Commit ·
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Parent(s):
Release Kumo Tabular
Browse files- .gitattributes +35 -0
- LICENSE +52 -0
- README.md +49 -0
- large/classifier.pt +3 -0
- large/regressor.pt +3 -0
- medium/classifier.pt +3 -0
- medium/regressor.pt +3 -0
- small/classifier.pt +3 -0
- small/regressor.pt +3 -0
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LICENSE
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Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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OpenMDW License Agreement, version 1.1 (OpenMDW-1.1)
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By exercising rights granted to you under this agreement, you accept and agree
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to its terms.
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As used in this agreement, "Model Materials" means the materials provided to
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you under this agreement, consisting of: (1) one or more machine learning
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models (including architecture and parameters); and (2) all related artifacts
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(including associated data, documentation and software) that are provided to
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you hereunder.
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Subject to your compliance with this agreement, permission is hereby granted,
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free of charge, to deal in the Model Materials without restriction, including
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under all copyright, patent, database, and trade secret rights included or
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embodied therein.
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If you distribute any portion of the Model Materials, you shall retain in your
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distribution (1) a copy of this agreement, and (2) all copyright notices and
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other notices of origin included in the Model Materials that are applicable to
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your distribution.
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If you file, maintain, or voluntarily participate in a lawsuit against any
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person or entity asserting that the Model Materials directly or indirectly
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infringe any patent or copyright, then all rights and grants made to you
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hereunder are terminated, unless that lawsuit was in response to a
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corresponding lawsuit first brought against you.
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This agreement does not impose any restrictions or obligations with respect to
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any use, modification, or sharing of any outputs generated by using the Model
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Materials.
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THE MODEL MATERIALS ARE PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
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OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE, TITLE, NONINFRINGEMENT, ACCURACY, OR THE
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ABSENCE OF LATENT OR OTHER DEFECTS OR ERRORS, WHETHER OR NOT DISCOVERABLE, ALL
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TO THE GREATEST EXTENT PERMISSIBLE UNDER APPLICABLE LAW.
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YOU ARE SOLELY RESPONSIBLE FOR (1) CLEARING RIGHTS OF OTHER PERSONS THAT MAY
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APPLY TO THE MODEL MATERIALS OR ANY USE THEREOF, INCLUDING WITHOUT LIMITATION
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ANY PERSON'S COPYRIGHTS OR OTHER RIGHTS INCLUDED OR EMBODIED IN THE MODEL
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MATERIALS; (2) OBTAINING ANY NECESSARY CONSENTS, PERMISSIONS OR OTHER RIGHTS
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REQUIRED FOR ANY USE OF THE MODEL MATERIALS; OR (3) PERFORMING ANY DUE
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DILIGENCE OR UNDERTAKING ANY OTHER INVESTIGATIONS INTO THE MODEL MATERIALS OR
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ANYTHING INCORPORATED OR EMBODIED THEREIN.
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IN NO EVENT SHALL THE PROVIDERS OF THE MODEL MATERIALS BE LIABLE FOR ANY CLAIM,
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DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
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OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE MODEL MATERIALS, THE
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USE THEREOF OR OTHER DEALINGS THEREIN.
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README.md
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---
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license: openmdw-1.1
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tags:
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- tabular-foundation-model
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- structured-data-models
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---
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# Kumo Tabular
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Kumo Tabular is NVIDIA's pretrained tabular foundation model for classification and regression.
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## Getting Started
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Install [`structured-data-models`](https://github.com/NVIDIA/structured-data-models) for inference:
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```bash
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pip install structured-data-models
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```
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Use labeled examples as context to predict class probabilities for new data:
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```python
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from sklearn.datasets import load_breast_cancer
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import sdm
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df = load_breast_cancer(as_frame=True).frame
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table = sdm.TableTensor.from_pandas(
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df=df,
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stypes=sdm.infer_stypes(df, overrides={"target": "categorical"}),
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device="cuda",
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)
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model = sdm.models.KumoTabular(task="classification", device="cuda")
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probs = model(
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x_context=table[:300].drop_columns("target"),
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y_context=table[:300, "target"],
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x_query=table[300:].drop_columns("target"),
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num_estimators=8,
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)
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print(probs)
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```
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To learn more, visit [structured-data-models](https://github.com/NVIDIA/structured-data-models).
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## License
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Kumo Tabular weights are released under OpenMDW 1.1.
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large/classifier.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:cc21484316949641cc3d65a7bb6c36cdea854cc569435755ff23b850018ec613
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size 854850034
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large/regressor.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:745e0e5d97e4256d8a24cb61c2eecbd1128da6c83938c196b7589ea6f62e7421
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size 862909259
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medium/classifier.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:70d6aee8fd13416300afead71a5145a40edb790952e2417d87f27b3f7e57793d
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size 246117940
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medium/regressor.pt
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version https://git-lfs.github.com/spec/v1
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size 250144651
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small/classifier.pt
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version https://git-lfs.github.com/spec/v1
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size 109936978
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small/regressor.pt
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version https://git-lfs.github.com/spec/v1
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size 113968523
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