---
license: mit
pretty_name: latenarray-I2CE data cache
tags:
- steam
- game-reviews
- sentence-embeddings
- representation-learning
size_categories:
- 10M<n<100M
---

# latenarray-I2CE data cache

The precomputed tensors behind the paper *Invariant Game Representation Learning from Steam
Reviews* (AICCC 2026): cached sentence embeddings of Steam reviews, store pages and wiki
articles for 2,020 games, with the anchor packs, the evaluation queries and the splits.

- Code, appendices and supplement:
  [`Nice9Tian/I2CE-GameRepresention-by-SteamReviews`](https://github.com/Nice9Tian/I2CE-GameRepresention-by-SteamReviews)
- The trained towers:
  [`chironYu/latenarray-i2ce-checkpoints`](https://huggingface.co/chironYu/latenarray-i2ce-checkpoints)

The w9 workers of the code repository (`contrast_experiment/w9/Pod/w9_cv_worker.py`,
`w9_a100_worker.py`) read this folder as their `--data-dir`. Download it, rebuild the full pool,
and pass the folder as `--data-dir`:

```bash
hf download chironYu/latenarray-i2ce-data --repo-type dataset --local-dir latenarray-i2ce-data --exclude "*_rawprefix.*"
cd latenarray-i2ce-data && python join_full_pool.py
```

The `--exclude` leaves out two files (10 GiB) that only repeating a run made before 2026-11-07
needs; see *Changes*.

`full_pool_fp16.npy` (150.1 GB) is stored as four byte parts under 50 GB each,
`full_pool_fp16.npy.part00` to `part03`. `join_full_pool.py` concatenates them, checks the
result against `SHA256SUMS`, writes `full_pool_READY` (the marker the w9 notebooks wait for)
and deletes the parts (`--keep-parts` keeps them).

The packaged entry points of the code release (`steam_reviews_framework/run.py`,
`contrast_experiment/run.py`) need neither the full pool nor the two `wscan_gal_rev_g*.npz`
galleries, about 21 GiB in all; they rebuild the anchor packs at their budget from
`wscan_pool_rev.npy` and `full_pool_meta.npz`:

```bash
hf download chironYu/latenarray-i2ce-data --repo-type dataset --local-dir data/assets \
  --exclude "full_pool_fp16.npy.part*" "wscan_gal_rev_g*.npz" "*_rawprefix.*"
```

The corpus covers 2,020 Steam games released 2020 to 2024: 6,649,325 reviews and
73,289,517 sentences. Every sentence is embedded once with Qwen/Qwen3-Embedding-0.6B
(1,024-d, last-token pooling, no instruction prefix, not normalized) and stored as float16.
The repository holds no review text, only vectors, offsets and identifiers.

Sentence vectors are stored raw in the full pool, the document views and the wiki queries; the
workers row-standardise them (mean 0, std 1 over the 1,024 dimensions) when they load them. The
review pool, the anchor packs and the pseudo-queries are stored already row-standardised.

| File | Contents |
|---|---|
| `full_pool_fp16.npy` | All 73,289,517 sentence vectors, `[73289517, 1024]` float16 (139.8 GiB). Training views are drawn from it (`--full-pool`); the 4,096-sentence evaluation draws read it as well |
| `full_pool_meta.npz` | `game_names` (2,020), `game_review_offsets` (2,021), `review_offsets` (6,649,326): game g owns reviews `game_review_offsets[g]:[g+1]`, review r owns sentence rows `review_offsets[r]:[r+1]` |
| `wscan_pool_rev.npy` | The 2,048-sentence review pool per game, `[2020, 2048, 1024]` float16: the 3 longest reviews of the game first (when they fit), then whole reviews at random; anchor packs above 512 sentences are rebuilt from it |
| `wscan_pool_rev_len.npy`, `wscan_pool_rev_rid.npy` | Pool lengths and review ids |
| `wscan_gal_rev.npz`, `wscan_gal_rev_g1024.npz`, `wscan_gal_rev_g4096.npz` | Prebuilt anchor galleries at 512, 1,024 and 4,096 sentences (the 4,096 one is a single draw used by the fixed-split worker): store-page prefix (`gal_doc_len` rows), then whole reviews; `gal_len` rows used. See *Changes* for the 512 one |
| `wscan_gal_rev_rawprefix.npz` | The 512-sentence galleries as built before 2026-11-07, store-page prefix not row-standardised. Only needed to repeat a run of that time: the w9 workers read it with `--raw-prefix` |
| `games.npz` | Game names (`names`, the game index of every other file) and per-game assets |
| `wiki_eval.npz`, `wiki_eval_split.json` | The evaluation queries: LLM rewrites of the wiki articles of the 814-game evaluation universe, four variants each (`neutral`, `noname`, `positive`, `negative`), and the split (seed 20260711) |
| `wiki_clean_views.npz`, `wiki_llm_views.npz`, `sp_raw_views.npz` | Document views: cleaned Wikipedia, LLM rewrites, store pages |
| `ss_queries_rev.npz`, `ss_queries_rev_S.npy` | Pseudo-queries: four anchor-shaped packs per game (store-page prefix, then whole pool reviews to 512 sentences), flat-stored, query j = `S[off[j]:off[j+1]]`. Report-only diagnostics; checkpoint selection and every published number use `wiki_eval.npz` instead |
| `ss_queries_rev_S_rawprefix.npy` | The pseudo-query vectors before the prefix fix (see *Changes*) |
| `tag_labels.npz`, `_tag_splitM.json` | Tag labels (23 coarse classes) and the tag-probe split |

## Changes

**2026-11-07: store-page prefix row-standardised in `wscan_gal_rev.npz` and `ss_queries_rev_S.npy`.**
Both files put a game's `sp_raw` store-page sentences in front of its review sentences. The
review sentences were row-standardised; the store-page sentences were copied raw (row std about
2.9 against 1.0), and the tower has no input normalisation. Every other input already reached it
standardised, including the store-page prefix of the 1,024- and 4,096-sentence galleries and of
the galleries the workers build on the GPU at 1,024 and 2,048 sentences. The two files now carry
the prefix row-standardised exactly as the workers standardise `sp_raw_views.npz` when they load
it; every review row, every length and the order are unchanged, bit for bit. All other files are
unchanged.

The paper's 512-sentence rows come from towers trained on the current files; their checkpoints
carry `_d512` in their names, the w9 workers name a run on the current packs that way, and they
stop when the `wscan_gal_rev.npz` they are given still holds the prefix raw. The files as they
were are kept as `wscan_gal_rev_rawprefix.npz` and `ss_queries_rev_S_rawprefix.npy`, for repeating
a run made before the change: pass the worker `--raw-prefix`, which reads the two files and names
the run without the tag.

| File | SHA-256 before | SHA-256 now |
|---|---|---|
| `wscan_gal_rev.npz` | `e4ee5da46c5d7240065f02a3948c51c78ca9fe22d3145613c752589b4b8f789e` (now `wscan_gal_rev_rawprefix.npz`) | `7a304e875717a3cb9abffb554f460d75f20e8fea9d41b4cb1020f7096cafbe5b` |
| `ss_queries_rev_S.npy` | `2762f528c70173f2613aad397d14f2f3abf3e1ca453d5441551ac911abf37dd4` (now `ss_queries_rev_S_rawprefix.npy`) | `413ebc66c15568ede34ecf2d7f1886a3420252e5533dfbb27c2db13efe247f67` |

## Citation

Zhilong Yu et al. *Invariant Game Representation Learning from Steam Reviews.* 2026 9th Artificial
Intelligence and Cloud Computing Conference (AICCC 2026), December 18 to 21, 2026, Tokyo, Japan.

```bibtex
@inproceedings{yu2026invariant,
  author    = {Yu, Zhilong and others},
  title     = {Invariant Game Representation Learning from Steam Reviews},
  booktitle = {2026 9th Artificial Intelligence and Cloud Computing Conference (AICCC 2026)},
  address   = {Tokyo, Japan},
  year      = {2026}
}
```

The tensors are under the MIT license.