Add the remaining corrected @512 towers (campaign 2, 67 runs, _d512) and their selection plan; README section

README.md

@@ -1,62 +1,92 @@
----
-license: mit
-tags:
-- steam
-- game-reviews
-- representation-learning
-- set-encoder
----
-
-# latenarray-I2CE checkpoints
-
-The 110 five-fold set-encoder checkpoints ("towers") behind the I-CE manuscript's
-retrieval and tag numbers: the 105 runs of `contrast_experiment/w9/eval/rank_ckpt_plan.json`
-and the five folds of I + SimCLR in `contrast_experiment/w9/eval/i2bce_sel_pool.json`, each at
-the epoch the validation rank rule picked. Code: the I-CE code release on GitHub,
-[`Nice9Tian/I2CE-GameRepresention-by-SteamReviews`](https://github.com/Nice9Tian/I2CE-GameRepresention-by-SteamReviews).
-The data folder the towers read is the dataset
-[`chironYu/latenarray-i2ce-data`](https://huggingface.co/datasets/chironYu/latenarray-i2ce-data).
-
-A file is named `ckpt_w9cv_wcle_<arm>_fold<k>[_g<cap>][_<suffix>]_fp_ep<N>.pt`: the training
-arm, the fold of the five-fold split, the anchor budget it trained with (none = 512), and the
-epoch. Each file is a PyTorch state dict of `Pod/w9_cv_worker.py`'s `SetPoolN(4)` (keys `q0`,
-`attn.*`, `head.*`); the VICReg runs hold `{"model": ..., "expander": ...}`. Only tensors, no
-code objects, so `torch.load(path, weights_only=True)` reads them.
-
-Score them against the manuscript's ten 4,096-sentence anchor draws (from the repository root):
-
-```bash
-hf download chironYu/latenarray-i2ce-checkpoints --local-dir checkpoints
-export W9_DATA_DIR=/path/to/fusion_cache_w9
-python contrast_experiment/w9/eval/k10_eval.py cells  cells.json --ckpt-dir checkpoints contrast_experiment/w9/eval/i2bce_sel_pool.json
-python contrast_experiment/w9/eval/k10_eval.py encode cells.json draws.npz
-python contrast_experiment/w9/eval/k10_eval.py score  cells.json draws.npz scores.json
-python contrast_experiment/w9/eval/k10_eval.py table  scores.json
-```
-
-## Corrected @512 towers (`_d512`), added 2026-11-07
-
-The 512-sentence anchor packs that the original five-fold @512 runs trained on (`wscan_gal_rev.npz`
-of the dataset before revision `35cc5dd4`, kept there as `wscan_gal_rev_rawprefix.npz`) stored the
-store-page prefix rows without the per-row standardisation every other input receives (row std
-about 2.9 against 1.0 for review rows), and the tower has no input normalisation. Only the 512 budget
-was affected; the 1,024 to 4,096 packs were standardised.
-
-The I-CE and CE @512 towers were retrained on the corrected packs (`wscan_gal_rev.npz` from dataset
-revision `35cc5dd4` on, SHA-256 `7a304e875717a3cb9abffb554f460d75f20e8fea9d41b4cb1020f7096cafbe5b`: the same
-reviews, padding and lengths, only the prefix rows standardised) with the manuscript's recipe
-(`Pod/w9_cv_worker.py`, `--anchor-cap 512 --epochs 2000 --full-pool`, seed = fold, rank-rule selection).
-Their selected checkpoints carry the suffix `_d512`, for example
-`ckpt_w9cv_wcle_i2ce_icetf_fold0_d512_fp_ep1400.pt`, and are listed in `docfix512_ckpt_plan.json`.
-The original @512 checkpoints are unchanged.
-
-Read against the manuscript's ten 4,096-sentence draws (five-fold mean ± std, Stripped hit@1):
-
-| Tower | original @512 | corrected @512 (`_d512`) |
-| --- | --- | --- |
-| I-CE | 0.669 ± 0.031 | 0.715 ± 0.010 |
-| CE | 0.638 ± 0.016 | 0.676 ± 0.013 |
-
-`docfix512_ckpt_plan.json` has the same entry format as `rank_ckpt_plan.json`, so it can be passed
-to `k10_eval.py cells` as an extra selection file. The other @512 cells of the manuscript are being
-retrained on the corrected packs and will be added under the same `_d512` suffix.
+---
+license: mit
+tags:
+- steam
+- game-reviews
+- representation-learning
+- set-encoder
+---
+
+# latenarray-I2CE checkpoints
+
+The 110 five-fold set-encoder checkpoints ("towers") behind the I-CE manuscript's
+retrieval and tag numbers: the 105 runs of `contrast_experiment/w9/eval/rank_ckpt_plan.json`
+and the five folds of I + SimCLR in `contrast_experiment/w9/eval/i2bce_sel_pool.json`, each at
+the epoch the validation rank rule picked. Code: the I-CE code release on GitHub,
+[`Nice9Tian/I2CE-GameRepresention-by-SteamReviews`](https://github.com/Nice9Tian/I2CE-GameRepresention-by-SteamReviews).
+The data folder the towers read is the dataset
+[`chironYu/latenarray-i2ce-data`](https://huggingface.co/datasets/chironYu/latenarray-i2ce-data).
+
+A file is named `ckpt_w9cv_wcle_<arm>_fold<k>[_g<cap>][_<suffix>]_fp_ep<N>.pt`: the training
+arm, the fold of the five-fold split, the anchor budget it trained with (none = 512), and the
+epoch. Each file is a PyTorch state dict of `Pod/w9_cv_worker.py`'s `SetPoolN(4)` (keys `q0`,
+`attn.*`, `head.*`); the VICReg runs hold `{"model": ..., "expander": ...}`. Only tensors, no
+code objects, so `torch.load(path, weights_only=True)` reads them.
+
+Score them against the manuscript's ten 4,096-sentence anchor draws (from the repository root):
+
+```bash
+hf download chironYu/latenarray-i2ce-checkpoints --local-dir checkpoints
+export W9_DATA_DIR=/path/to/fusion_cache_w9
+python contrast_experiment/w9/eval/k10_eval.py cells  cells.json --ckpt-dir checkpoints contrast_experiment/w9/eval/i2bce_sel_pool.json
+python contrast_experiment/w9/eval/k10_eval.py encode cells.json draws.npz
+python contrast_experiment/w9/eval/k10_eval.py score  cells.json draws.npz scores.json
+python contrast_experiment/w9/eval/k10_eval.py table  scores.json
+```
+
+## Corrected @512 towers (`_d512`), added 2026-11-07
+
+The 512-sentence anchor packs that the original five-fold @512 runs trained on (`wscan_gal_rev.npz`
+of the dataset before revision `35cc5dd4`, kept there as `wscan_gal_rev_rawprefix.npz`) stored the
+store-page prefix rows without the per-row standardisation every other input receives (row std
+about 2.9 against 1.0 for review rows), and the tower has no input normalisation. Only the 512 budget
+was affected; the 1,024 to 4,096 packs were standardised.
+
+The I-CE and CE @512 towers were retrained on the corrected packs (`wscan_gal_rev.npz` from dataset
+revision `35cc5dd4` on, SHA-256 `7a304e875717a3cb9abffb554f460d75f20e8fea9d41b4cb1020f7096cafbe5b`: the same
+reviews, padding and lengths, only the prefix rows standardised) with the manuscript's recipe
+(`Pod/w9_cv_worker.py`, `--anchor-cap 512 --epochs 2000 --full-pool`, seed = fold, rank-rule selection).
+Their selected checkpoints carry the suffix `_d512`, for example
+`ckpt_w9cv_wcle_i2ce_icetf_fold0_d512_fp_ep1400.pt`, and are listed in `docfix512_ckpt_plan.json`.
+The original @512 checkpoints are unchanged.
+
+Read against the manuscript's ten 4,096-sentence draws (five-fold mean ± std, Stripped hit@1):
+
+| Tower | original @512 | corrected @512 (`_d512`) |
+| --- | --- | --- |
+| I-CE | 0.669 ± 0.031 | 0.715 ± 0.010 |
+| CE | 0.638 ± 0.016 | 0.676 ± 0.013 |
+
+`docfix512_ckpt_plan.json` has the same entry format as `rank_ckpt_plan.json`, so it can be passed
+to `k10_eval.py cells` as an extra selection file. The other @512 cells of the manuscript were retrained
+the next day; see the following section.
+
+## Remaining corrected @512 towers (campaign 2), added 2026-11-08
+
+Every other manuscript cell whose training loss reads the 512-sentence anchors was retrained on the
+corrected packs, under the same recipe and suffix; 67 runs, the selected checkpoint of each listed
+in `c2_docfix512_ckpt_plan.json` (same entry format, plus the file's SHA-256 and its rank score).
+
+- Five folds (`Pod/w9_cv_worker.py`, seed = fold, 2,000 epochs): CE + anchor-in-I (`ai2ce_icetf`), no
+  document view (`nodoc_i2ce_icetf`), LLM-rewritten documents (`i2ce_icetf ... _wllm`), τ = 0.05 and
+  0.10 (`i2cet05_icetf`, `i2cet10_icetf`), views-first anchors (`vfai2ce_icetf`).
+- Folds 0 to 2 only: the invariance weight λ = 1, 3, 4 (`ice_icetf`, `i3ce_icetf`, `i4ce_icetf`), the view
+  count V = 3, 5, 6, 7 and V = 6 with λ = 3 (`d1r{2,4,5,6}_i2ce_icetf`, `d1r5_i3ce_icetf`), and 8 or 16
+  latent queries (`slot8i2cemean_icetf`, `slot16i2cemean_icetf`). Folds 3 and 4 are not trained.
+- Fixed split (`Pod/w9_a100_worker.py`, seed 0, the original run's epoch count): I-CE, CE and
+  CE + anchor-in-I of Table 1 and the four alignment-plus-uniformity runs aimed at the anchor gallery
+  (`ai25auni2_icetf`, `ai6auni2_icetf`, `ai4auni2_icetf`, `ai2auni25_icetf`); keys end in `_fixed_d512`.
+
+Runs whose loss never reads the anchors (VICReg, the SimCLR-style tower, BYOL, I + SimCLR and alignment
+plus uniformity at the batch views) train the same weights on either pack, which a three-step A/B on
+both packs confirmed, so their original checkpoints stand; only readouts that used the 512-sentence
+packs were redone. Selected Stripped hit@1 on the ten 4,096-sentence draws (five-fold mean ± std):
+
+| Tower | original @512 | corrected @512 (`_d512`) |
+| --- | --- | --- |
+| CE + anchor-in-I | 0.663 ± 0.014 | 0.725 ± 0.015 |
+| I-CE, no document view | 0.650 ± 0.022 | 0.683 ± 0.025 |
+| I-CE, LLM-rewritten documents | 0.680 ± 0.034 | 0.711 ± 0.009 |
+| τ = 0.05 | 0.601 ± 0.029 | 0.642 ± 0.026 |
+| τ = 0.10 | 0.510 ± 0.022 (four folds) | 0.520 ± 0.015 |
+

c2_docfix512_ckpt_plan.json

@@ -0,0 +1,739 @@
+{
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+  "fold": 0,
+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 1450,
+  "ckpt": "ckpt_w9cv_wcle_ai2ce_icetf_fold0_d512_fp_ep1450.pt",
+  "sha256": "6619e0ba5ca4ec5de8504fee9d3ea0eb010d69b2ee03d7bfec62ec87c1424e6b",
+  "ranksel": 103.10678848667865
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+  "worker": "Pod/w9_cv_worker.py",
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+  "worker": "Pod/w9_cv_worker.py",
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+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 1100,
+  "ckpt": "ckpt_w9cv_wcle_d1r6_i2ce_icetf_fold1_d512_fp_ep1100.pt",
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+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 1400,
+  "ckpt": "ckpt_w9cv_wcle_d1r6_i2ce_icetf_fold2_d512_fp_ep1400.pt",
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+  "protocol": "five-fold (seed = fold)",
+  "worker": "Pod/w9_cv_worker.py",
+  "sfx": "_d512",
+  "ep": 1050,
+  "ckpt": "ckpt_w9cv_wcle_i2ce_icetf_fold0_wllm_d512_fp_ep1050.pt",
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