TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification.
PythonApache-2.0★ 1988 estrellas⑂ 200 forksdesde jun 2026Ver en GitHub ↗
google-research/tabfm tiene un índice de salud de 68 sobre 100, lo que lo sitúa en la banda Moderado. Su puntuación más alta es Community & Adoption (74/100) y la más baja, AI Readiness (41/100). Se actualizó por última vez hoy. Una sola persona concentra la mayor parte del trabajo reciente.
68
global / 100
Moderado
Índice de salud del software
Las métricas se agrupan en categorías ponderadas sobre una escala de 1 a 100. El resultado global parte de su media; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe el límite 49 (En riesgo). Preparación para IA queda fuera.
68
Excelente85-100Ejemplar; cumple prácticamente todos los criterios evaluados
Bueno70-84Saludable; carencias menores
Moderado50-69Aceptable con carencias notables; se recomienda revisión
En riesgo30-49Debilidades significativas; su adopción exige cautela
Crítico1-29Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
Perfil de puntuación
Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.
Dependencias directas libres de avisos conocidos — ninguna dependencia directa tiene un aviso conocido
0/25
Dependencias indirectas libres de avisos conocidos — el conjunto transitivo no es separable de las dependencias de desarrollo y prueba en este alcance
0/40
Sin avisos pendientes — ningún aviso tiene fecha de publicación
Datos de entrada utilizados
source
osv
advisories
2
affected_packages
2
assessed_packages
65
unassessed_packages
10
affected_by_severity
moderate 2
direct_affected_packages
0
Excluidos de la puntuación (sin datos o no aplicable): Dependencias indirectas libres de avisos conocidos, Sin avisos pendientes. Los pesos restantes se han renormalizado. Se cotejaron 65 dependencias resueltas con OSV. 10 no pudieron evaluarse: sin versión resuelta, ecosistema no admitido o fuera de la lista de paquetes informada. Este repositorio no publica ningún paquete que el índice resuelva, por lo que se evaluó en su lugar el grafo de dependencias del repositorio. Ese grafo mezcla fijaciones de desarrollo y prueba con las dependencias distribuidas, de modo que solo se puntúan las dependencias declaradas en tiempo de ejecución; los hallazgos transitivos se informan como contexto y quedan excluidos de la puntuación. No se analiza la alcanzabilidad.
¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Es una insignia independiente y experimental — peso 0,0, de modo que se presenta por separado y no afecta a la puntuación de salud global.
deps.dev does not index pypi:tabfm@1.0.1; advisories assessed against the repository dependency graph instead
Más detalle
Historial de estrellas y forks 1988 ★ / 200 ⇿
1988Estrellas
200Forks
Cuándo se añadió cada estrella y fork, recopilado de GitHub y agrupado por día. El crecimiento acumulado se sitúa justo encima de las adiciones diarias que lo componen, de modo que ambos se leen en conjunto: la acumulación orgánica sostenida no se parece en nada a un pico abrupto y efímero. Cuando esa diferencia es medible, se informa como autenticidad del crecimiento.
Solo se muestra el historial más reciente: este repositorio supera la ventana de recopilación, por lo que no se captura el historial más antiguo.
OpenSSF Scorecard 5.2 / 10
5.2agregado
Evaluación de seguridad independiente y agnóstica en cuanto a herramientas, procedente del proyecto de código abierto OpenSSF Scorecard. Cada comprobación premia una práctica de seguridad, no la herramienta de un proveedor concreto. Las comprobaciones que Scorecard no pudo determinar se marcan como n/d y se excluyen de la puntuación de seguridad (nunca se cuentan como cero).Scorecard v5.5.0 · 2026-07-21 18:24 UTC
Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 16 paquetes directos y 59 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.
Registro
Paquete
Versión
Relación
PyPI
absl-py
—
directa
PyPI
absl-py
2.4.0
directa
PyPI
huggingface-hub
—
directa
PyPI
huggingface-hub
1.21.0
directa
PyPI
jaxtyping
—
directa
PyPI
jaxtyping
0.2.25
directa
PyPI
numpy
—
directa
PyPI
numpy
2.2.0
directa
PyPI
pandas
—
directa
PyPI
pandas
2.2.3
directa
PyPI
scikit-learn
—
directa
PyPI
scikit-learn
1.6.0
directa
PyPI
scipy
—
directa
PyPI
scipy
1.17.1
directa
PyPI
typeguard
—
directa
PyPI
typeguard
2.13.3
directa
PyPI
aiofiles
23.2.1
indirecta
PyPI
annotated-doc
0.0.4
indirecta
PyPI
anyio
4.14.1
indirecta
PyPI
certifi
2026.6.17
indirecta
PyPI
chex
0.1.92
indirecta
PyPI
click
8.4.2
indirecta
PyPI
einops
0.8.2
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PyPI
etils
1.7.0
indirecta
PyPI
filelock
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indirecta
PyPI
flax
0.12.7
indirecta
PyPI
flit-core
—
indirecta
PyPI
fsspec
2024.6.0
indirecta
PyPI
h11
0.16.0
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PyPI
hf-xet
1.5.1
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PyPI
httpcore
1.0.9
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PyPI
httpx
0.28.1
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PyPI
humanize
4.9.0
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PyPI
idna
3.18
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PyPI
importlib-resources
6.4.0
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PyPI
jax
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PyPI
jaxlib
0.10.1
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PyPI
jinja2
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PyPI
joblib
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PyPI
markdown-it-py
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PyPI
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PyPI
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PyPI
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PyPI
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PyPI
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PyPI
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PyPI
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PyPI
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PyPI
packaging
26.2
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PyPI
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0.20.0
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PyPI
protobuf
5.29.6
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PyPI
psutil
5.9.8
indirecta
PyPI
pygments
2.20.0
indirecta
PyPI
pylint
—
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PyPI
python-dateutil
2.9.0.post0
indirecta
PyPI
pytz
2024.1
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PyPI
pyyaml
6.0.1
indirecta
PyPI
rich
13.7.1
indirecta
PyPI
setuptools
81.0.0
indirecta
PyPI
shellingham
1.5.4
indirecta
PyPI
simplejson
3.19.2
indirecta
PyPI
six
1.16.0
indirecta
PyPI
sympy
1.14.0
indirecta
PyPI
tensorstore
0.1.84
indirecta
PyPI
threadpoolctl
3.5.0
indirecta
PyPI
toolz
1.1.0
indirecta
PyPI
torch
2.12.1+cpu
indirecta
PyPI
tqdm
4.68.3
indirecta
PyPI
treescope
0.1.10
indirecta
PyPI
typer
0.24.2
indirecta
PyPI
typing-extensions
4.15.0
indirecta
PyPI
tzdata
2024.1
indirecta
PyPI
uvloop
0.19.0
indirecta
PyPI
zipp
4.1.0
indirecta
Avisos de dependencias 2
Este repositorio no publica ningún paquete que el índice resuelva, así que se evaluó su propio grafo de dependencias — 65 paquetes, que incluyen también fijaciones de desarrollo y prueba que nunca se distribuyen: 2 tienen avisos conocidos, de los cuales 0 son directas. 10 no pudieron evaluarse: sin versión resuelta, ecosistema no admitido, o fuera de la lista de paquetes informada.
Paquete
Versión
Relación
Gravedad
Avisos
Corregido en
setuptools
81.0.0
indirecta
moderada
1
83.0.0
torch
2.12.1+cpu
indirecta
moderada
1
2.13.0
Un aviso significa que la versión registrada en el grafo de dependencias cae dentro del rango afectado de un aviso. No se analiza la alcanzabilidad, y el grafo incluye fijaciones de desarrollo y prueba: un hallazgo puede referirse al utillaje y no al software distribuido.
Informe JSON sin procesar legible por máquina
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"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.",
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"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).",
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"headline": "Fail fast when the checkpoint type does not match the estimator",
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"body": "Fix predict crashing on multi-device hosts (IndivisibleError / device mismatch)",
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"headline": "Merge pull request #42 from devYRPauli/fix-multi-device-predict-sharding",
"author_name": "Weihao Kong",
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"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",
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{
"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,
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},
{
"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,
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"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,
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},
{
"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
},
{
"oid": "d316826a05e58c87e0fc3c45235bf167ee2e5115",
"body": null,
"is_bot": false,
"headline": "Fix datetime-as-text detection for pandas>=3 string dtype",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T04:45:46Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "35a8560e892b883781d0c71ae4fd315f3c5163ad",
"body": "Reorganize jax torch",
"is_bot": false,
"headline": "Merge pull request #21 from erzel/reorganize-jax-torch",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-30T02:46:54Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "76aad7ab80d9dc08cef6af3768e17d07d56f2171",
"body": "- Decoupled JAX and PyTorch in classifier_and_regressor.py\n- Protected JAX tests and added PyTorch integration tests\n- Fixed PyTorch model out-of-bounds index crashes\n- Made JAX and PyTorch model loading thread-safe\n- Reorganized JAX targets in Bazel BUILD files",
"is_bot": false,
"headline": "Implement JAX-free support and PyTorch integration for estimators",
"author_name": "Erez Louidor Ilan",
"author_login": "erzel",
"committed_at": "2026-06-30T02:32:11Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "b673130d3768d5a88478b7de6235dd7577ca6f93",
"body": "- Moved JAX files to tabfm/src/jax/\n- Moved PyTorch model implementation to tabfm/src/pytorch/\n- Fixed a precision bug inside InducedSelfAttentionBlock in the JAX code\n- Updated import paths across JAX files, estimator tests, and __init__.py\n- Created pytorch/model_test.py unit tests with numerical parity checks\n- Created hugging_face/convert_and_upload.py to convert Orbax weights to PyTorch weights and upload to hugging face\n- Updated HF JAX model repo to google/tabfm-1.0.0-jax",
"is_bot": false,
"headline": "Reorganize repo into jax/ and pytorch/ subdirectories",
"author_name": "Erez Louidor Ilan",
"author_login": "erzel",
"committed_at": "2026-06-30T02:32:11Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "0842bd42dc7c92b9d7ca8159006512fcf2c213e6",
"body": "…ll CV for large datasets. (#19)",
"is_bot": false,
"headline": "Add parameter to use a single val fold for optimization instead of fu…",
"author_name": "tamannarayan",
"author_login": "tamannarayan",
"committed_at": "2026-06-30T01:35:08Z",
"body_truncated": false,
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},
{
"oid": "da5130ac4ef94ca73cc25754d42e5d1659aad309",
"body": "Support restructured HF JAX checkpoints layout",
"is_bot": false,
"headline": "Merge pull request #20 from google-research/fix-jax-load-path",
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{
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{
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{
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{
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{
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]
},
{
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{
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{
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{
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}
],
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{
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{
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{
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],
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],
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{
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{
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},
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},
{
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"status": "met",
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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,
"status": "met",
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{
"key": "maintained",
"name": "Maintained",
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"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "packaging",
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"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
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],
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},
{
"key": "pinned_dependencies",
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"points": 0,
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},
{
"key": "sast",
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"status": "missed",
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},
{
"key": "security_policy",
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"status": "missed",
"details": [],
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},
{
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"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
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}
],
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{
"key": "token_permissions",
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"status": "missed",
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},
{
"key": "vulnerabilities",
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"status": "partial",
"details": [],
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]
},
{
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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": {}
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{
"code": "advisories_reachability",
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}
],
"value": 100,
"inputs": {
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"assessed_packages": 65,
"unassessed_packages": 10,
"affected_by_severity": "moderate 2",
"direct_affected_packages": 0
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"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
},
{
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"points": 0,
"status": "excluded",
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"code": "advisories_scope_not_separable",
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}
],
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},
{
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"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",
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"assessed_packages": 65,
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"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",
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}
],
"max_points": 100
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
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"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",
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}
],
"value": 100,
"inputs": {
"meaning": "self-published location evidence; not nationality or citizenship",
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"exposures": [],
"policy_countries": [
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"Iran",
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"review_only_matches": 0,
"assessed_self_published_locations": 8
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Registros relacionados
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Las puntuaciones son señales, no garantías. Reflejan prácticas públicamente visibles en GitHub; no son una auditoría de código ni una garantía de seguridad.
Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v1.13.0, esquema v0.23.0 — metodología completa · wiki de métricas.
