google-research/tabfm має індекс здоров’я 68 зі 100, що відповідає смузі «Помірний». Найвищий показник — Community & Adoption (74/100), найнижчий — AI Readiness (41/100). Останнє оновлення — сьогодні. Більшість нещодавньої роботи виконує один учасник.
68
загалом / 100
Помірний
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.
68
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)
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.
Прямі залежності без відомих сповіщень — жодна пряма залежність не має відомих сповіщень
0/25
Непрямі залежності без відомих сповіщень — транзитивний набір не відокремлюється від залежностей розробки й тестування в цьому обсязі
0/40
Немає задавнених сповіщень — жодне сповіщення не має дати публікації
Використані вхідні дані
source
osv
advisories
2
affected_packages
2
assessed_packages
65
unassessed_packages
10
affected_by_severity
moderate 2
direct_affected_packages
0
Виключено з оцінювання (немає даних або не застосовно): Непрямі залежності без відомих сповіщень, Немає задавнених сповіщень. Залишкові ваги перенормовано. Звірено 65 резолвлених залежностей із OSV. 10 не вдалося оцінити — немає резолвленої версії, непідтримувана екосистема або поза межами звітованого переліку пакетів. Цей репозиторій не публікує пакета, який резолвить індекс, тож натомість оцінено граф залежностей репозиторію. Цей граф змішує закріплені версії для розробки й тестування зі справді постачаними залежностями, тож оцінюються лише задекларовані runtime-залежності; транзитивні знахідки подаються як контекст і в оцінку не входять. Досяжність не аналізується.
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.
deps.dev does not index pypi:tabfm@1.0.1; advisories assessed against the repository dependency graph instead
Докладніше
Історія зірок і форків 1 988 ★ / 200 ⇿
1 988Зірки
200Форки
Коли додано кожну зірку й форк — зібрано з GitHub і згруповано за днями. Кумулятивне зростання розміщено просто над денними додаваннями, з яких воно складається, тож їх видно одне проти одного: рівномірне органічне накопичення виглядає зовсім інакше, ніж різкий короткочасний сплеск. Там, де цю різницю можна виміряти, її подано як автентичність росту.
Показано лише найновішу історію — цей репозиторій перевищує вікно збору, тож найраніша історія не захоплена.
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 · 2026-07-21 18:24 UTC
Повний розв'язаний набір залежностей із графа залежностей GitHub: 16 прямих і 59 непрямих (транзитивних) пакетів. Транзитивне замикання є повним, коли в репозиторії закомічено lockfile.
Реєстр
Пакет
Версія
Зв'язок
PyPI
absl-py
—
пряма
PyPI
absl-py
2.4.0
пряма
PyPI
huggingface-hub
—
пряма
PyPI
huggingface-hub
1.21.0
пряма
PyPI
jaxtyping
—
пряма
PyPI
jaxtyping
0.2.25
пряма
PyPI
numpy
—
пряма
PyPI
numpy
2.2.0
пряма
PyPI
pandas
—
пряма
PyPI
pandas
2.2.3
пряма
PyPI
scikit-learn
—
пряма
PyPI
scikit-learn
1.6.0
пряма
PyPI
scipy
—
пряма
PyPI
scipy
1.17.1
пряма
PyPI
typeguard
—
пряма
PyPI
typeguard
2.13.3
пряма
PyPI
aiofiles
23.2.1
непряма
PyPI
annotated-doc
0.0.4
непряма
PyPI
anyio
4.14.1
непряма
PyPI
certifi
2026.6.17
непряма
PyPI
chex
0.1.92
непряма
PyPI
click
8.4.2
непряма
PyPI
einops
0.8.2
непряма
PyPI
etils
1.7.0
непряма
PyPI
filelock
3.29.4
непряма
PyPI
flax
0.12.7
непряма
PyPI
flit-core
—
непряма
PyPI
fsspec
2024.6.0
непряма
PyPI
h11
0.16.0
непряма
PyPI
hf-xet
1.5.1
непряма
PyPI
httpcore
1.0.9
непряма
PyPI
httpx
0.28.1
непряма
PyPI
humanize
4.9.0
непряма
PyPI
idna
3.18
непряма
PyPI
importlib-resources
6.4.0
непряма
PyPI
jax
0.10.1
непряма
PyPI
jaxlib
0.10.1
непряма
PyPI
jinja2
3.1.6
непряма
PyPI
joblib
1.4.2
непряма
PyPI
markdown-it-py
3.0.0
непряма
PyPI
markupsafe
3.0.3
непряма
PyPI
mdurl
0.1.2
непряма
PyPI
ml-dtypes
0.5.0
непряма
PyPI
mpmath
1.3.0
непряма
PyPI
msgpack
1.2.1
непряма
PyPI
networkx
3.6.1
непряма
PyPI
opt-einsum
3.3.0
непряма
PyPI
optax
0.2.8
непряма
PyPI
orbax-checkpoint
0.12.0
непряма
PyPI
packaging
26.2
непряма
PyPI
prometheus-client
0.20.0
непряма
PyPI
protobuf
5.29.6
непряма
PyPI
psutil
5.9.8
непряма
PyPI
pygments
2.20.0
непряма
PyPI
pylint
—
непряма
PyPI
python-dateutil
2.9.0.post0
непряма
PyPI
pytz
2024.1
непряма
PyPI
pyyaml
6.0.1
непряма
PyPI
rich
13.7.1
непряма
PyPI
setuptools
81.0.0
непряма
PyPI
shellingham
1.5.4
непряма
PyPI
simplejson
3.19.2
непряма
PyPI
six
1.16.0
непряма
PyPI
sympy
1.14.0
непряма
PyPI
tensorstore
0.1.84
непряма
PyPI
threadpoolctl
3.5.0
непряма
PyPI
toolz
1.1.0
непряма
PyPI
torch
2.12.1+cpu
непряма
PyPI
tqdm
4.68.3
непряма
PyPI
treescope
0.1.10
непряма
PyPI
typer
0.24.2
непряма
PyPI
typing-extensions
4.15.0
непряма
PyPI
tzdata
2024.1
непряма
PyPI
uvloop
0.19.0
непряма
PyPI
zipp
4.1.0
непряма
Сповіщення про залежності 2
Цей репозиторій не публікує пакета, який розпізнає індекс, тож оцінено його власний граф залежностей — 65 пакетів, серед яких є й піниї розробки та тестування, що ніколи не постачаються: 2 мають відомі сповіщення, з них 0 прямі. 10 не вдалося оцінити — немає резолвленої версії, непідтримувана екосистема або поза наведеним переліком пакетів.
Пакет
Версія
Зв'язок
Критичність
Сповіщень
Виправлено в
setuptools
81.0.0
непряма
помірна
1
83.0.0
torch
2.12.1+cpu
непряма
помірна
1
2.13.0
Сповіщення означає, що версія, записана в графі залежностей, потрапляє в уражений діапазон. Досяжність не аналізується, а граф містить піниї розробки й тестування — знахідка може стосуватися інструментів, а не поставленого коду.
Звіт у форматі JSON машиночитний
{
"data": {
"repo": {
"topics": [],
"is_fork": false,
"size_kb": 230,
"has_wiki": false,
"homepage": "https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/",
"languages": {
"Python": 422498,
"Starlark": 5389
},
"pushed_at": "2026-07-21T18:18:01Z",
"created_at": "2026-06-16T21:06:19Z",
"owner_type": "Organization",
"updated_at": "2026-07-21T17:54:06Z",
"description": "TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification. ",
"is_archived": false,
"is_disabled": false,
"license_spdx": "Apache-2.0",
"default_branch": "main",
"license_spdx_raw": "Apache-2.0",
"primary_language": "Python",
"significant_languages": [
"Python"
]
},
"owner": {
"blog": "https://research.google",
"name": "Google Research",
"type": "Organization",
"login": "google-research",
"company": null,
"location": "Earth",
"followers": 16705,
"avatar_url": "https://avatars.githubusercontent.com/u/43830688?v=4",
"created_at": "2018-10-03T21:40:41Z",
"is_verified": null,
"public_repos": 350,
"account_age_days": 2847
},
"license": {
"state": "standard",
"spdx_id": "Apache-2.0",
"raw_spdx": "Apache-2.0",
"file_present": true,
"scorecard_found": true,
"profile_has_license": true
},
"activity": {
"releases": [
{
"tag": "v1.0.1",
"kind": "patch",
"published_at": "2026-07-21T18:18:01Z"
}
],
"recent_commits": [
{
"oid": "cb6ba46b7ebc9a6581a81827e14e9c246202afb9",
"body": "remove extra import(`Dict`)",
"is_bot": false,
"headline": "Merge pull request #70 from direkkakkar319-ops/extra-import-dict",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-18T04:25:50Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "e035af9cd68834296d39d445b922303ae95dfb43",
"body": null,
"is_bot": false,
"headline": "removed extra import",
"author_name": "Direk Kakkar",
"author_login": "direkkakkar319-ops",
"committed_at": "2026-07-17T18:54:22Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "f9cdd395aa8b744912d8a308fd95680459e565a8",
"body": "Add ICL context caching for the PyTorch backend + expose in sklearn API",
"is_bot": false,
"headline": "Merge pull request #62 from astonishedrobo/pytorch-context-caching",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-13T23:22:18Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "d8678b6895f1428a468d4cc299c1ff4cf704e726",
"body": "Release 1.0.1",
"is_bot": false,
"headline": "Merge pull request #54 from google-research/bump-version-1.0.1",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-09T20:26:05Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "a4fed9ec4e7c7d31c5c40076d6a5882483a1990e",
"body": null,
"is_bot": false,
"headline": "Update 1.0.1 release date to reflect the final merged fixes",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-09T20:15:11Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "81a3b4b8be04ba7c8bde016ec7ed56eaba7db877",
"body": "…uantization",
"is_bot": false,
"headline": "Add ICL context caching to TabFM PyTorch backend with int8 KV-cache q…",
"author_name": "Soumyajit Basu",
"author_login": "astonishedrobo",
"committed_at": "2026-07-08T15:11:54Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "aca65d18e9d53c6318bcb56687a18ba0b5c52405",
"body": "…he 1.0.1 changelog",
"is_bot": false,
"headline": "Add the checkpoint-mismatch, column-name, and picklability fixes to t…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-06T21:47:54Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "633cd265f498e1d20c9625be0639f6305d8e2541",
"body": "Make fitted estimators picklable after predict (JAX backend)",
"is_bot": false,
"headline": "Merge pull request #48 from fus3r/fix-estimator-pickle-after-predict",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-06T21:46:02Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "30f04b4378e8ff90655feb9120fc02d6ace2cc9e",
"body": null,
"is_bot": false,
"headline": "Merge branch 'main' into fix-estimator-pickle-after-predict",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-06T21:29:34Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "6716b0af8961dbed9e261472e9eccbb138d5214c",
"body": "Fix sklearn-layer crashes on duplicate and non-string column names",
"is_bot": false,
"headline": "Merge pull request #45 from fus3r/fix-column-name-handling",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-06T21:10:30Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "a74e55635ca3c3776186aa4d366237749757bf0a",
"body": "Fail fast with a clear error when the checkpoint type does not match the estimator",
"is_bot": false,
"headline": "Merge pull request #44 from fus3r/fail-fast-on-model-type-mismatch",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-06T21:09:53Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "4d9081a94c95325bda92eb2235ea6366ce976df4",
"body": "…tion\n\nMake the PyTorch model picklable (module-level gelu activation)",
"is_bot": false,
"headline": "Merge pull request #47 from google-research/fix-pytorch-pickle-activa…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-06T17:23:39Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "7468767c21149597b42455cf05b25a4c1b366a64",
"body": "Bump __version__ to 1.0.1 and add the CHANGELOG entry so the auto-publish\npushes a new PyPI release.\n\nThe published 1.0.0 loader looks for pytorch_model.bin, but the Hugging Face\ncheckpoint now ships model.safetensors, so `load()` raises FileNotFoundError.\nThe fixed loader (and the other post-1.0.0 fixes) have been on main since\n1.0.0 was uploaded, but were never released because __version__ was unchanged.",
"is_bot": false,
"headline": "Release 1.0.1",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-04T23:23:06Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "65aeeed690b6e7c29dedb9bb21696f9786287533",
"body": "Enable activation chunking by default with fixed memory-safe sizes",
"is_bot": false,
"headline": "Merge pull request #37 from google-research/fix-default-chunking",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-04T17:16:55Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "e3a66ac2da878206b55d226861daabe0f87323e1",
"body": "The first predict memoizes nnx.jit-compiled step functions on the\nestimator instance (_predict_step_compiled_with_cat /\n_predict_step_compiled_no_cat in _batch_forward). Those closures cannot\nbe pickled, so saving a fitted TabFMClassifier/TabFMRegressor with\nstdlib pickle crashes with \"Can't pickle \n[…]\n-side fix in #47.\n\nDrop the memoized functions from __getstate__ on both estimators: they\nare pure caches and are rebuilt lazily on the next predict. Restored\nestimators produce identical predictions.",
"is_bot": false,
"headline": "Make fitted estimators picklable after predict (JAX backend)",
"author_name": "Riad Darwish",
"author_login": "fus3r",
"committed_at": "2026-07-04T06:40:15Z",
"body_truncated": true,
"is_coding_agent": false
},
{
"oid": "ad9f417ea12e01e315b9e4c8e61c3016027e702f",
"body": "`get_activation(\"gelu\")` returned a lambda, stored as `MLP.act` on the\nencode/decode heads, so pickling the model raised\n`Can't pickle local object 'get_activation.<locals>.<lambda>'`.\nAutoGluon / TabArena save the fitted estimator (which holds the model)\nwith stdlib pickle, so the model must be pic\n[…]\ncklable by reference, and\nnumerically identical to the previous lambda).\n\nAdd pickle round-trip tests for the classifier and regressor models,\nplus a forward-output equivalence check after unpickling.",
"is_bot": false,
"headline": "Make the PyTorch model picklable (module-level gelu activation)",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-04T04:49:02Z",
"body_truncated": true,
"is_coding_agent": false
},
{
"oid": "90ce4e5c29c2354d17d0eef0cd6e843b6aaed9ba",
"body": "Raise NotFittedError from TabFMRegressor.predict before fit",
"is_bot": false,
"headline": "Merge pull request #46 from fus3r/fix-regressor-notfitted",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-04T04:17:56Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "93d371680102aa55cca83714c8257816c72aac91",
"body": "Fix silent query-axis mask/bias collapse in memory-efficient (FLASH) attention",
"is_bot": false,
"headline": "Merge pull request #40 from qflen/fix-memattn-mask-query-collapse",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-04T03:15:46Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "48e54c20149bc382892f91665df305e123bb7c99",
"body": "…stalled",
"is_bot": false,
"headline": "Skip memory_efficient_attention_test.py collection when jax is not in…",
"author_name": "qflen",
"author_login": "qflen",
"committed_at": "2026-07-03T13:04:08Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "c13fe0f00b815ff1312367ecd647b0758514c188",
"body": "TabFMRegressor.predict on an unfitted estimator raised AttributeError\n('X_encoder_') instead of sklearn's NotFittedError. Add the\ncheck_is_fitted call in _predict_internal that TabFMClassifier already\nhas in _predict_proba_internal.",
"is_bot": false,
"headline": "Raise NotFittedError from TabFMRegressor.predict before fit",
"author_name": "Riad Darwish",
"author_login": "fus3r",
"committed_at": "2026-07-03T12:32:30Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "d97bee9e361cde9c33afd3081b923a0730ddbbf6",
"body": "Duplicate column names (common after pandas joins/concats) crashed\nTransformToNumerical with \"'DataFrame' object has no attribute\n'dtype'\". sklearn's ColumnTransformer cannot process them either, so\nfail fast with an actionable ValueError naming the duplicates.\n\nA datetime column with a non-string n\n[…]\nthroughout,\nwhich also drops the name->position get_loc round-trip. Datetime\nexpansion values are unchanged for well-formed inputs (verified\nelement-for-element against the previous name-based logic).",
"is_bot": false,
"headline": "Fix crashes on duplicate and non-string column names",
"author_name": "Riad Darwish",
"author_login": "fus3r",
"committed_at": "2026-07-03T12:30:19Z",
"body_truncated": true,
"is_coding_agent": false
},
{
"oid": "eaf1a271e9c624f9a2c532efdda3a4d437c53cfc",
"body": "A classification checkpoint passed to TabFMRegressor crashes deep in\n_predict_internal with numpy's cryptic 'cannot select an axis to squeeze\nout which has size not equal to one' (issue #43), while a regression\ncheckpoint passed to TabFMClassifier silently returns all-1.0\nprobabilities of shape (T, \n[…]\nutputs, and raise a ValueError naming the\nlikely cause and the exact fix. _batch_forward itself stays\nloss-agnostic (its pass-through behavior is pinned by\ntest_regressor_batch_forward_cross_entropy).",
"is_bot": false,
"headline": "Fail fast when the checkpoint type does not match the estimator",
"author_name": "Riad Darwish",
"author_login": "fus3r",
"committed_at": "2026-07-03T11:25:58Z",
"body_truncated": true,
"is_coding_agent": false
},
{
"oid": "5ee6cd7829b5a4fdfd7e2a266259df733d40d036",
"body": "Fix predict crashing on multi-device hosts (IndivisibleError / device mismatch)",
"is_bot": false,
"headline": "Merge pull request #42 from devYRPauli/fix-multi-device-predict-sharding",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-03T06:31:02Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "455302f479399aca2fc46580574bf6141afb5afc",
"body": "README: load the regression checkpoint in the Regression Example",
"is_bot": false,
"headline": "Merge pull request #41 from qflen/fix-readme-regression-example",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-03T05:52:28Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "cff13f9eb51d76ebad594687de3ac3db4003ac5f",
"body": "…generator\n\nAvoid eager full-train re-transform in EnsembleGenerator._transform_features",
"is_bot": false,
"headline": "Merge pull request #39 from damienrj/fix-eager-transform-in-ensemble-…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-03T05:49:00Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "347ff9e428962004f9c39e3aa0b6da9ad88eebdc",
"body": "… mismatch)\n\nThe JAX forward path in TabFMClassifier and TabFMRegressor rebuilt\ndata_sharding over every visible device on first compile, discarding the\nsharding derived from the active mesh just above. With the default batch_size\nof 1 and no user mesh this forced a batch of size 1 into an N-way sha\n[…]\nrough a user-configured mesh.\n\nAdd a regression test that runs the classifier and regressor default-batch\npredict path on simulated CPU devices, so the multi-device path is covered in\nCI without GPUs.",
"is_bot": false,
"headline": "Fix predict crashing on multi-device hosts (IndivisibleError / device…",
"author_name": "Yash Raj Pandey",
"author_login": "devYRPauli",
"committed_at": "2026-07-03T04:45:49Z",
"body_truncated": true,
"is_coding_agent": false
},
{
"oid": "f013e0c4225487a35fafaef484da1ed8c24ca7bf",
"body": "The example called bare load() for both backends, which default to\nmodel_type=\"classification\", so it downloaded the wrong multi-GB\ncheckpoint and crashed on the first predict() with a squeeze\nValueError (issue #32). Add model_type=\"regression\" to both calls,\nmatching examples/regression_example.py.",
"is_bot": false,
"headline": "README: load the regression checkpoint in the Regression Example",
"author_name": "qflen",
"author_login": "qflen",
"committed_at": "2026-07-03T00:54:38Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "616db09943ee2b21c04a8fc62800bc3bc2aab212",
"body": "bias_fn hardcoded slice_q_len = 1, collapsing any query-varying mask/bias to\neach chunk's first row (the correct min() slicing was commented out just above);\na kv-broadcastable bias also crashed lax.dynamic_slice. Restore the general\nslicing; the [B, 1, 1, S] masks the model builds today stay bit-identical.\nAdds regression tests against jax.nn.dot_product_attention.",
"is_bot": false,
"headline": "Fix silent query-axis mask/bias collapse in memory-efficient attention",
"author_name": "qflen",
"author_login": "qflen",
"committed_at": "2026-07-03T00:47:51Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "cb7cca21f5bd8a791c3cd197ea2e82ccea5d4cb0",
"body": null,
"is_bot": false,
"headline": "Retrigger CLA check",
"author_name": "damienrj",
"author_login": "damienrj",
"committed_at": "2026-07-02T23:02:21Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "5df7deffa20fa6126da4402e54451ba17a6181dd",
"body": "add PyTorchModelHubMixin to TabFM",
"is_bot": false,
"headline": "Merge pull request #33 from kashif/add-pytorch-hub-mixin",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-07-02T22:41:24Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "b8926d42d3e214725cd9b7ad543098a62c82c7a1",
"body": "…eatures\n\ngetattr(obj, name, default) evaluates the default eagerly, so\npreprocessor.transform(self.X_) ran on every call per ensemble member\neven though PreprocessingPipeline.fit() always sets X_transformed_.\nReference the cached attribute directly; behavior is unchanged.",
"is_bot": false,
"headline": "Avoid eager full-train re-transform in EnsembleGenerator._transform_f…",
"author_name": "damienrj",
"author_login": "damienrj",
"committed_at": "2026-07-02T18:45:55Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "ce1612df8ea508ba6b18f60e67427e31291feabd",
"body": "# Conflicts:\n#\ttabfm/src/pytorch/tabfm_v1_0_0.py",
"is_bot": false,
"headline": "Merge remote-tracking branch 'origin/main' into add-pytorch-hub-mixin",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-02T17:38:27Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "35a68537e370ce78666ccc9e4d0c8a2696a945cf",
"body": "Run the PyTorch model in bfloat16 to match the JAX compute dtype",
"is_bot": false,
"headline": "Merge pull request #23 from google-research/run-pytorch-in-bf16",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-02T15:25:07Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "81c2669c946c5cbd0f84969f40080dae2dbdd219",
"body": null,
"is_bot": false,
"headline": "move HF hub code from TabFM into TabFM_HF",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-02T07:28:09Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "2fb67e86477e0d2d3466baed00c2fc43053b0973",
"body": "add ModelHubMixin to JAX model and narrow hub download",
"is_bot": false,
"headline": "Merge pull request #34 from kashif/improve-jax-hub-download",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-07-02T02:54:46Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "99d72b70aa2b690dd5af52ad967e8a07b3b75e82",
"body": null,
"is_bot": false,
"headline": "Enable activation chunking by default with fixed memory-safe sizes",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-02T00:23:29Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "7bbb9040c6af5a1330e0cda2b41dbbf73e69671c",
"body": null,
"is_bot": false,
"headline": "Note that the dtype option may be removed in a future release",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-01T23:44:38Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "cc56f135586d707c192d9bede65838b8cc058010",
"body": "The JAX release runs in bf16 (load(dtype=jnp.bfloat16)) and casts its\ninput to bf16 at the very start of the model's __call__\n(jnp.nan_to_num(X, nan=-100.0).astype(self.dtype)). The native PyTorch\nestimator path ran everything in float32: load() never cast the float32\ncheckpoint, and the model never\n[…]\ns no bfloat16).\n\nHalves activation memory (35.6 -> 17.8 GB on the kddcup09 forward; large\ndatasets that previously OOM'd now fit, ~24 GB) and brings PyTorch\ninference in line with the JAX/TPU results.",
"is_bot": false,
"headline": "Run the PyTorch model in bfloat16 to match the JAX compute dtype",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-01T23:39:09Z",
"body_truncated": true,
"is_coding_agent": false
},
{
"oid": "5fb1e88037b09de471f8709dce59ce7b18ec8b39",
"body": null,
"is_bot": false,
"headline": "wrap long lines in _from_pretrained to fit 80 cols",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T22:08:05Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "d9a97456ac48cb0002b703f64a3675f2a1085067",
"body": null,
"is_bot": false,
"headline": "make TabFM_HF subclass TabFM instead of wrapping it",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T21:58:27Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "69f5e90b1507c34521bf087684a0cb0d0e84cc04",
"body": null,
"is_bot": false,
"headline": "fix subfolder support in _from_pretrained",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T11:12:34Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "61c5675719561b9c7f1d575c157c2219268e592b",
"body": null,
"is_bot": false,
"headline": "fix license to other for non-commercial weights",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T10:42:41Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "56779a13c26aaeb6570e4376e9a382e8590ad7ad",
"body": "TabFMJax wraps the NNX module with from_pretrained, save_pretrained,\nand push_to_hub. snapshot_download now uses allow_patterns to fetch\nonly the needed model_type subfolder instead of both classification\nand regression weights.",
"is_bot": false,
"headline": "add ModelHubMixin to JAX TabFM and narrow snapshot download",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T10:37:53Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "f9c193d66fd529170d88e05ee6cdcf36a4dfc46e",
"body": "TabFM now extends PyTorchModelHubMixin giving it from_pretrained,\nsave_pretrained, and push_to_hub. The load() helper uses\nTabFM.from_pretrained() instead of manual snapshot_download + torch.load.\nsave_pretrained writes model.safetensors which is the preferred format.\nRemove redundant config dataclasses and manual json/bin saving.",
"is_bot": false,
"headline": "add PyTorchModelHubMixin to TabFM pytorch model",
"author_name": "Kashif Rasul",
"author_login": "kashif",
"committed_at": "2026-07-01T10:35:44Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "05f2c8e14064522c3614547cd6e5e8db2b579bdf",
"body": "Fix TabFMClassifier.predict() returning object-dtype labels",
"is_bot": false,
"headline": "Merge pull request #28 from tmacleod/fix-predict-dtype",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-07-01T05:08:50Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "2efc01bee9f764c9f1da23d1fc09ec731d573521",
"body": "CategoricalOrdinalEncoder.inverse_transform() returns an object-dtype\r\nndarray (np.empty(..., dtype=object)). predict() flattens this and\r\nreturns it directly, so predicted labels come back as plain Python\r\nints/strs wrapped in an object array instead of a proper numeric/\r\nstring dtype.\r\n\r\nsklearn's\n[…]\nross_val_score(TabFMClassifier(...), X, y, cv=...,\r\nscoring='accuracy') raises the above even with a dummy model\r\nreturning random logits — confirms this is dtype handling, not a\r\ndata or model issue.",
"is_bot": false,
"headline": "Fix TabFMClassifier.predict() returning object-dtype labels",
"author_name": "tmacleod",
"author_login": "tmacleod",
"committed_at": "2026-07-01T02:57:51Z",
"body_truncated": true,
"is_coding_agent": false
},
{
"oid": "b6ea70b3a76b3c2053c4b89c50abf42c676e0752",
"body": "Skip backend test modules when their optional extra isn't installed",
"is_bot": false,
"headline": "Merge pull request #26 from google-research/fix-ci-skip-backend-tests",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T22:26:20Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "b12b1eca84234c713eaced1db6b3660b501514fe",
"body": "With only .[dev] installed, the backend test modules were skipped (see the\nconftest change). Install both backend extras so the pytorch and jax tests\n(incl. the torch<->jax parity test) actually run in CI. Validated locally:\nthe full suite is 65 passed, 0 failed with both backends present.",
"is_bot": false,
"headline": "ci: install jax and pytorch extras so backend tests run",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T22:18:29Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "5c01e066ceb600581c1c8f502055e98daaa35931",
"body": "The pytorch/jax test modules import torch/chex/flax at import time, but the CI\n\"core tests\" job runs `pip install -e .[dev]`, which pulls neither the\n`pytorch` nor `jax` extra. pytest then fails to *collect* those modules\n(ModuleNotFoundError) and the whole job errors. Add `collect_ignore` to\nconftest.py so a backend's test modules are skipped when that backend isn't\nimportable. pytorch/model_test.py is a torch<->jax parity test (imports both),\nso it's skipped unless both backends are present.",
"is_bot": false,
"headline": "Skip backend test modules when their optional extra isn't installed",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T20:49:45Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "443cbec7fed1a7994dbe65a4664999b4f4015680",
"body": "Add Jax TPU results tables",
"is_bot": false,
"headline": "Merge pull request #25 from google-research/weihaokong-patch-1",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T20:48:06Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "805e6e1231adb13c39c87e39777d726afb087060",
"body": null,
"is_bot": false,
"headline": "Rename classification result tables to the jax-tpu-tabarena convention",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T20:46:01Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "7d6825dbef93a176d3410d9dc4d34a4e0357e8cb",
"body": null,
"is_bot": false,
"headline": "Rename regression result tables to the jax-tpu-tabarena convention",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T20:45:26Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "a8749a6ff20a07f2bd2da9a6652db8b2a5152cfe",
"body": null,
"is_bot": false,
"headline": "Document evaluation results in README (results/)",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T19:05:57Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "4d52e13881f4b8ceea7bd51f9b7691850b4a6e15",
"body": null,
"is_bot": false,
"headline": "Add files via upload",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T18:50:29Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "53f3fcfb8a3355f55c9fb49f04fbb62b8ba29109",
"body": "Add results/ folder",
"is_bot": false,
"headline": "Merge pull request #24 from google-research/add-results-folder",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T18:49:38Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "db04a699b78af698346e22642a324732c1717ced",
"body": null,
"is_bot": false,
"headline": "Add results/ folder",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T18:43:14Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "f9d7ab36cb58196400bd47d630afb7d8764f4e21",
"body": "Update documentation and packaging for JAX/PyTorch separation",
"is_bot": false,
"headline": "Merge pull request #22 from erzel/update-docs",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-06-30T05:50:44Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "4cfef1d63aa8329096457ac471c9e10aae9c27c6",
"body": "- Update README.md to show installation options for JAX and PyTorch backends\n- Make examples in README.md and examples/ directory neutral with comments showing PyTorch usage\n- Expose tabfm_v1_0_0_jax and tabfm_v1_0_0_pytorch symmetric loaders in __init__.py\n- Split pyproject.toml dependencies into optional extras (jax and pytorch)\n- Update CHANGELOG.md with recent changes\n- Fix checkpointing_test.py flag parsing when running via unittest discovery",
"is_bot": false,
"headline": "Update documentation and packaging for JAX/PyTorch separation",
"author_name": "Erez Louidor Ilan",
"author_login": "erzel",
"committed_at": "2026-06-30T05:46:03Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "cdfe068b9907aecab22d134741ddbb22a3673d8e",
"body": "Fix datetime-as-text detection for pandas>=3 string dtype",
"is_bot": false,
"headline": "Merge pull request #18 from google-research/fix-pandas3-datetime",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T05:13:53Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "b7c7d0bd2dab3bf52c65b6fa9e4965cae5fa9a5c",
"body": "The ensemble calibration path in TabFMClassifier.fit called\nchex.assert_shape, but chex is only imported inside the JAX try/except\nblock. In a JAX-free (PyTorch-only) install this raised\n'NameError: name chex is not defined' during fit(). Replace it with an\nequivalent numpy-shape assert and drop the now-unused chex import.",
"is_bot": false,
"headline": "Fix JAX-free crash: replace chex.assert_shape with plain assert",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T05:09:00Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "9144f4d8c502521538c3c1345ac07efa521d81a5",
"body": "…tial-date leniency",
"is_bot": false,
"headline": "Make datetime detector private (_looks_like_datetime) + tests for par…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T04:46:54Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "fe09d44f537c2de38aa93754ec24a254b61140b7",
"body": "…ing dtypes",
"is_bot": false,
"headline": "Add regression tests for datetime-as-text detection across object/str…",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T04:46:25Z",
"body_truncated": false,
"is_coding_agent": false
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"headline": "Merge pull request #11 from weihaokong/add-tamannarayan-codeowner",
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"headline": "Merge pull request #8 from weihaokong/perf-inference",
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"headline": "Merge pull request #10 from google-research/dependabot/pip/msgpack-1.2.1",
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"body": "Encode class labels alphabetically (sklearn convention)",
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"headline": "Merge pull request #7 from weihaokong/y-encoder-alphabetical",
"author_name": "Weihao Kong",
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"headline": "Bump msgpack from 1.0.8 to 1.2.1",
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"headline": "Merge pull request #6 from weihaokong/fix-ci-deps",
"author_name": "Weihao Kong",
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"body": "The classifier already pads the ICL sequence length to a multiple of 128\nfor the flash (memory_efficient_attention) path, but the regressor did\nnot. With flash attention enabled, any regression dataset whose\nin-context sequence length is not a multiple of 128 fails with a reshape\nerror. Mirror the classifier's fix: pad the sequence with -100 and slice\nthe output back to orig_seq_len so padded rows are dropped.",
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"headline": "Apply sequence 128-padding in TabFMRegressor._batch_forward",
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"body": "The config constructor param was stored but never read: the classifier\nnever used it, and the regressor only read self.config as a fallback for\nself.model.loss, which TabFM always sets -- so the fallback was\nunreachable. Drop config from both estimators (and the now-unused\nargparse/flags/logging imp\n[…]\nror was\nunreachable; replace the block with an unconditional output.squeeze(-1).\n\nAlso use self.batch_size directly in the regressor instead of\ngetattr(self, 'batch_size', 1); __init__ always sets it.",
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"body": "Address review: explain that _LOAD_CACHE is a dict keyed by load settings\nso the classification/regression variants can coexist in one process and\nso a cached model is never returned for mismatched settings.",
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"headline": "Document why the load cache is keyed by settings",
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"body": "Address review: use explicit branches for 'appearance', 'alphabetical',\nand 'frequency', and raise ValueError for any unrecognized mode instead\nof silently treating it as appearance order.",
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"headline": "Make encoder mode handling explicit; raise on unknown mode",
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"body": "Removes the never-instantiated ClassNode class and the vestigial\nICLearning.root attribute (hierarchical classification was never\nimplemented; the forward path always raised), the unused\n_round_to_multiple_of helper, and scrubs related docstrings. The\nclassifier now raises a clear ValueError up front when the number of\nclasses exceeds max_classes instead of printing a misleading message\nand failing later in the model.",
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"headline": "Remove dead hierarchical-classification code and unused helper",
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"body": "In TabFMClassifier._batch_forward, pad the sequence length to a multiple\nof 128 with -100 before the (jitted) predict step, and slice the padding\noff the output. Padding outside the jit boundary buckets varying dataset\nsizes to the same compiled shape, avoiding a recompile per distinct\nlength; padded rows fall past train_size and are masked out, so results\nare unchanged.",
"is_bot": false,
"headline": "Pad inference batches to a multiple of 128 to cut JIT recompiles",
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"body": "The v1.0.0 checkpoint is trained with ssmax disabled (ssmax=False\neverywhere; create_ssmax_layer returns None, so no ssmax params exist\nin the checkpoint). Remove the SSMax/SSMaxMLP/QASSMaxMLP classes, the\ncreate_ssmax_layer factory, the ssmax constructor params/pass-throughs,\nthe forward hook, and the related test case. Verified: model loads and\nblood-transfusion ROC AUC is unchanged (~0.748).",
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"headline": "Encode class labels alphabetically to match sklearn convention",
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"body": "The pytest CI (pip install -e .[dev] + pytest -n auto) failed at import\ncollection. Root causes and fixes:\n\n1. Declare runtime deps in pyproject.toml (it was empty) and add the\n missing 'typeguard'. Only the deps with known incompatibilities are\n constrained; the rest float:\n - flax>=0.12.7 \n[…]\nt.py parses absl flags so absltest helpers (create_tempdir ->\n --test_tmpdir) work under the pytest runner.\n\nVerified in a fresh Python 3.11 venv (latest jax/numpy/pandas floated):\n32/32 tests pass.",
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"headline": "Merge pull request #5 from weihaokong/fix-deps",
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{
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{
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],
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{
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{
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{
"key": "fuzzing",
"name": "Fuzzing",
"detail": "project is not fuzzed",
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"status": "missed",
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},
{
"key": "license",
"name": "License",
"detail": "license file detected",
"points": 2.5,
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{
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"status": "missed",
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},
{
"key": "packaging",
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{
"code": "no_data",
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],
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},
{
"key": "pinned_dependencies",
"name": "Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
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},
{
"key": "sast",
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"status": "missed",
"details": [],
"max_points": 5
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{
"key": "security_policy",
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"detail": "security policy file not detected",
"points": 0,
"status": "missed",
"details": [],
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},
{
"key": "signed_releases",
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"detail": "no releases found",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
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],
"max_points": 7.5
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{
"key": "token_permissions",
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"points": 0,
"status": "missed",
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},
{
"key": "vulnerabilities",
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"detail": "2 existing vulnerabilities detected",
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"status": "partial",
"details": [],
"max_points": 7.5
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]
},
{
"key": "dependency_advisories",
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"note": "Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 65 resolved dependencies against OSV; 10 could not be assessed (no resolved version, an unsupported ecosystem, or beyond the reported package list). This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. Reachability is not analyzed.",
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"code": "excluded_no_data",
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},
{
"code": "weights_renormalized",
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{
"code": "advisories_scope_repository",
"params": {
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},
{
"code": "advisories_unassessed",
"params": {
"count": 10
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},
{
"code": "advisories_repo_graph_caveat",
"params": {}
},
{
"code": "advisories_reachability",
"params": {}
}
],
"value": 100,
"inputs": {
"source": "osv",
"advisories": 2,
"affected_packages": 2,
"assessed_packages": 65,
"unassessed_packages": 10,
"affected_by_severity": "moderate 2",
"direct_affected_packages": 0
},
"components": [
{
"key": "direct_dependencies_free_of_known_advisories",
"name": "Direct dependencies free of known advisories",
"detail": "no direct dependency carries a known advisory",
"points": 35,
"status": "met",
"details": [
{
"code": "no_direct_advisories",
"params": {}
}
],
"max_points": 35
},
{
"key": "indirect_dependencies_free_of_known_advisories",
"name": "Indirect dependencies free of known advisories",
"detail": "transitive set not separable from development and test dependencies in this scope",
"points": 0,
"status": "excluded",
"details": [
{
"code": "advisories_scope_not_separable",
"params": {}
}
],
"max_points": 25
},
{
"key": "no_advisories_left_outstanding",
"name": "No advisories left outstanding",
"detail": "no advisory carries a publication date",
"points": 0,
"status": "excluded",
"details": [
{
"code": "advisories_no_publication_date",
"params": {}
}
],
"max_points": 40
}
]
},
{
"key": "malicious_dependencies",
"band": "excellent",
"name": "Malicious dependencies",
"note": null,
"notes": [],
"value": 100,
"inputs": {
"source": "osv",
"meaning": "reported as a malicious package by the OpenSSF corpus; the remedy is removal or moving off the compromised name, never an upgrade of the same artifact. Versions the registry has since pulled are listed but not scored",
"packages": [],
"red_flag": false,
"assessed_packages": 65,
"malicious_packages": 0,
"direct_malicious_packages": 0,
"withdrawn_malicious_packages": 0,
"installable_malicious_packages": 0
},
"components": [
{
"key": "no_dependency_reported_as_a_malicious_package",
"name": "No dependency reported as a malicious package",
"detail": "no dependency is reported as a malicious package",
"points": 100,
"status": "met",
"details": [
{
"code": "no_malicious_dependencies",
"params": {}
}
],
"max_points": 100
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
"band": "excellent",
"name": "High-Risk Jurisdiction Exposure",
"note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
"notes": [
{
"code": "jurisdiction_evidence_limits",
"params": {}
}
],
"value": 100,
"inputs": {
"meaning": "self-published location evidence; not nationality or citizenship",
"red_flag": false,
"exposures": [],
"policy_countries": [
"Russia",
"Iran",
"North Korea"
],
"review_only_matches": 0,
"assessed_self_published_locations": 8
},
"components": [
{
"key": "policy_exposure_multiplier",
"name": "Policy exposure multiplier",
"detail": "no confirmed policy-scope location match",
"points": 100,
"status": "met",
"details": [
{
"code": "jurisdiction_no_match",
"params": {}
}
],
"max_points": 100
}
]
}
],
"description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
},
{
"key": "ai_readiness",
"band": "at_risk",
"name": "AI Readiness",
"value": 41,
"weight": 0,
"metrics": [
{
"key": "ai_agent_context",
"band": "at_risk",
"name": "Agent context & guidance",
"note": null,
"notes": [],
"value": 35,
"inputs": {
"has_llms_txt": false,
"legible_history_share": 0.663,
"agent_instruction_files": [],
"agent_instruction_max_bytes": null
},
"components": [
{
"key": "agent_instructions",
"name": "Agent instructions",
"detail": "no CLAUDE.md / AGENTS.md / editor rules",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_instructions",
"params": {}
}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
"name": "Legible commit history",
"detail": "65 of 98 human commits state their intent (structured subject or explanatory body)",
"points": 35.4,
"status": "partial",
"details": [
{
"code": "legible_history",
"params": {
"legible": 65,
"sampled": 98
}
}
],
"max_points": 40
}
]
},
{
"key": "ai_verify_loop",
"band": "at_risk",
"name": "Verify loop (build / test / typecheck)",
"note": null,
"notes": [],
"value": 41,
"inputs": {
"has_nix": false,
"has_tests": true,
"lockfiles": [],
"has_dockerfile": false,
"typed_language": false,
"bootstrap_files": [],
"has_devcontainer": false,
"has_linter_config": true,
"typecheck_configs": [],
"agent_commit_share": 0,
"toolchain_manifests": [],
"dependency_bot_commit_share": 0.02
},
"components": [
{
"key": "one_command_bootstrap",
"name": "One-command bootstrap",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 18
},
{
"key": "automated_tests",
"name": "Automated tests",
"detail": null,
"points": 22,
"status": "met",
"details": [],
"max_points": 22
},
{
"key": "lint_format_config",
"name": "Lint / format config",
"detail": ".pylintrc",
"points": 11,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": ".pylintrc"
}
}
],
"max_points": 11
},
{
"key": "static_type_checking",
"name": "Static type checking",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 11
},
{
"key": "reproducible_environment",
"name": "Reproducible environment",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
},
{
"key": "demonstrated_agent_practice",
"name": "Demonstrated agent practice",
"detail": "no agent-authored commits among the last 100",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_agent_authored_commits",
"params": {
"sampled": 100
}
}
],
"max_points": 10
},
{
"key": "automated_maintenance",
"name": "Automated maintenance",
"detail": "2 of the last 100 commits are automated dependency updates",
"points": 8,
"status": "met",
"details": [
{
"code": "dependency_bot_commits",
"params": {
"count": 2,
"sampled": 100
}
}
],
"max_points": 8
},
{
"key": "openssf_scorecard_pinned_dependencies",
"name": "OpenSSF Scorecard: Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 10
}
]
},
{
"key": "ai_code_legibility",
"band": "moderate",
"name": "Code legibility for models",
"note": null,
"notes": [],
"value": 51,
"inputs": {
"primary_language": "Python",
"largest_source_bytes": 142134,
"source_files_sampled": 25,
"oversized_source_files": 2
},
"components": [
{
"key": "type_checkable_code",
"name": "Type-checkable code",
"detail": "Python without a type-check config",
"points": 0,
"status": "missed",
"details": [
{
"code": "no_typecheck_config_language",
"params": {
"language": "Python"
}
}
],
"max_points": 45
},
{
"key": "manageable_file_sizes",
"name": "Manageable file sizes",
"detail": "2/25 source files over 60KB",
"points": 50.6,
"status": "partial",
"details": [
{
"code": "oversized_source_files",
"params": {
"kb": 60,
"sampled": 25,
"oversized": 2
}
}
],
"max_points": 55
}
]
},
{
"key": "ai_interfaces",
"band": "at_risk",
"name": "Machine-readable interfaces",
"note": null,
"notes": [],
"value": 40,
"inputs": {
"example_dirs": [
"examples"
],
"has_mcp_signal": false,
"api_schema_files": []
},
"components": [
{
"key": "api_schema_openapi_graphql_proto",
"name": "API schema (OpenAPI/GraphQL/proto)",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 40
},
{
"key": "mcp_server",
"name": "MCP server",
"detail": null,
"points": 0,
"status": "missed",
"details": [],
"max_points": 20
},
{
"key": "runnable_examples",
"name": "Runnable examples",
"detail": "examples",
"points": 40,
"status": "met",
"details": [
{
"code": "file_list",
"params": {
"files": "examples"
}
}
],
"max_points": 40
}
]
}
],
"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
}
],
"metrics_version": "1.13.0"
},
"warnings": [
"deps.dev does not index pypi:tabfm@1.0.1; advisories assessed against the repository dependency graph instead"
],
"report_type": "repository",
"generated_at": "2026-07-21T18:25:14.622670Z",
"schema_version": "0.23.0",
"badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/g/google-research/tabfm.svg",
"full_name": "google-research/tabfm",
"license_state": "standard",
"license_spdx": "Apache-2.0"
}
Пов’язані записи
Перевірені репозиторії, що мають спільні каталожні теги або екосистеми з google-research/tabfm. Читати поруч →
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 v0.23.0 — повна методологія · вікі метрик.
