google-research/tabfm erreicht einen Gesundheitsindex von 68 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Community & Adoption (74/100) ab, am schwächsten bei AI Readiness (41/100). Zuletzt heute aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.
68
gesamt / 100
Mittel
Software-Gesundheitsindex
Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.
68
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.
Release-Rhythmus — Rhythmus unbekannt (nur ein Release)
0/10
OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count
1
latest_release_tag
v1.0.1
releases_from_tags
nein
days_since_latest_release
0
mean_days_between_releases
—
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.
Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory
0/25
Indirekte Abhängigkeiten ohne bekannte Advisories — transitive Menge in diesem Bereich nicht von Entwicklungs- und Test-Abhängigkeiten trennbar
0/40
Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum
Verwendete Eingangsdaten
source
osv
advisories
2
affected_packages
2
assessed_packages
65
unassessed_packages
10
affected_by_severity
moderate 2
direct_affected_packages
0
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Indirekte Abhängigkeiten ohne bekannte Advisories, Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. 65 aufgelöste Abhängigkeiten wurden mit OSV abgeglichen. 10 konnten nicht bewertet werden — keine aufgelöste Version, ein nicht unterstütztes Ökosystem oder außerhalb der ausgewiesenen Paketliste. Dieses Repository veröffentlicht kein Paket, das der Index auflöst; bewertet wurde daher der Abhängigkeitsgraph des Repositorys. Dieser Graph vermischt Entwicklungs- und Test-Pins mit ausgelieferten Abhängigkeiten, daher werden nur die deklarierten Laufzeit-Abhängigkeiten bewertet; transitive Befunde werden als Kontext ausgewiesen und fließen nicht in die Bewertung ein. Erreichbarkeit wird nicht analysiert.
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.
deps.dev does not index pypi:tabfm@1.0.1; advisories assessed against the repository dependency graph instead
Weitere Details
Stern- und Fork-Verlauf 1.988 ★ / 200 ⇿
1.988Sterne
200Forks
Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.
Es wird nur die jüngste Historie angezeigt — dieses Repository überschreitet das Erfassungsfenster, sodass die früheste Historie nicht erfasst wird.
OpenSSF Scorecard 5.2 / 10
5.2Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).Scorecard v5.5.0 · 2026-07-21 18:24 UTC
Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 16 direkte und 59 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.
Registry
Paket
Version
Beziehung
PyPI
absl-py
—
direkt
PyPI
absl-py
2.4.0
direkt
PyPI
huggingface-hub
—
direkt
PyPI
huggingface-hub
1.21.0
direkt
PyPI
jaxtyping
—
direkt
PyPI
jaxtyping
0.2.25
direkt
PyPI
numpy
—
direkt
PyPI
numpy
2.2.0
direkt
PyPI
pandas
—
direkt
PyPI
pandas
2.2.3
direkt
PyPI
scikit-learn
—
direkt
PyPI
scikit-learn
1.6.0
direkt
PyPI
scipy
—
direkt
PyPI
scipy
1.17.1
direkt
PyPI
typeguard
—
direkt
PyPI
typeguard
2.13.3
direkt
PyPI
aiofiles
23.2.1
indirekt
PyPI
annotated-doc
0.0.4
indirekt
PyPI
anyio
4.14.1
indirekt
PyPI
certifi
2026.6.17
indirekt
PyPI
chex
0.1.92
indirekt
PyPI
click
8.4.2
indirekt
PyPI
einops
0.8.2
indirekt
PyPI
etils
1.7.0
indirekt
PyPI
filelock
3.29.4
indirekt
PyPI
flax
0.12.7
indirekt
PyPI
flit-core
—
indirekt
PyPI
fsspec
2024.6.0
indirekt
PyPI
h11
0.16.0
indirekt
PyPI
hf-xet
1.5.1
indirekt
PyPI
httpcore
1.0.9
indirekt
PyPI
httpx
0.28.1
indirekt
PyPI
humanize
4.9.0
indirekt
PyPI
idna
3.18
indirekt
PyPI
importlib-resources
6.4.0
indirekt
PyPI
jax
0.10.1
indirekt
PyPI
jaxlib
0.10.1
indirekt
PyPI
jinja2
3.1.6
indirekt
PyPI
joblib
1.4.2
indirekt
PyPI
markdown-it-py
3.0.0
indirekt
PyPI
markupsafe
3.0.3
indirekt
PyPI
mdurl
0.1.2
indirekt
PyPI
ml-dtypes
0.5.0
indirekt
PyPI
mpmath
1.3.0
indirekt
PyPI
msgpack
1.2.1
indirekt
PyPI
networkx
3.6.1
indirekt
PyPI
opt-einsum
3.3.0
indirekt
PyPI
optax
0.2.8
indirekt
PyPI
orbax-checkpoint
0.12.0
indirekt
PyPI
packaging
26.2
indirekt
PyPI
prometheus-client
0.20.0
indirekt
PyPI
protobuf
5.29.6
indirekt
PyPI
psutil
5.9.8
indirekt
PyPI
pygments
2.20.0
indirekt
PyPI
pylint
—
indirekt
PyPI
python-dateutil
2.9.0.post0
indirekt
PyPI
pytz
2024.1
indirekt
PyPI
pyyaml
6.0.1
indirekt
PyPI
rich
13.7.1
indirekt
PyPI
setuptools
81.0.0
indirekt
PyPI
shellingham
1.5.4
indirekt
PyPI
simplejson
3.19.2
indirekt
PyPI
six
1.16.0
indirekt
PyPI
sympy
1.14.0
indirekt
PyPI
tensorstore
0.1.84
indirekt
PyPI
threadpoolctl
3.5.0
indirekt
PyPI
toolz
1.1.0
indirekt
PyPI
torch
2.12.1+cpu
indirekt
PyPI
tqdm
4.68.3
indirekt
PyPI
treescope
0.1.10
indirekt
PyPI
typer
0.24.2
indirekt
PyPI
typing-extensions
4.15.0
indirekt
PyPI
tzdata
2024.1
indirekt
PyPI
uvloop
0.19.0
indirekt
PyPI
zipp
4.1.0
indirekt
Abhängigkeits-Advisories 2
Dieses Repository veröffentlicht kein vom Index auflösbares Paket, daher wurde sein eigener Abhängigkeitsgraph bewertet – 65 Pakete, darunter auch Entwicklungs- und Test-Pins, die nie ausgeliefert werden: 2 tragen bekannte Advisories, davon 0 direkte. 10 konnten nicht bewertet werden – keine aufgelöste Version, ein nicht unterstütztes Ökosystem, oder außerhalb der ausgewiesenen Paketliste.
Paket
Version
Beziehung
Schweregrad
Advisories
Behoben in
setuptools
81.0.0
indirekt
mittel
1
83.0.0
torch
2.12.1+cpu
indirekt
mittel
1
2.13.0
Ein Advisory bedeutet, dass die im Abhängigkeitsgraphen erfasste Version in den betroffenen Bereich eines Advisories fällt. Erreichbarkeit wird nicht analysiert, und der Graph enthält Entwicklungs- und Test-Pins — ein Fund kann das Werkzeug betreffen und nicht die ausgelieferte Software.
JSON-Rohbericht maschinenlesbar
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"headline": "Release 1.0.1",
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"headline": "Make fitted estimators picklable after predict (JAX backend)",
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"headline": "Make the PyTorch model picklable (module-level gelu activation)",
"author_name": "Weihao Kong",
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{
"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",
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{
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"headline": "Skip memory_efficient_attention_test.py collection when jax is not in…",
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"committed_at": "2026-07-03T13:04:08Z",
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"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.",
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"headline": "Raise NotFittedError from TabFMRegressor.predict before fit",
"author_name": "Riad Darwish",
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"body_truncated": false,
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{
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"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",
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"committed_at": "2026-07-03T12:30:19Z",
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{
"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
},
{
"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
},
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"headline": "Merge pull request #10 from google-research/dependabot/pip/msgpack-1.2.1",
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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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{
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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": "missed",
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{
"key": "license",
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{
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"status": "missed",
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{
"key": "packaging",
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],
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{
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{
"key": "sast",
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"status": "missed",
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{
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"status": "missed",
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{
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"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
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],
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{
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{
"key": "vulnerabilities",
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]
},
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{
"code": "advisories_scope_repository",
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},
{
"code": "advisories_unassessed",
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"count": 10
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},
{
"code": "advisories_repo_graph_caveat",
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{
"code": "advisories_reachability",
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],
"value": 100,
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"components": [
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"name": "Direct dependencies free of known advisories",
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"points": 35,
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"details": [
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"code": "no_direct_advisories",
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}
],
"max_points": 35
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{
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"status": "excluded",
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],
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},
{
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{
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],
"max_points": 40
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]
},
{
"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"
}
Verwandte Einträge
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Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.23.0 — vollständige Methodik · Metriken-Wiki.
