TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification.
PythonApache-2.0★ 1,988 stars⑂ 200 forkssince Jun 2026View on GitHub ↗
google-research/tabfm holds a health index of 68 out of 100, placing it in the Moderate band. It scores highest on Community & Adoption (74/100) and lowest on AI Readiness (41/100). It was last updated today. A single contributor accounts for most of its recent work.
68
overall / 100
Moderate
Software health index
Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At risk ceiling of 49. AI Readiness sits outside the overall score.
68
Excellent85-100Exemplary; meets essentially all checked criteria
Good70-84Healthy; minor gaps
Moderate50-69Acceptable with notable gaps; review recommended
At risk30-49Significant weaknesses; adoption warrants caution
Critical1-29Severe problems (abandoned, single-maintainer, no hygiene)
Score profile
Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.
Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25
Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40
No advisories left outstanding — no advisory carries a publication date
Inputs used
source
osv
advisories
2
affected_packages
2
assessed_packages
65
unassessed_packages
10
affected_by_severity
moderate 2
direct_affected_packages
0
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.
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.
deps.dev does not index pypi:tabfm@1.0.1; advisories assessed against the repository dependency graph instead
More detail
Star and fork history 1,988 ★ / 200 ⇿
1,988Stars
200Forks
When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.
Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.
OpenSSF Scorecard 5.2 / 10
5.2aggregate
Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-07-21 18:24 UTC
Full resolved dependency set from the GitHub dependency graph: 16 direct and 59 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.
Registry
Package
Version
Relation
PyPI
absl-py
—
direct
PyPI
absl-py
2.4.0
direct
PyPI
huggingface-hub
—
direct
PyPI
huggingface-hub
1.21.0
direct
PyPI
jaxtyping
—
direct
PyPI
jaxtyping
0.2.25
direct
PyPI
numpy
—
direct
PyPI
numpy
2.2.0
direct
PyPI
pandas
—
direct
PyPI
pandas
2.2.3
direct
PyPI
scikit-learn
—
direct
PyPI
scikit-learn
1.6.0
direct
PyPI
scipy
—
direct
PyPI
scipy
1.17.1
direct
PyPI
typeguard
—
direct
PyPI
typeguard
2.13.3
direct
PyPI
aiofiles
23.2.1
indirect
PyPI
annotated-doc
0.0.4
indirect
PyPI
anyio
4.14.1
indirect
PyPI
certifi
2026.6.17
indirect
PyPI
chex
0.1.92
indirect
PyPI
click
8.4.2
indirect
PyPI
einops
0.8.2
indirect
PyPI
etils
1.7.0
indirect
PyPI
filelock
3.29.4
indirect
PyPI
flax
0.12.7
indirect
PyPI
flit-core
—
indirect
PyPI
fsspec
2024.6.0
indirect
PyPI
h11
0.16.0
indirect
PyPI
hf-xet
1.5.1
indirect
PyPI
httpcore
1.0.9
indirect
PyPI
httpx
0.28.1
indirect
PyPI
humanize
4.9.0
indirect
PyPI
idna
3.18
indirect
PyPI
importlib-resources
6.4.0
indirect
PyPI
jax
0.10.1
indirect
PyPI
jaxlib
0.10.1
indirect
PyPI
jinja2
3.1.6
indirect
PyPI
joblib
1.4.2
indirect
PyPI
markdown-it-py
3.0.0
indirect
PyPI
markupsafe
3.0.3
indirect
PyPI
mdurl
0.1.2
indirect
PyPI
ml-dtypes
0.5.0
indirect
PyPI
mpmath
1.3.0
indirect
PyPI
msgpack
1.2.1
indirect
PyPI
networkx
3.6.1
indirect
PyPI
opt-einsum
3.3.0
indirect
PyPI
optax
0.2.8
indirect
PyPI
orbax-checkpoint
0.12.0
indirect
PyPI
packaging
26.2
indirect
PyPI
prometheus-client
0.20.0
indirect
PyPI
protobuf
5.29.6
indirect
PyPI
psutil
5.9.8
indirect
PyPI
pygments
2.20.0
indirect
PyPI
pylint
—
indirect
PyPI
python-dateutil
2.9.0.post0
indirect
PyPI
pytz
2024.1
indirect
PyPI
pyyaml
6.0.1
indirect
PyPI
rich
13.7.1
indirect
PyPI
setuptools
81.0.0
indirect
PyPI
shellingham
1.5.4
indirect
PyPI
simplejson
3.19.2
indirect
PyPI
six
1.16.0
indirect
PyPI
sympy
1.14.0
indirect
PyPI
tensorstore
0.1.84
indirect
PyPI
threadpoolctl
3.5.0
indirect
PyPI
toolz
1.1.0
indirect
PyPI
torch
2.12.1+cpu
indirect
PyPI
tqdm
4.68.3
indirect
PyPI
treescope
0.1.10
indirect
PyPI
typer
0.24.2
indirect
PyPI
typing-extensions
4.15.0
indirect
PyPI
tzdata
2024.1
indirect
PyPI
uvloop
0.19.0
indirect
PyPI
zipp
4.1.0
indirect
Dependency advisories 2
This repository publishes no package the index resolves, so its own dependency graph was assessed — 65 packages, which also include development and test pins that never ship: 2 carry known advisories, of which 0 are direct. 10 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.
Package
Version
Relation
Severity
Advisories
Fixed in
setuptools
81.0.0
indirect
moderate
1
83.0.0
torch
2.12.1+cpu
indirect
moderate
1
2.13.0
An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.
Raw JSON report machine-readable
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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": "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).",
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"headline": "Fix crashes on duplicate and non-string column names",
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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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"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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"body": "README: load the regression checkpoint in the Regression Example",
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"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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"body": "…generator\n\nAvoid eager full-train re-transform in EnsembleGenerator._transform_features",
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"headline": "Merge pull request #39 from damienrj/fix-eager-transform-in-ensemble-…",
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"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",
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"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
},
{
"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,
"is_coding_agent": false
},
{
"oid": "da5130ac4ef94ca73cc25754d42e5d1659aad309",
"body": "Support restructured HF JAX checkpoints layout",
"is_bot": false,
"headline": "Merge pull request #20 from google-research/fix-jax-load-path",
"author_name": "Erez Louidor",
"author_login": "erzel",
"committed_at": "2026-06-30T00:40:38Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "a2f75a18a169fb10e0f6a4519923d2bb3eaadba7",
"body": null,
"is_bot": false,
"headline": "Update JAX repository ID to tabfm-1.0.0-jax",
"author_name": "Erez Louidor Ilan",
"author_login": "erzel",
"committed_at": "2026-06-29T23:25:22Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "15422d70898e86bfe8af3cec90d9c0580a6c2b42",
"body": "Use airfoil_self_noise and maternal_health_risk for the examples",
"is_bot": false,
"headline": "Merge pull request #15 from google-research/swap-example-datasets",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-29T16:46:42Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "ef3bec82fa7c2fc05687b0ffb8b5a8d31f98c726",
"body": null,
"is_bot": false,
"headline": "Use clf.classes_ directly in the classification example",
"author_name": "Weihao Kong",
"author_login": "weihaokong",
"committed_at": "2026-06-27T00:09:43Z",
"body_truncated": false,
"is_coding_agent": false
},
{
"oid": "12948ca3d09eb7873eefcf4dc189cb694ed77db1",
"body": null,
"is_bot": false,
"headline": "Use maternal_health_risk for the classification example",
"author_name": "Weihao Kong",
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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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},
{
"key": "stewardship",
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"notes": [],
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"components": [
{
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],
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{
"key": "verified_domain",
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"detail": null,
"points": 0,
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},
{
"key": "owner_reach",
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"login": "google-research"
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}
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{
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"points": 25,
"status": "met",
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},
{
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}
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]
},
{
"key": "package_maintenance",
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"note": null,
"notes": [],
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"inputs": {
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"ecosystems": "pypi",
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"min_days_since_publish": 0
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"components": [
{
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}
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{
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"details": [
{
"code": "publish_recency",
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}
],
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{
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"points": 12,
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"details": [
{
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}
],
"max_points": 20
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{
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],
"max_points": 20
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]
}
],
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},
{
"key": "engineering",
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"weight": 0.2,
"metrics": [
{
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{
"key": "tests_present",
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{
"key": "linter_config",
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{
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{
"key": "editorconfig",
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{
"key": "openssf_scorecard_ci_tests",
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]
},
{
"key": "documentation",
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"note": null,
"notes": [],
"value": 55,
"inputs": {
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"components": [
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{
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{
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{
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"max_points": 10
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{
"key": "topics",
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"details": [],
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{
"key": "wiki",
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]
}
],
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},
{
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"value": 62,
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"notes": [
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"code": "excluded_no_data",
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{
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],
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"components": [
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"points": 7.5,
"status": "met",
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{
"key": "branch_protection",
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{
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{
"key": "cii_best_practices",
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"points": 0,
"status": "missed",
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},
{
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{
"key": "contributors",
"name": "Contributors",
"detail": "project has 1 contributing companies or organizations -- score normalized to 3",
"points": 0.8,
"status": "partial",
"details": [],
"max_points": 2.5
},
{
"key": "dangerous_workflow",
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"points": 10,
"status": "met",
"details": [],
"max_points": 10
},
{
"key": "dependency_update_tool",
"name": "Dependency-Update-Tool",
"detail": "update tool detected",
"points": 7.5,
"status": "met",
"details": [],
"max_points": 7.5
},
{
"key": "fuzzing",
"name": "Fuzzing",
"detail": "project is not fuzzed",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "license",
"name": "License",
"detail": "license file detected",
"points": 2.5,
"status": "met",
"details": [],
"max_points": 2.5
},
{
"key": "maintained",
"name": "Maintained",
"detail": "project was created within the last 90 days. Please review its contents carefully",
"points": 0,
"status": "missed",
"details": [],
"max_points": 7.5
},
{
"key": "packaging",
"name": "Packaging",
"detail": "packaging workflow not detected",
"points": 0,
"status": "excluded",
"details": [
{
"code": "no_data",
"params": {}
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],
"max_points": 5
},
{
"key": "pinned_dependencies",
"name": "Pinned-Dependencies",
"detail": "dependency not pinned by hash detected -- score normalized to 0",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"key": "sast",
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"detail": "SAST tool is not run on all commits -- score normalized to 0",
"points": 0,
"status": "missed",
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"max_points": 5
},
{
"key": "security_policy",
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"detail": "security policy file not detected",
"points": 0,
"status": "missed",
"details": [],
"max_points": 5
},
{
"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",
"details": [],
"max_points": 7.5
},
{
"key": "vulnerabilities",
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"detail": "2 existing vulnerabilities detected",
"points": 6,
"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": "advisories_scope_repository",
"params": {
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},
{
"code": "advisories_unassessed",
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"count": 10
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},
{
"code": "advisories_repo_graph_caveat",
"params": {}
},
{
"code": "advisories_reachability",
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}
],
"value": 100,
"inputs": {
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"affected_packages": 2,
"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,
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"details": [
{
"code": "no_direct_advisories",
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}
],
"max_points": 35
},
{
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"points": 0,
"status": "excluded",
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}
],
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},
{
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"details": [
{
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}
],
"max_points": 40
}
]
},
{
"key": "malicious_dependencies",
"band": "excellent",
"name": "Malicious dependencies",
"note": null,
"notes": [],
"value": 100,
"inputs": {
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"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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"red_flag": false,
"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": [
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"code": "no_malicious_dependencies",
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}
],
"max_points": 100
}
]
},
{
"key": "high_risk_jurisdiction_exposure",
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"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",
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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
},
"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": {
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"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",
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"code": "no_agent_instructions",
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}
],
"max_points": 45
},
{
"key": "machine_readable_docs_llms_txt",
"name": "Machine-readable docs (llms.txt)",
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"points": 0,
"status": "missed",
"details": [],
"max_points": 15
},
{
"key": "legible_commit_history",
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"detail": "65 of 98 human commits state their intent (structured subject or explanatory body)",
"points": 35.4,
"status": "partial",
"details": [
{
"code": "legible_history",
"params": {
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}
],
"max_points": 40
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]
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"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."
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Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.
Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v1.13.0, schema v0.23.0 — full methodology · metrics wiki.
