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65 changes: 41 additions & 24 deletions docs/CHECKPOINT_LICENSES.md
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ local checkpoint are marked `TBD; compute after download`.
| TUTA | MIT | YES, attribution required | Mirror |
| TaBERT | CC BY-NC 4.0 | NON-COMMERCIAL ONLY | Mirror under NC tag (TRL-Bench is academic) |
| TURL | Apache-2.0 | YES, attribution required | Mirror |
| TabSketchFM | CC BY-NC-ND 4.0 | NO (no derivatives) | Document upstream URL only |
| TabSketchFM | CC BY-NC-ND 4.0 (code) | WEIGHTS ARE OURS | Mirror our own trained weights (no upstream ckpt exists) |
| Starmie | No LICENSE file | No (upstream-only) | Document upstream URL only (user retrains) |
| TABBIE | MIT (SFIG611 fork) | YES, attribution required | Document upstream URL only |

Expand Down Expand Up @@ -121,31 +121,46 @@ local checkpoint are marked `TBD; compute after download`.
### TabSketchFM

- **Upstream repo:** https://github.com/IBM/tabsketchfm
- **License:** Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International (CC BY-NC-ND 4.0). GitHub returns NOASSERTION/Other for the
- **License (code):** Creative Commons Attribution-NonCommercial-NoDerivatives
4.0 International (CC BY-NC-ND 4.0). GitHub returns NOASSERTION/Other for the
SPDX classifier; the upstream README explicitly states "This code is released
with CC BY-NC-ND 4.0 License" plus an additional restrictive-use clause from
IBM.
- **Re-host permitted:** NO. The ND clause forbids sharing of adapted material;
a fine-tuned or repacked checkpoint can plausibly be considered an adaptation.
Commercial use is also prohibited. The additional IBM clause ("only for the
purpose of comparing this code to other code for scientific experimental
purposes, where that distribution is not for a fee") further restricts use.
with CC BY-NC-ND 4.0 License" plus an additional IBM clause granting the right
to "copy, modify and distribute this code only for the purpose of comparing
this code to other code for scientific experimental purposes, where that
distribution is not for a fee".
- **Upstream checkpoint:** NONE. The TabSketchFM authors did not release
pretrained weights or a pretraining corpus. Earlier revisions of this document
attributed the checkpoint below to IBM and directed users to the LakeBench
Zenodo record; that attribution was incorrect, and following it made the
paper's TabSketchFM results impossible to reproduce.
- **Checkpoint provenance:** Both checkpoints recorded here were trained from
scratch by the TRL-Bench authors using the upstream `pretrain.py`, initialized
from `bert-base-uncased` (Apache-2.0), over tables fetched from the source URLs
that upstream publishes in `pretraining_tables.txt` (127,934 open-data URLs,
primarily government open-data portals). The corpus was not retained.
Confirmed from checkpoint metadata: the file records `pytorch-lightning 2.5.6`,
a release postdating the upstream paper (arXiv 2407.01619, July 2024), so it
cannot be an upstream artefact.
- **Re-host permitted:** YES, for the weights. They are our own training output
rather than a copy of any IBM-distributed artefact, so the ND clause — which
governs redistribution of *code* — is not engaged by publishing them. Hosting
is also required for reproducibility, since no upstream checkpoint exists.
Source code is NOT redistributed: the mirror carries weights only, and users
obtain the code from the upstream repository under the upstream license.
Comment on lines +144 to +149
- **Attribution text:**
- Retain creator attribution to IBM and full CC BY-NC-ND 4.0 notice; do not
modify or repack the checkpoint.
- Retain attribution to IBM for the architecture, training code, and method,
and the full CC BY-NC-ND 4.0 notice covering the code.
- State explicitly that the weights are not an official IBM release.
- Cite: Khatiwada, A. et al. "TabSketchFM: Sketch-based Tabular Representation
Learning for Data Discovery over Data Lakes." IEEE ICDE 2025. arXiv:2407.01619.
- **Checkpoint URL (upstream):** Pretrained `.ckpt` is referenced from the
upstream README; downloads are linked from
https://github.com/IBM/tabsketchfm and the
TabSketchFM Zenodo record (LakeBench): https://doi.org/10.5281/zenodo.8014642
- **SHA256:**
- `checkpoints/tabsketchfm/epoch=10-step=27786.ckpt`: `26f2107d7640bf9485026ff643ff83e569e412572aa68d540d88494f7f9f211d`
- `checkpoints/tabsketchfm/epoch=15-step=12112.ckpt`: `90d38c0d8f22d171b759758a782314460f1dec8fc5cfc44e1cfa32e943500525`
- **Decision:** Document upstream URL only. Do not mirror to
`logo-lab/trl-bench-ckpts/`. Users must obtain the checkpoint directly from
IBM under the upstream license.
- **Decision:** Mirror `epoch=10-step=27786.ckpt` — the checkpoint behind the
paper's TabSketchFM results — to `logo-lab/trl-bench-ckpts/tabsketchfm/`,
with per-model `LICENSE` and `NOTICE` recording the provenance above (uploaded
2026-08-04; remote SHA256 verified against local). `epoch=15-step=12112.ckpt`
is also ours but is not currently mirrored.

---

Expand Down Expand Up @@ -213,10 +228,12 @@ local checkpoint are marked `TBD; compute after download`.
TRL-Bench uses a user-trained Starmie flow (`run_pretrain.py`, per-dataset).
2. **TABBIE** — weights are obtained from the upstream `SFIG611/tabbie` source
and are not redistributed here.
3. **TabSketchFM mirror policy** — has CC BY-NC-ND 4.0 (no-derivatives);
stays upstream-only. TaBERT (CC BY-NC 4.0, was previously also
upstream-only) is now mirrored on logo-lab/trl-bench-ckpts under the
non-commercial inheritance described in its section above.
3. **TabSketchFM is no longer in this category.** Upstream released no
checkpoint at all, and the weights TRL-Bench uses were trained by us; they
are now mirrored on logo-lab/trl-bench-ckpts (weights only — no upstream
code is redistributed). See its section above. TaBERT (CC BY-NC 4.0, was
previously also upstream-only) is likewise mirrored under the
non-commercial inheritance described in its section.
Comment on lines +231 to +236

## Per-wrapper "where to place it" (for `--checkpoint-root <root>`)

Expand All @@ -231,6 +248,6 @@ the expected on-disk layout is:
| TaBERT | `tabert/tabert_base_k3/model.bin` | HF: `logo-lab/trl-bench-ckpts` (NC mirror) OR upstream Google Drive |
| TURL | `turl/pretrained/{pytorch_model.bin,config.json}` | HF: `logo-lab/trl-bench-ckpts` (auto-fetched) |
| TUTA | `tuta/tuta.bin` | HF: `logo-lab/trl-bench-ckpts` (auto-fetched) |
| TabSketchFM | `tabsketchfm/epoch=10-step=27786.ckpt` | MANUAL: https://doi.org/10.5281/zenodo.8014642 |
| TabSketchFM | `tabsketchfm/epoch=10-step=27786.ckpt` | HF: `logo-lab/trl-bench-ckpts` (TRL-Bench-trained weights; no upstream ckpt exists) |
| TABBIE | `tabbie/weights.pt` | MANUAL: SFIG611/tabbie Google Drive |
| Starmie | `starmie/<dataset>/model_drop_col,sample_row_head_column_0.pt` | RETRAIN: `python -m trl_bench.models.starmie.run_pretrain --data_path <dataset>` per-dataset |
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