddings-benchmark / mteb

MTEB: State-of-the-art evaluation of embeddings across languages and modalities

Python · Jupyter NotebookApache-2.0★ 3 362 зірки⑂ 645 форківз квіт. 2022 р.Переглянути на GitHub ↗
#benchmark#clustering#information-retrieval#sentence-transformers#sts#text-embedding#retrieval#neural-search#semantic-search#sbert#text-classification#bitext-mining#reranking#multilingual-nlp#mteb#low-resource-nlp#multimodal#embeddings#evaluation

embeddings-benchmark/mteb має індекс здоров’я 82 зі 100, що відповідає смузі «Добрий». Найвищий показник — Vitality (96/100), найнижчий — Security (51/100). Останнє оновлення — сьогодні. Більшість нещодавньої роботи виконують 3 учасники.

82
загалом / 100
Добрий

Індекс здоров'я програмного забезпечення

Метрики згруповано у зважені категорії на шкалі 1–100. Загальна оцінка починається як їхнє середнє; коли публічні дані активують Політику юрисдикцій високого ризику, рейтинг коригується й отримує верхню межу 49 («Під ризиком»). Готовність до ШІ не входить до індексу.

82
Відмінний85-100Зразковий; відповідає практично всім перевіреним критеріям
Добрий70-84Здоровий; незначні прогалини
Помірний50-69Прийнятний, але з помітними прогалинами; рекомендовано перевірку
У зоні ризику30-49Суттєві слабкі місця; впровадження потребує обережності
Критичний1-29Серйозні проблеми (покинутий, єдиний мейнтейнер, без базової гігієни)
ЖиттєздатністьСпільнота тавпровадженняСталість таврядуванняІнженернаякістьБезпекаГотовність доШІ

Профіль оцінок

Кожна вісь — окрема категорія. Форма важить більше, ніж середнє: здоровий об'єкт заповнює всю фігуру, тоді як профіль із піками та провалами означає, що сила в одному вимірі маскує ризик в іншому.

Власність

Massive Text Embedding BenchmarkОрганізація
232 підписники10 публічних репозиторіївз квіт. 2022 р.

За цим репозиторієм стоїть організація — спільна, підзвітна опіка, здатна пережити будь-якого окремого мейнтейнера.

Пакетні екосистеми

РеєстрПакетВерсіяЗавантажень / місВерсіїОстання публікаціяТеги
PyPImteb2.18.4-7610 днів томуdeep-learningtext-embeddingsembeddingsmultimodalbenchmarkretrievalinformation-retrieval

Метрики за категоріями

Життєздатність

Чи живий проєкт — чи пишеться код і чи виходять релізи?

96Відмінний · 22% загального індексу

Активність розробки

100Відмінний
Як обчислюється оцінка
36/36Свіжість push — останній push 0 дн. тому
36/36Ритм комітів — 52/52 тижнів із комітами
18/18Обсяг комітів — 1 172 комітів за останній рік
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
Використані вхідні дані
commits_last_year1 172
human_commit_share—
days_since_last_push0
active_weeks_last_year52

Дисципліна релізів

90Відмінний
Як обчислюється оцінка
27/27Випускає релізи — опубліковано 100 релізів
36/36Свіжість релізів — останній реліз 0 дн. тому
27/27Ритм релізів — реліз кожні ~3,1 дн.
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Використані вхідні дані
releases_count100
latest_release_tag2.18.4
releases_from_tagsні
days_since_latest_release0
mean_days_between_releases3,1

Спільнота та впровадження

Чи має проєкт користувачів, завантаження, увагу та влаштовані умови для контриб’юторів?

82Добрий · 18% загального індексу

Популярність і впровадження

87Відмінний
Як обчислюється оцінка
57.2/60Зірки — 3 362 зірок
23.4/25Форки — 645 форків
6.7/15Спостерігачі — 17 спостерігачів
Використані вхідні дані
forks645
stars3 362
watchers17
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Здоров'я спільноти

77Добрий
Як обчислюється оцінка
22.5/22.5README
22.5/22.5Ліцензія — визнана ліцензія (Apache-2.0)
18/18Настанови CONTRIBUTING
0/13.5Кодекс поведінки
0/7.2Шаблон issue
6.3/6.3Шаблон PR
Використані вхідні дані
has_readmeтак
has_licenseтак
has_contributingтак
has_issue_templateні
has_code_of_conductні
has_pull_request_templateтак

Сталість та врядування

Чи переживе проєкт своїх людей — бас-фактор, реактивність, хто за ним стоїть і як супроводжуються пакети?

80Добрий · 24% загального індексу

Стійкість мейнтейнерів (бас-фактор)

76Добрий
Як обчислюється оцінка
36/54Бас-фактор — на 3 контриб’ютор(ів) припадає половина всіх комітів
16.2/22.5Розподіл комітів — головний контриб’ютор — автор 28% комітів
13.5/13.5Широта контриб’юторів — 100 контриб’юторів
10/10OpenSSF Scorecard: Contributors — project has 36 contributing companies or organizations
Використані вхідні дані
bus_factor3
contributors_sampled100
top_contributor_share0,278

Реактивність на issue та PR

84Добрий
Як обчислюється оцінка
39.7/46.8Вирішення issue — закрито 85% issue
33.9/38.3Прийняття PR — злито 2 661/3 004 вирішених PR
10.5/15OpenSSF Scorecard: Code-Review — Found 21/30 approved changesets -- score normalized to 7
Використані вхідні дані
merged_prs2 661
open_issues270
closed_issues1 531
issue_closed_ratio0,85
closed_unmerged_prs343

Власність та опіка

63Помірний
Як обчислюється оцінка
30/30Підтримка власника — у власності організації
0/20Верифікований домен
17/25Охоплення власника — 232 підписників у embeddings-benchmark
16.2/25Послужний список — 10 публічних репозиторіїв, вік облікового запису ~4 р.
Використані вхідні дані
followers232
owner_typeOrganization
is_verified—
owner_loginembeddings-benchmark
public_repos10
account_age_days1 566

Супровід пакетів

100Відмінний
Як обчислюється оцінка
25/25Опубліковано й доступно — 1 пакет(ів) у pypi
35/35Свіжість публікацій — остання публікація 0 дн. тому
20/20Історія версій — 761 опублікованих версій
20/20Не застарілий — активний, не deprecated і не yanked
Використані вхідні дані
packagesmteb
ecosystemspypi
any_deprecatedні
min_days_since_publish0

Інженерна якість

Чи наявні базові інженерні практики та документація?

92Відмінний · 20% загального індексу

Інженерні практики

94Відмінний
Як обчислюється оцінка
24/24Процеси CI — 15 процес(ів) CI
24/24Наявні тести
16/16Конфігурація лінтера
9.6/9.6Pre-commit-хуки
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 25 out of 25 merged PRs checked by a CI test -- score normalized to 10
Використані вхідні дані
has_ciтак
has_testsтак
has_editorconfigні
has_linter_configтак
has_precommit_configтак

Документація

90Відмінний
Як обчислюється оцінка
30/30README
25/25Каталог документації
15/15Сайт документації / домашня сторінка — https://docs.mteb.org
10/10Опис репозиторію
10/10Теми — 19 тем
0/10Wiki
Використані вхідні дані
topicsbenchmark, clustering, information-retrieval, sentence-transformers, sts, text-embedding, retrieval, neural-search, semantic-search, sbert, text-classification, bitext-mining, reranking, multilingual-nlp, mteb, low-resource-nlp, multimodal, embeddings, evaluation
has_wikiні
homepagehttps://docs.mteb.org
has_readmeтак
has_docs_dirтак
has_descriptionтак

Безпека

Чи міцні видимі практики безпеки й ланцюга постачання, без непослабленої пов’язаності з юрисдикціями високого ризику?

51Помірний · 16% загального індексу

Стан безпеки

51Помірний
Як обчислюється оцінка
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — немає даних
2.5/2.5CI-Tests — 25 out of 25 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
5.2/7.5Code-Review — Found 21/30 approved changesets -- score normalized to 7
2.5/2.5Contributors — project has 36 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Ліцензія — license file detected
7.5/7.5Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Використані вхідні дані
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5,2
Виключено з оцінювання (немає даних або не застосовно): branch_protection. Залишкові ваги перенормовано.

Готовність до ШІ

Наскільки репозиторій оснащений для розробки та супроводу за участі ШІ-агентів? Незалежний, експериментальний бейдж — вага 0.0, тож він подається окремо і не впливає на загальний індекс здоров'я.

54Помірний · 0% загального індексу

Контекст і настанови для агентів

1Критичний
Як обчислюється оцінка
0/45Інструкції для агентів — немає CLAUDE.md / AGENTS.md / правил редактора
0/15Машиночитана документація (llms.txt)
0/40Читабельна історія комітів — немає даних
Використані вхідні дані
has_llms_txtні
legible_history_share—
agent_instruction_files—
agent_instruction_max_bytes—
Виключено з оцінювання (немає даних або не застосовно): Читабельна історія комітів. Залишкові ваги перенормовано.

Цикл верифікації (збірка / тести / перевірка типів)

88Відмінний
Як обчислюється оцінка
18/18Розгортання однією командою — Makefile
22/22Автоматизовані тести
11/11Конфігурація лінтера / форматера
11/11Статична перевірка типів — mteb/py.typed
10/10Відтворюване середовище — Dockerfile, lockfile
0/10Підтверджена практика роботи з агентами — немає даних
0/8Автоматизоване супроводження — немає даних
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Використані вхідні дані
has_nixні
has_testsтак
lockfilesuv.lock
has_dockerfileтак
typed_languageні
bootstrap_filesMakefile
has_devcontainerні
has_linter_configтак
typecheck_configsmteb/py.typed
agent_commit_share—
toolchain_manifests—
dependency_bot_commit_share—
Виключено з оцінювання (немає даних або не застосовно): Підтверджена практика роботи з агентами, Автоматизоване супроводження. Залишкові ваги перенормовано.

Читабельність коду для моделей

82Добрий
Як обчислюється оцінка
27/45Типізований код — Python з конфігурацією перевірки типів (mteb/py.typed)
54.7/55Керовані розміри файлів — 8/1 717 файлів вихідного коду понад 60 КБ
Використані вхідні дані
primary_languagePython
largest_source_bytes305 351
source_files_sampled1 717
oversized_source_files8

Машиночитні інтерфейси

40У зоні ризику
Як обчислюється оцінка
0/40Схема API (OpenAPI/GraphQL/proto)
0/20Сервер MCP
40/40Придатні до запуску приклади — notebooks
Використані вхідні дані
example_dirsnotebooks
has_mcp_signalні
api_schema_files—

Ключові факти

3 362зірок GitHub
100контриб'юторів
1 172комітів за останні 12 місяців
0днів від останнього пушу
100релізів
3бас-фактор
270відкритих issue
PyPIпакетних екосистем

Докладніше

OpenSSF Scorecard 5.2 / 10
5.2сукупно

Незалежна, не прив'язана до інструментів оцінка безпеки від відкритого проєкту OpenSSF Scorecard. Кожна перевірка винагороджує практику безпеки, а не інструмент конкретного постачальника. Перевірки, які Scorecard не зміг визначити, позначено н/д і виключено з оцінки безпеки (вони ніколи не зараховуються як нуль).Scorecard v5.5.0 · 2026-07-19 17:58 UTC

10Binary-Artifactsno binaries found in the repo
н/дBranch-Protectioninternal 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
10CI-Tests25 out of 25 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
7Code-ReviewFound 21/30 approved changesets -- score normalized to 7
10Contributorsproject has 36 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Прямі залежності 14
РеєстрПакетОбмеження версіїМаніфест
PyPIdatasets>=2.19.0pyproject.toml
PyPInumpy>=1.23.0,<3.0.0pyproject.toml
PyPIrequests>=2.26.0pyproject.toml
PyPIscikit-learn>=1.4.0pyproject.toml
PyPIscipy>=1.14.0pyproject.toml
PyPIsentence_transformers>=3.0.0pyproject.toml
PyPItransformers>=4.40.0,<6.0.0pyproject.toml
PyPItyping-extensions>=4.5.0pyproject.toml
PyPItorch>=2.0.0pyproject.toml
PyPItqdm>1.0.0pyproject.toml
PyPIrich>=13.0.0pyproject.toml
PyPIpytrec-eval-terrier>=0.5.6pyproject.toml
PyPIpydantic>=2.11.0pyproject.toml
PyPIpolars>=1.0.0pyproject.toml
Усі залежності 544

Повний розв'язаний набір залежностей із графа залежностей GitHub: 26 прямих і 518 непрямих (транзитивних) пакетів. Транзитивне замикання є повним, коли в репозиторії закомічено lockfile.

РеєстрПакетВерсіяЗв'язок
PyPIdatasets5.0.0пряма
PyPInumpy1.26.4пряма
PyPInumpy2.2.6пряма
PyPInumpy2.3.5пряма
PyPInumpy2.4.6пряма
PyPIpolars1.41.2пряма
PyPIpydantic2.12.4пряма
PyPIpytrec-eval-terrier0.5.10пряма
PyPIrequests2.34.2пряма
PyPIrich15.0.0пряма
PyPIscikit-learn1.7.2пряма
PyPIscikit-learn1.9.0пряма
PyPIscipy1.15.3пряма
PyPIscipy1.17.1пряма
PyPIsentence-transformers5.3.0пряма
PyPIsentence-transformers5.5.1пряма
PyPItorch2.11.0пряма
PyPItorch2.8.0пряма
PyPItqdm4.68.2пряма
PyPItransformers4.44.2пряма
PyPItransformers4.49.0пряма
PyPItransformers4.51.0пряма
PyPItransformers4.52.4пряма
PyPItransformers5.0.0пряма
PyPItransformers5.10.2пряма
PyPItyping-extensions4.15.0пряма
PyPIaccelerate1.13.0непряма
PyPIaiofiles24.1.0непряма
PyPIaiohappyeyeballs2.6.2непряма
PyPIaiohttp3.14.1непряма
PyPIaiolimiter1.2.1непряма
PyPIaiosignal1.4.0непряма
PyPIaltair6.2.1непряма
PyPIannotated-doc0.0.4непряма
PyPIannotated-types0.7.0непряма
PyPIanthropic0.109.1непряма
PyPIantlr4-python3-runtime4.9.3непряма
PyPIanyio4.13.0непряма
PyPIapache-tvm-ffi0.1.9непряма
PyPIarrow1.4.0непряма
PyPIasgiref3.11.1непряма
PyPIast-serialize0.5.0непряма
PyPIastor0.8.1непряма
PyPIasttokens3.0.1непряма
PyPIasync-timeout5.0.1непряма
PyPIattrs26.1.0непряма
PyPIaudioop-lts0.2.2непряма
PyPIaudioread3.1.0непряма
PyPIav17.1.0непряма
PyPIbackports-strenum1.3.1непряма
PyPIbeartype0.22.9непряма
PyPIbeautifulsoup44.15.0непряма
PyPIbibtexparser1.4.4непряма
PyPIblake31.0.8непряма
PyPIbleach6.4.0непряма
PyPIblinker1.9.0непряма
PyPIblis1.3.3непряма
PyPIbm25s0.3.9непряма
PyPIbraceexpand0.1.7непряма
PyPIbrotli1.2.0непряма
PyPIcachetools7.1.4непряма
PyPIcatalogue2.0.10непряма
PyPIcbor1.0.0непряма
PyPIcbor26.1.2непряма
PyPIcertifi2026.5.20непряма
PyPIcffi2.0.0непряма
PyPIcfgv3.5.0непряма
PyPIcharset-normalizer3.4.7непряма
PyPIclick8.4.1непряма
PyPIcloudpathlib0.24.0непряма
PyPIcloudpickle3.1.2непряма
PyPIcodecarbon2.7.1непряма
PyPIcohere5.14.0непряма
PyPIcolorama0.4.6непряма
PyPIcolpali-engine0.3.17непряма
PyPIcompressed-tensors0.15.0.1непряма
PyPIconfection1.3.3непряма
PyPIcontexttimer0.3.3непряма
PyPIcontourpy1.3.2непряма
PyPIcontourpy1.3.3непряма
PyPIcoverage7.14.1непряма
PyPIcryptography43.0.3непряма
PyPIcuda-bindings13.3.1непряма
PyPIcuda-core1.0.1непряма
PyPIcuda-pathfinder1.5.5непряма
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PyPIcuda-tile1.4.0непряма
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Звіт у форматі JSON машиночитний
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            "reason": "no dangerous workflow patterns detected",
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                ],
                "max_points": 20
              }
            ]
          }
        ],
        "description": "Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?"
      },
      {
        "key": "engineering",
        "band": "excellent",
        "name": "Engineering Quality",
        "value": 92,
        "weight": 0.2,
        "metrics": [
          {
            "key": "engineering_practices",
            "band": "excellent",
            "name": "Engineering practices",
            "note": null,
            "notes": [],
            "value": 94,
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              "has_tests": true,
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            "components": [
              {
                "key": "ci_workflows",
                "name": "CI workflows",
                "detail": "15 workflow(s)",
                "points": 24,
                "status": "met",
                "details": [
                  {
                    "code": "ci_workflows",
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                "max_points": 24
              },
              {
                "key": "tests_present",
                "name": "Tests present",
                "detail": null,
                "points": 24,
                "status": "met",
                "details": [],
                "max_points": 24
              },
              {
                "key": "linter_config",
                "name": "Linter config",
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                "points": 16,
                "status": "met",
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              {
                "key": "pre_commit_hooks",
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                "key": "editorconfig",
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              },
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                "key": "openssf_scorecard_ci_tests",
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          {
            "key": "documentation",
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                "key": "documentation_homepage_site",
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                "key": "topics",
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        ],
        "description": "Are baseline engineering and documentation practices in place?"
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      {
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                "status": "met",
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              },
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                "key": "branch_protection",
                "name": "Branch-Protection",
                "detail": "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",
                "points": 0,
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                "points": 0,
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                "key": "code_review",
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                "key": "contributors",
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              },
              {
                "key": "dependency_update_tool",
                "name": "Dependency-Update-Tool",
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                "status": "missed",
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              },
              {
                "key": "fuzzing",
                "name": "Fuzzing",
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                "key": "license",
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                "detail": "license file detected",
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              {
                "key": "maintained",
                "name": "Maintained",
                "detail": "30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10",
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                "status": "met",
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                "points": 5,
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              },
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                "key": "pinned_dependencies",
                "name": "Pinned-Dependencies",
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              {
                "key": "sast",
                "name": "SAST",
                "detail": "SAST tool is not run on all commits -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
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              },
              {
                "key": "security_policy",
                "name": "Security-Policy",
                "detail": "security policy file not detected",
                "points": 0,
                "status": "missed",
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                "key": "vulnerabilities",
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                "Iran",
                "North Korea"
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              "review_only_matches": 0,
              "assessed_self_published_locations": 8
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                "points": 100,
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        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
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        "key": "ai_readiness",
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}

Пов’язані записи

Перевірені репозиторії, що мають спільні каталожні теги або екосистеми з embeddings-benchmark/mteb. Читати поруч →

88weaviate/weaviateWeaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.Відмінний87semi-technologies/weaviateWeaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.Відмінний87creativesoftwarefdn/weaviateWeaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.Відмінний93deepset-ai/haystackOpen-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.Відмінний88eventual-inc/daftHigh-performance data engine for AI and multimodal workloads. Process images, audio, video, and structured data at any scaleВідмінний87Unstructured-IO/unstructuredConvert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.Відмінний

Оцінки — це сигнали, а не гарантії. Вони відображають публічно видимі практики на GitHub — це не аудит коду й не гарантія безпеки.

Відсутні дані виключаються, а ваги перенормовуються — нуль за відсутність ніколи не ставиться. Методологія версіонована й відкрита: метрики v1.13.0, схема v0.15.0 — повна методологія · вікі метрик.

Як окремий результат виглядає на тлі всього реєстру: сукупна статистика — PyPI.