Publish 1895 files, replacing 825 cells

.gitattributes

@@ -60,3 +60,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
 modelblocks_kenlm_models/openwebtext.1.fw.kenlm filter=lfs diff=lfs merge=lfs -text
 owt/unigram_counts.tsv filter=lfs diff=lfs merge=lfs -text
+verification/wave1-tokens.json filter=lfs diff=lfs merge=lfs -text
+verification/wave2.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,5 +1,5 @@
 ---
-pretty_name: Per-word reading predictors
+pretty_name: REPLICA predictor store
 tags:
   - psycholinguistics
   - reading-times
@@ -8,6 +8,8 @@ tags:
 configs:
   - config_name: tags
     data_files: tags.parquet
+  - config_name: coverage
+    data_files: coverage.parquet
   - config_name: brothers_kuperberg
     data_files: brothers_kuperberg.parquet
   - config_name: brown_spr
@@ -16,6 +18,20 @@ configs:
     data_files: bsc.parquet
   - config_name: celer
     data_files: celer.parquet
+  - config_name: characters_celer
+    data_files: characters/celer.parquet
+  - config_name: characters_geco
+    data_files: characters/geco.parquet
+  - config_name: characters_meco_l1_w2_ch_s
+    data_files: characters/meco_l1_w2_ch_s.parquet
+  - config_name: characters_meco_l1_w2_ch_t
+    data_files: characters/meco_l1_w2_ch_t.parquet
+  - config_name: characters_provo
+    data_files: characters/provo.parquet
+  - config_name: characters_sbsat
+    data_files: characters/sbsat.parquet
+  - config_name: characters_zuco1
+    data_files: characters/zuco1.parquet
   - config_name: copco
     data_files: copco.parquet
   - config_name: devarda2023
@@ -30,30 +46,6 @@ configs:
     data_files: geco.parquet
   - config_name: hubbard2019_n400
     data_files: hubbard2019_n400.parquet
-  - config_name: meco_char_de
-    data_files: meco_char_de.parquet
-  - config_name: meco_char_du
-    data_files: meco_char_du.parquet
-  - config_name: meco_char_ee
-    data_files: meco_char_ee.parquet
-  - config_name: meco_char_en
-    data_files: meco_char_en.parquet
-  - config_name: meco_char_fi
-    data_files: meco_char_fi.parquet
-  - config_name: meco_char_he
-    data_files: meco_char_he.parquet
-  - config_name: meco_char_it
-    data_files: meco_char_it.parquet
-  - config_name: meco_char_ko
-    data_files: meco_char_ko.parquet
-  - config_name: meco_char_no
-    data_files: meco_char_no.parquet
-  - config_name: meco_char_ru
-    data_files: meco_char_ru.parquet
-  - config_name: meco_char_sp
-    data_files: meco_char_sp.parquet
-  - config_name: meco_char_tr
-    data_files: meco_char_tr.parquet
   - config_name: meco_de
     data_files: meco_de.parquet
   - config_name: meco_du
@@ -136,6 +128,76 @@ configs:
     data_files: szewczyk_federmeier2022_n400.parquet
   - config_name: szewczyk_n400
     data_files: szewczyk_n400.parquet
+  - config_name: tokens_celer
+    data_files: tokens/celer.parquet
+  - config_name: tokens_copco
+    data_files: tokens/copco.parquet
+  - config_name: tokens_dundee
+    data_files: tokens/dundee.parquet
+  - config_name: tokens_federmeier2007_n400
+    data_files: tokens/federmeier2007_n400.parquet
+  - config_name: tokens_geco
+    data_files: tokens/geco.parquet
+  - config_name: tokens_meco_de
+    data_files: tokens/meco_de.parquet
+  - config_name: tokens_meco_ee
+    data_files: tokens/meco_ee.parquet
+  - config_name: tokens_meco_fi
+    data_files: tokens/meco_fi.parquet
+  - config_name: tokens_meco_gr
+    data_files: tokens/meco_gr.parquet
+  - config_name: tokens_meco_he
+    data_files: tokens/meco_he.parquet
+  - config_name: tokens_meco_it
+    data_files: tokens/meco_it.parquet
+  - config_name: tokens_meco_ko
+    data_files: tokens/meco_ko.parquet
+  - config_name: tokens_meco_l1_w2_ba
+    data_files: tokens/meco_l1_w2_ba.parquet
+  - config_name: tokens_meco_l1_w2_bp
+    data_files: tokens/meco_l1_w2_bp.parquet
+  - config_name: tokens_meco_l1_w2_ch_s
+    data_files: tokens/meco_l1_w2_ch_s.parquet
+  - config_name: tokens_meco_l1_w2_ch_t
+    data_files: tokens/meco_l1_w2_ch_t.parquet
+  - config_name: tokens_meco_l1_w2_ge_po
+    data_files: tokens/meco_l1_w2_ge_po.parquet
+  - config_name: tokens_meco_l1_w2_ge_zu
+    data_files: tokens/meco_l1_w2_ge_zu.parquet
+  - config_name: tokens_meco_l1_w2_hi_iiith
+    data_files: tokens/meco_l1_w2_hi_iiith.parquet
+  - config_name: tokens_meco_l1_w2_hi_iitk
+    data_files: tokens/meco_l1_w2_hi_iitk.parquet
+  - config_name: tokens_meco_l1_w2_ic
+    data_files: tokens/meco_l1_w2_ic.parquet
+  - config_name: tokens_meco_l1_w2_tr
+    data_files: tokens/meco_l1_w2_tr.parquet
+  - config_name: tokens_meco_l2_w2
+    data_files: tokens/meco_l2_w2.parquet
+  - config_name: tokens_meco_ru
+    data_files: tokens/meco_ru.parquet
+  - config_name: tokens_meco_tr
+    data_files: tokens/meco_tr.parquet
+  - config_name: tokens_natural_stories
+    data_files: tokens/natural_stories.parquet
+  - config_name: tokens_natural_stories_maze
+    data_files: tokens/natural_stories_maze.parquet
+  - config_name: tokens_onestop
+    data_files: tokens/onestop.parquet
+  - config_name: tokens_potec
+    data_files: tokens/potec.parquet
+  - config_name: tokens_provo
+    data_files: tokens/provo.parquet
+  - config_name: tokens_sbsat
+    data_files: tokens/sbsat.parquet
+  - config_name: tokens_szewczyk_federmeier2022_n400
+    data_files: tokens/szewczyk_federmeier2022_n400.parquet
+  - config_name: tokens_szewczyk_n400
+    data_files: tokens/szewczyk_n400.parquet
+  - config_name: tokens_wlotko_federmeier2012_n400
+    data_files: tokens/wlotko_federmeier2012_n400.parquet
+  - config_name: tokens_zuco1
+    data_files: tokens/zuco1.parquet
   - config_name: ucl_et
     data_files: ucl_et.parquet
   - config_name: ucl_spr
@@ -150,540 +212,250 @@ configs:
     data_files: zuco_n400.parquet
 ---
 
-# Per-word reading predictors
+# REPLICA predictor store
 
-Precomputed per-word predictors for computational models of human sentence
-processing: word surprisal, several kinds of entropy, and unigram surprisal,
-scored over reading corpora (eye tracking, self-paced reading, N400, maze).
+Per-unit predictors for computational models of human sentence processing:
+surprisal, several entropies, similarity-adjusted surprisal, information
+value and unigram surprisal, scored by language models over reading
+corpora (eye tracking, self-paced reading, maze, N400). Every row names the
+text unit it describes and the unit its number is in.
 
-Every row says what text unit it describes and what unit its number is in:
+| `dataset` | `tag` | `predictor` | `stimulus_id` | `unit_index` | `text_unit` | `value` | `value_unit` | `layer` | `storage_class` | `unit_rule` |
+|---|---|---|---|---|---|---|---|---|---|---|
+| provo | `gpt2_surprisal_provo` | `surprisal` | provo_1 | 4 | Apple | 8.4213 | bits | 1 | positional | |
 
-| `dataset` | `tag` | `predictor` | `stimulus_id` | `unit_index` | `text_unit` | `value` | `value_unit` |
-|---|---|---|---|---|---|---|---|
-| provo | `gpt2_ft_entropy_provo` | `token_entropy` | provo_1 | 4 | Apple | 8.4213 | bits |
+Start from `tags.parquet`: one row per (`tag`, `predictor`, file), with the
+model, method, configuration, storage class, layer and provenance of every
+cell decoded, so a cell can be found by model, method and configuration.
 
-Counts below describe cells indexed in `tags.parquet`. Cells are unique tags.
-Rows sum `n_rows` over (`tag`, `predictor`, file), so one word can contribute
-to several predictor rows.
+- 2,596 cells across 58 corpora, 280,030,555 scalar rows
+- 25 methods, 26 models and frequency tables
 
-<!-- TABLE:totals -->
-- **2,111 cells** across **58 corpora**, **142,164,743 scored rows**
-- 25 methods x 26 models x 158 predictor keys
-<!-- /TABLE:totals -->
+## Layout
 
-## Start here: `tags.parquet`
-
-One row per (`tag`, `predictor`, file), with the model, method and configuration
-decoded out of the tag, so you can find a cell by what it is instead of by
-guessing at its name.
+| path | contents |
+|---|---|
+| `tags.parquet` | the cell index: one row per (`tag`, `predictor`, file) |
+| `coverage.parquet` | one row per planned or produced table with its status |
+| `history.parquet` | first-publish and last-change commit of every file |
+| `<dataset>.parquet` | word-grain scalar predictors of one corpus |
+| `tokens/<dataset>.parquet` | token-grain scalar predictors of one corpus |
+| `characters/<dataset>.parquet` | character-grain scalar predictors of one corpus |
+| `responsivity/<tag>.parquet` | embedding-distance word predictors, one file per cell |
+| `lens_layers/<tag>.parquet` | per-layer log-probability vectors of the lens cells, one file per cell |
+| `mcword_samples/`, `cont_entropy_samples/` | per-unit Monte Carlo sample pools behind the entropy cells |
+| `next_log_probs/` | the next-position distribution over word types at each scored word |
+| `simadj_esim/` | per-unit expected-similarity arrays behind the similarity-adjusted cells |
+| `fwd_word_lookahead_entropy_samples/` | look-ahead entropies at depths `1..K` and the per-sample step log probabilities |
+| `manifests/<tag>.json` | the compute driver's provenance sidecar of every exported cell |
+| `verification/<wave>.json` | per-table verification records of one recompute wave |
+| `owt/`, `modelblocks_kenlm_models/`, `kenlm_o1/` | frequency tables the unigram cells read |
+| `pool/`, `pool_s900000/`, `pool_tokens/`, `pool_tokens_s900000/` | the marginal sample pools earlier releases published |
+
+## Layers
+
+Layer 1 holds the predictors REPLICA computes under its own conventions. Layer 2 holds a released defect reconstructed for one study; the `study` column of the index names it. Cells published before the layer field existed carry the layer of their method.
+
+| `layer` | cells |
+|---|---|
+| 1 | 2507 |
+| 2 | 63 |
+| null | 26 |
 
-```python
-import pandas as pd
-idx = pd.read_parquet("hf://datasets/replicaverse/replica-predictors/tags.parquet")
+| `method` | `study` | cells |
+|---|---|---|
+| `continuation_entropy_buggy` | `giulianelli2024generalized` | 1 |
+| `modelblocks_surprisal` | `clark2025wordentropy` | 28 |
+| `wordsprobability_surprisal_buggy` | `pimentel2024probability` | 34 |
 
-# Every word-level Renyi-1/2 entropy cell computed with 512 samples.
-idx[(idx.method == "mc_word_entropy") & (idx.alpha == 0.5) & (idx.n_samples == 512)]
+## Storage classes and units
 
-# What is available for one corpus?
-idx[idx.dataset == "provo"][["model", "method", "predictor", "value_unit", "n_rows"]]
+A `positional` estimator conditions on a prefix ending at a token position and returns a value for that position; its cell is stored per token under `tokens/` and any coarser unit derives from the token table. A `unit_bound` estimator takes the unit as an input, so its cell is stored at the unit it was computed for and `unit_rule` names the boundary rule the estimator used (`whitespace_continuation` is the model's own notion of a word: a token sequence that continues until the next whitespace-initial token). `unit` is the grain of the rows: `words`, `tokens` or `characters`. A carried cell whose method the compute driver no longer accepts has null storage columns.
 
-# Which file holds the raw sample arrays behind a scalar cell?
-idx[idx.tag == "gpt2_mcword_entropy_N512_M20_a1_s0_provo"][["predictor", "file"]]
-```
+| `storage_class` | `unit` | cells |
+|---|---|---|
+| null | words | 26 |
+| positional | characters | 15 |
+| positional | tokens | 429 |
+| positional | words | 1253 |
+| unit_bound | words | 873 |
 
-| column | meaning |
+| `unit_rule` | cells |
 |---|---|
-| `tag` | the cell id, as it appears in the `tag` column of the data files |
-| `file` | the file in this repo that holds these rows |
-| `dataset` | corpus |
-| `model` | the model or frequency table the cell was computed from |
-| `model_kind` | `lm` or `frequency_table` |
-| `method` | how the value was computed (see [Methods](#methods)) |
-| `predictor` | the value key |
-| `value_unit` | `bits`, `nats`, `dimensionless`, `identifier` or `text` |
-| `n_stimuli` | stimuli covered |
-| `n_rows` | rows, which is also the number of scored units |
-| `alpha`, `n_samples`, `max_tokens`, `beam_width`, `max_words`, `seed` | the cell's configuration, `null` where the method has no such setting |
-| `provenance` | where the columns below come from: `manifest` (the runner's sidecar, written for cells computed since 2026-09-06), `unit_metadata` (the Monte Carlo families stamp device, torch build and chunk size on every unit), `records` (the frozen compute environment, the job log that wrote the cell, the scheduler's GPU record, the pickle's write time and the model cache), or `none` |
-| `chunk_size`, `device`, `cuda`, `torch`, `transformers`, `python`, `replica` | the realization: how many samples were drawn at once, the GPU the model ran on, and the package versions. A sample is only reproducible on the same device and torch build |
-| `code_commit`, `code_dirty`, `source_sha256` | the commit of the source tree the cell was computed at, whether that tree had uncommitted changes, and a content hash of `src/replica` that identifies the code even without a commit |
-| `model_revision`, `computed_at` | the Hugging Face revision of the model snapshot and when the cell was written |
-
-Cells published before 2026-09-08 predate the manifest. The environment columns
-are filled only for the Monte Carlo families, which carried their device and torch
-build on every unit. Their `code_commit` was established afterwards from the
-schedulers' job accounting, the checkouts' reflogs, and a file-by-file match of
-the compute tree against the repository; the 50 cells whose tree was overwritten
-before it could be matched stay null, as does every other pre-manifest cell. The
-`history.parquet` file records, for every cell, the commit that first published
-it and the commit that last changed it: the publishing commit is not the
-computing commit, but it is the one record every cell has.
-
-## Row schema
-
-The per-corpus files are long format: one row per (`tag`, `predictor`,
-`stimulus_id`, `unit_index`).
+| `spacy_sentence` | 26 |
+| `whitespace_continuation` | 395 |
 
-| column | type | meaning |
+## Word values derived from token tables
+
+410 word cells are aggregated from a token table of the same model and corpus; `derivation_source` names that table's tag and the cell's manifest records the aggregation. A word takes the tokens whose spans nest inside its span; a token that would have to be split, dropped or interpolated stops the derivation. Log probabilities sum over the word's tokens and the summed value is also stored as `surprisal` in bits; an entropy or distance takes the value at the word's first token. A corpus without word spacing derives word surprisal from the `char_beam` character table instead.
+
+| `method` | rule | derived cells |
 |---|---|---|
-| `dataset` | string | corpus name, which is also the file name of the per-corpus tables |
-| `tag` | string | cell id: model, method and configuration, e.g. `gpt2_surprisal_provo` |
-| `predictor` | string | which quantity the row carries, e.g. `token_entropy` |
-| `stimulus_id` | string | stimulus (passage or sentence) within the corpus |
-| `unit_index` | int32 | 0-based position of the text unit within the stimulus |
-| `text_unit` | string | the text unit itself: the word, or the character for the `meco_char_*` corpora |
-| `value` | double | the number, `NaN` where undefined |
-| `value_unit` | string | measurement unit of `value` |
-
-`text_unit` is the surface string the predictor was computed on, so a row can
-be read on its own and a join against the corpus is only needed to add reading
-times. It is `null` wherever that string cannot be established, which is a small
-and enumerated set of stimuli (see [Data notes](#data-notes)); a non-null
-`text_unit` is the string the value was computed on, never a guess.
-
-## Predictors and units
-
-Log bases are mixed on purpose: `log_p_observed` is a signed natural
-log-probability, kept in the base it is computed in, and every entropy is in
-bits. `value_unit` is what disambiguates them, and the same information is
-attached to the array columns as Parquet field metadata.
-
-<!-- TABLE:predictors -->
-<details>
-<summary>All indexed predictor keys and units</summary>
-
-| `predictor` | `value_unit` | cells | rows |
-|---|---|---|---|
-| `continuation_entropy` | bits | 170 | 3,211,755 |
-| `continuation_entropy_buggy` | bits | 1 | 1,931 |
-| `joint_word_entropy` | bits | 22 | 700,591 |
-| `mc_word_entropy` | bits | 312 | 7,057,470 |
-| `next_word_entropy` | bits | 126 | 1,448,928 |
-| `next_word_renyi_entropy_alpha0.5` | bits | 101 | 1,430,124 |
-| `token_entropy` | bits | 86 | 1,371,270 |
-| `token_renyi_entropy_alpha0.5` | bits | 87 | 1,403,256 |
-| `token_renyi_entropy_alpha2.0` | bits | 83 | 1,365,466 |
-| `unigram_surprisal` | bits | 154 | 2,342,173 |
-| `decontextualised_distance` | dimensionless | 58 | 1,231,728 |
-| `expected_decontextualised_distance` | dimensionless | 58 | 1,231,728 |
-| `expected_similarity_tokens` | dimensionless | 25 | 606,073 |
-| `information_value_context_informativeness_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_context_informativeness_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_context_informativeness_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_context_informativeness_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_context_informativeness_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_context_informativeness_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_context_informativeness_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_context_informativeness_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_deviation_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_deviation_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_deviation_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_deviation_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_deviation_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_deviation_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_deviation_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_deviation_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_expected_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_expected_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_expected_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_expected_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_expected_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_expected_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_expected_context_informativeness_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_expected_context_informativeness_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_expected_context_informativeness_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_expected_context_informativeness_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_expected_context_informativeness_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_expected_context_informativeness_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_expected_context_informativeness_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_expected_context_informativeness_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_expected_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_expected_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_max_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_max_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_max_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_max_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_max_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_max_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_max_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_max_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_mean_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_mean_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_mean_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_mean_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_mean_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_mean_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_mean_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_mean_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_min_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_min_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_min_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_min_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_min_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_min_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_min_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_min_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_deviation_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_deviation_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_deviation_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_deviation_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_deviation_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_deviation_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_deviation_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_deviation_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_expected_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_expected_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_expected_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_expected_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_expected_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_expected_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_expected_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_expected_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_max_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_max_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_max_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_max_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_max_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_max_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_max_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_max_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_mean_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_mean_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_mean_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_mean_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_mean_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_mean_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_mean_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_mean_euclidean` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_min_1gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_min_1gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_min_2gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_min_2gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_min_3gram` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_min_3gram_pos` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_min_cosine` | dimensionless | 54 | 639,428 |
-| `information_value_ooc_min_euclidean` | dimensionless | 54 | 639,428 |
-| `infovalue_layer0` | dimensionless | 41 | 665,808 |
-| `infovalue_layer1` | dimensionless | 41 | 665,808 |
-| `infovalue_layer10` | dimensionless | 41 | 665,808 |
-| `infovalue_layer11` | dimensionless | 41 | 665,808 |
-| `infovalue_layer12` | dimensionless | 41 | 665,808 |
-| `infovalue_layer13` | dimensionless | 41 | 665,808 |
-| `infovalue_layer14` | dimensionless | 41 | 665,808 |
-| `infovalue_layer15` | dimensionless | 41 | 665,808 |
-| `infovalue_layer16` | dimensionless | 41 | 665,808 |
-| `infovalue_layer17` | dimensionless | 41 | 665,808 |
-| `infovalue_layer18` | dimensionless | 41 | 665,808 |
-| `infovalue_layer19` | dimensionless | 41 | 665,808 |
-| `infovalue_layer2` | dimensionless | 41 | 665,808 |
-| `infovalue_layer20` | dimensionless | 41 | 665,808 |
-| `infovalue_layer21` | dimensionless | 41 | 665,808 |
-| `infovalue_layer22` | dimensionless | 41 | 665,808 |
-| `infovalue_layer23` | dimensionless | 41 | 665,808 |
-| `infovalue_layer24` | dimensionless | 41 | 665,808 |
-| `infovalue_layer3` | dimensionless | 41 | 665,808 |
-| `infovalue_layer4` | dimensionless | 41 | 665,808 |
-| `infovalue_layer5` | dimensionless | 41 | 665,808 |
-| `infovalue_layer6` | dimensionless | 41 | 665,808 |
-| `infovalue_layer7` | dimensionless | 41 | 665,808 |
-| `infovalue_layer8` | dimensionless | 41 | 665,808 |
-| `infovalue_layer9` | dimensionless | 41 | 665,808 |
-| `next_word_information_value` | dimensionless | 25 | 606,073 |
-| `next_word_information_value_first` | dimensionless | 25 | 606,073 |
-| `next_word_information_value_orth` | dimensionless | 6 | 28,953 |
-| `next_word_information_value_pos` | dimensionless | 6 | 28,953 |
-| `log_p_layers` | nats | 204 | 6,492,037 |
-| `log_p_observed` | nats | 735 | 13,695,105 |
-| `log_p_samples_nats` | nats | 482 | 10,269,225 |
-| `next_log_probs` | nats | 21 | 62,355 |
-| `similarity_adjusted_surprisal` | nats | 25 | 606,073 |
-| `similarity_adjusted_surprisal_first` | nats | 25 | 606,073 |
-| `similarity_adjusted_surprisal_orth` | nats | 6 | 28,953 |
-| `similarity_adjusted_surprisal_pos` | nats | 6 | 28,953 |
-| `information_value_context_informativeness_length` | tokens | 54 | 639,428 |
-| `information_value_deviation_length` | tokens | 54 | 639,428 |
-| `information_value_expected_context_informativeness_length` | tokens | 54 | 639,428 |
-| `information_value_expected_length` | tokens | 54 | 639,428 |
-| `information_value_max_length` | tokens | 54 | 639,428 |
-| `information_value_mean_length` | tokens | 54 | 639,428 |
-| `information_value_min_length` | tokens | 54 | 639,428 |
-| `information_value_ooc_deviation_length` | tokens | 54 | 639,428 |
-| `information_value_ooc_expected_length` | tokens | 54 | 639,428 |
-| `information_value_ooc_max_length` | tokens | 54 | 639,428 |
-| `information_value_ooc_mean_length` | tokens | 54 | 639,428 |
-| `information_value_ooc_min_length` | tokens | 54 | 639,428 |
-
-</details>
-<!-- /TABLE:predictors -->
-
-| `value_unit` | means |
-|---|---|
-| `nats` | log base e. `log_p_observed` is a log-probability, so it is `<= 0`; negate and divide by `ln 2` for surprisal in bits |
-| `bits` | log base 2, `>= 0` |
-| `dimensionless` | a cosine distance or a probability-like ratio, no log base |
-| `identifier` | not a measurement: a hash used to group samples |
-| `text` | not a measurement: a surface string |
+| `char_beam` | sum | 2 |
+| `continuation_entropy` | first | 77 |
+| `first_token_entropy` | first | 40 |
+| `logit_lens_surprisal` | sum | 88 |
+| `responsivity` | first | 36 |
+| `similarity_surprisal_contextual` | sum | 2 |
+| `similarity_surprisal_noncontextual` | sum | 10 |
+| `surprisal` | sum | 60 |
+| `token_renyi_entropy` | first | 95 |
 
-## Tag grammar
+## Row schemas
 
-```
-<model>_<method><config>_<dataset>
-   |        |        |        |
-   |        |        |        `- corpus, also the file name
-   |        |        `- configuration tokens, method dependent
-   |        `- how the value was computed
-   `- HuggingFace model id with "/" written as "_", or a frequency table name
-```
+Word and character tables, and the `responsivity/` files:
 
-`gpt2_mcword_entropy_N512_M20_a1_s0_provo` is `gpt2`, word entropy by Monte
-Carlo, 512 samples, at most 20 subword steps per sampled word, Renyi order 1
-(Shannon), seed 0, on `provo`. The pieces are already split out in
-`tags.parquet`, so nothing needs to parse this string.
+| column | type | meaning |
+|---|---|---|
+| `dataset` | string | corpus, also the file name of the per-corpus tables |
+| `tag` | string | cell id: model, method and configuration |
+| `predictor` | string | value key, e.g. `surprisal` |
+| `stimulus_id` | string | stimulus within the corpus |
+| `unit_index` | int32 | 0-based position of the unit within the stimulus |
+| `text_unit` | string | the unit as the corpus loader spells it: the word, or the character; null when the stored unit count of the stimulus differs from the corpus |
+| `value` | double | the number; `NaN` where undefined |
+| `value_unit` | string | `nats`, `bits`, `dimensionless` or `tokens` |
+| `layer` | int32 | 1 or 2, see Layers |
+| `storage_class` | string | `positional` or `unit_bound`, see Storage classes |
+| `unit_rule` | string | boundary rule of a unit-bound estimator; null for a positional one |
+
+Token tables (`tokens/<dataset>.parquet`):
 
-| token | column | meaning |
+| column | type | meaning |
 |---|---|---|
-| `N<n>` | `n_samples` | Monte-Carlo samples drawn per unit |
-| `L<n>` | `max_tokens` | continuation length, in subword tokens |
-| `M<n>` | `max_tokens` | cap on subword steps taken while extending one sampled word |
-| `K<n>` | `beam_width` / `max_words` | beam width for `charbeam`, look-ahead depth in words for `fwd_word_lookahead` |
-| `a<x>` | `alpha` | Renyi order. `a1` is the Shannon limit |
-| `s<n>` | `seed` | random seed |
-
-Some `fwd_word_lookahead_entropy_samples/` files end in `.shard<start>_<end>`: one
-slice of a corpus that was scored in pieces, covering stimuli `start` to `end`.
-The part before it is the cell id, and `tags.parquet` groups the slices under
-that id while its `file` column keeps them apart.
-
-## Methods
-
-<!-- TABLE:methods -->
-| `method` | predictors it carries | settings used | cells | corpora | models |
-|---|---|---|---|---|---|
-| `char_beam` | `log_p_observed` | `beam_width`=5 | 43 | 28 | 2 |
-| `continuation_entropy` | `continuation_entropy`, `log_p_samples_nats` | `n_samples`=64, 512; `max_tokens`=5, 10, 15; `seed`=0 | 170 | 57 | 2 |
-| `continuation_entropy_buggy` | `continuation_entropy_buggy` | `n_samples`=512; `max_tokens`=5; `seed`=0 | 1 | 1 | 1 |
-| `first_token_entropy` | `next_log_probs`, `token_entropy` | none | 86 | 58 | 2 |
-| `forward_word_lookahead_entropy` | `joint_word_entropy` | `n_samples`=512; `max_tokens`=30; `max_words`=5; `seed`=0 | 22 | 18 | 2 |
-| `information_value` | 108 keys; see predictor table or `tags.parquet` | `n_samples`=100; `max_tokens`=60; `seed`=0 | 54 | 27 | 2 |
-| `logit_lens_surprisal` | `log_p_layers` | none | 188 | 16 | 17 |
-| `mc_word_entropy` | `log_p_samples_nats`, `mc_word_entropy` | `alpha`=0.5, 1; `n_samples`=64, 512; `max_tokens`=20; `seed`=0 | 312 | 56 | 2 |
-| `mmc_unigram_surprisal` | `log_p_observed`, `unigram_surprisal` | none | 55 | 36 | 2 |
-| `mmc_unigram_surprisal_pool` | `log_p_observed`, `unigram_surprisal` | `n_samples`=4e+07; `seed`=900000 | 13 | 13 | 1 |
-| `mmc_unigram_surprisal_pool_ee` | `log_p_observed`, `unigram_surprisal` | `n_samples`=4e+07; `seed`=900000 | 10 | 10 | 1 |
-| `modelblocks_surprisal` | `log_p_observed` | none | 28 | 28 | 1 |
-| `next_word_entropy` | `next_word_entropy` | none | 126 | 58 | 2 |
-| `next_word_renyi_entropy` | `next_word_renyi_entropy_alpha0.5` | `alpha`=0.5 | 101 | 58 | 2 |
-| `responsivity` | `decontextualised_distance`, `expected_decontextualised_distance` | `n_samples`=512; `seed`=0 | 58 | 29 | 2 |
-| `similarity_surprisal_contextual` | `expected_similarity_tokens`, `next_word_information_value`, `next_word_information_value_first`, `similarity_adjusted_surprisal`, `similarity_adjusted_surprisal_first` | `n_samples`=50; `seed`=0 | 6 | 6 | 1 |
-| `similarity_surprisal_noncontextual` | `expected_similarity_tokens`, `next_word_information_value`, `next_word_information_value_first`, `similarity_adjusted_surprisal`, `similarity_adjusted_surprisal_first` | none | 19 | 19 | 1 |
-| `similarity_surprisal_word` | `next_word_information_value_orth`, `next_word_information_value_pos`, `similarity_adjusted_surprisal_orth`, `similarity_adjusted_surprisal_pos` | `n_samples`=50; `max_tokens`=10; `seed`=0 | 6 | 6 | 1 |
-| `surprisal` | `log_p_observed` | none | 442 | 57 | 15 |
-| `token_renyi_entropy` | `token_renyi_entropy_alpha0.5`, `token_renyi_entropy_alpha2.0` | `alpha`=0.5, 2 | 170 | 58 | 2 |
-| `tuned_lens_surprisal` | `log_p_layers` | none | 16 | 4 | 4 |
-| `unigram_surprisal` | `log_p_observed`, `unigram_surprisal` | none | 76 | 56 | 5 |
-| `word_information_value` | 25 keys; see predictor table or `tags.parquet` | `n_samples`=5; `max_tokens`=3; `seed`=0 | 41 | 41 | 1 |
-| `wordsprobability_surprisal` | `log_p_observed` | none | 34 | 25 | 2 |
-| `wordsprobability_surprisal_buggy` | `log_p_observed` | none | 34 | 25 | 2 |
-<!-- /TABLE:methods -->
-
-What each one computes for a word `w` with left context `c`, and the piece of
-the tag it appears as:
-
-| `method` | tag piece | definition |
+| `dataset`, `tag`, `predictor`, `stimulus_id`, `value`, `value_unit` | | as above |
+| `token_index` | int32 | 0-based position of the token within the stimulus |
+| `span_start`, `span_end` | int32 | character offsets of the token in the stimulus text |
+| `token_id` | int64 | vocabulary id under the cell's tokenizer |
+| `token` | string | the token string as the tokenizer spells it |
+
+Array files carry `dataset`, `tag`, `stimulus_id`, `unit_index`, one or more
+list columns whose unit is in the column's Parquet field metadata, and
+`value_unit`. A word-grain file adds `text_unit`; a token-grain file adds the
+token identity columns instead.
+
+Index (`tags.parquet`) columns beyond the row schema: `file` (where the rows
+live), `model`, `model_kind` (`lm` or `frequency_table`), `method`,
+`n_stimuli`, `n_rows`, the configuration (`alpha`, `n_samples`,
+`max_tokens`, `beam_width`, `max_words`, `seed`), the realization
+(`provenance`, `chunk_size`, `device`, `cuda`, `torch`, `transformers`,
+`python`, `replica`, `code_commit`, `code_dirty`, `source_sha256`,
+`model_revision`, `computed_at`), and the storage columns `layer`, `study`,
+`storage_class`, `unit`, `unit_rule`, `context_scope`, `derivation_source`,
+`wave`, `verification`, `verification_file`.
+
+## Verification
+
+Each recompute wave was checked table by table against the corpus loader's unit grid and, where a released or independently recomputed value exists, against that value. The records live in `verification/<wave>.json`; the index carries each cell's verdict in `verification` and the file in `verification_file`. `pass` means no flag was raised; `check` means at least one flag names something to look at, listed in the record's `flags`. A flag that begins `manifest` describes the sidecar the producing commit wrote, so it names a field that commit predates; the index columns are filled from the tag and the method and stay complete. A cell without a record has a null verdict.
+
+| file | verdict | cells |
 |---|---|---|
-| `surprisal` | `surprisal` | `log P(w \| c)` from a causal LM, summed over the word's subword tokens |
-| `wordsprobability_surprisal` | `wordsprobability_surprisal` | word log-probability with the word-boundary and sentence-start correction of the upstream `wordsprobability` package |
-| `wordsprobability_surprisal_buggy` | `wordsprobability_surprisal_buggy` | the same package's uncorrected column: a plain sum of subword log-probabilities, equal to `surprisal` aggregated to whitespace words |
-| `char_beam` | `charbeam_surprisal_K<k>` | character-level log-probability, marginalising the top-`K` tokenisations of the string with a byte-level beam search (`genlm-bytes`), then aggregated to the corpus's units |
-| `first_token_entropy` | `ft_entropy` | Shannon entropy of the next-token distribution over the full subword vocabulary, read at the word's first subword |
-| `token_renyi_entropy` | `token_renyi_entropy_a<x>` | Renyi-`alpha` entropy of that same first-subword distribution |
-| `next_word_entropy` | `next_word_entropy` | Shannon entropy after restricting the next-token distribution to word-initial tokens and renormalising |
-| `next_word_renyi_entropy` | `next_word_renyi_entropy_a<x>` | Renyi-`alpha` entropy of that restricted distribution |
-| `mc_word_entropy` | `mcword_entropy_N<n>_M<m>_a<x>_s<s>` | word entropy by Monte Carlo: `N` samples, each continued subword by subword until a word boundary is drawn (at most `M` steps), aggregated at Renyi-`alpha` |
-| `continuation_entropy` | `cont_entropy_N<n>_L<l>_s<s>` | sequence entropy by Monte Carlo: `N` sampled continuations of up to `L` tokens each |
-| `forward_word_lookahead_entropy` | `fwd_word_lookahead_entropy_N<n>_M<m>_K<k>_s<s>` | look-ahead entropy over whole words at every depth `1..K`, in joint, conditional and marginal form |
-| `unigram_surprisal` | `unigram_surprisal` | context-free word probability read off a frequency table |
-| `mmc_unigram_surprisal` | `mmc_unigram_surprisal` | context-free word probability recovered from an LM by chain-rule Rao-Blackwellisation over sampled contexts |
-| `responsivity` | `responsivity_N<n>_s<s>` | mean cosine distance in the model's input-embedding space from the observed word to `N` sampled alternatives, and among those alternatives |
-
-## Models and frequency tables
-
-<!-- TABLE:models -->
-| `model` | `model_kind` | methods | cells |
-|---|---|---|---|
-| `owt` | frequency_table | 1 | 5 |
-| `wordfreq` | frequency_table | 1 | 56 |
-| `wordfreq-da` | frequency_table | 1 | 1 |
-| `wordfreq-en` | frequency_table | 1 | 1 |
-| `EleutherAI/pythia-1.4b-deduped` | lm | 2 | 43 |
-| `EleutherAI/pythia-12b-deduped` | lm | 1 | 4 |
-| `EleutherAI/pythia-14m` | lm | 2 | 39 |
-| `EleutherAI/pythia-160m-deduped` | lm | 2 | 43 |
-| `EleutherAI/pythia-1b-deduped` | lm | 1 | 16 |
-| `EleutherAI/pythia-2.8b-deduped` | lm | 1 | 4 |
-| `EleutherAI/pythia-31m` | lm | 2 | 39 |
-| `EleutherAI/pythia-410m-deduped` | lm | 2 | 43 |
-| `EleutherAI/pythia-6.9b-deduped` | lm | 1 | 4 |
-| `EleutherAI/pythia-70m-deduped` | lm | 2 | 43 |
-| `Qwen/Qwen3-0.6B` | lm | 1 | 29 |
-| `Qwen/Qwen3-1.7B` | lm | 1 | 29 |
-| `Qwen/Qwen3-4B` | lm | 1 | 29 |
-| `ai-forever/mGPT` | lm | 17 | 897 |
-| `facebook/opt-1.3b` | lm | 1 | 4 |
-| `facebook/opt-125m` | lm | 3 | 35 |
-| `facebook/opt-6.7b` | lm | 1 | 4 |
-| `gpt2` | lm | 21 | 623 |
-| `gpt2-large` | lm | 3 | 34 |
-| `gpt2-medium` | lm | 2 | 26 |
-| `gpt2-xl` | lm | 3 | 47 |
-| `wt103` | lm | 1 | 13 |
-<!-- /TABLE:models -->
-
-`wordfreq` resolves its language from the corpus; `wordfreq-en` and
-`wordfreq-da` pin it. `owt` is a whitespace-token count table over
-OpenWebText, published here as `owt/unigram_counts.tsv`.
-
-Values from different models are on different scales and different
-vocabularies. Compare within a model, not across models.
-
-## Corpora
-
-<!-- TABLE:corpora -->
-**EEG (N400)** (8): `federmeier2007_n400`, `hubbard2019_n400`, `michaelov_n400`, `szewczyk2022_n400`, `szewczyk_federmeier2022_n400`, `szewczyk_n400`, `wlotko_federmeier2012_n400`, `zuco_n400`
-
-**eye tracking** (45): `bsc`, `celer`, `copco`, `devarda2023`, `dundee`, `emtec`, `geco`, `meco_de`, `meco_du`, `meco_ee`, `meco_en`, `meco_fi`, `meco_gr`, `meco_he`, `meco_it`, `meco_ko`, `meco_l1_w2_ba`, `meco_l1_w2_bp`, `meco_l1_w2_ch_s`, `meco_l1_w2_ch_t`, `meco_l1_w2_da`, `meco_l1_w2_en_uk`, `meco_l1_w2_ge_po`, `meco_l1_w2_ge_zu`, `meco_l1_w2_hi_iiith`, `meco_l1_w2_hi_iitk`, `meco_l1_w2_ic`, `meco_l1_w2_no`, `meco_l1_w2_ru_mo`, `meco_l1_w2_se`, `meco_l1_w2_sp_ch`, `meco_l1_w2_tr`, `meco_l2_w1`, `meco_l2_w2`, `meco_no`, `meco_ru`, `meco_sp`, `meco_tr`, `onestop`, `potec`, `provo`, `sbsat`, `ucl_et`, `zuco1`, `zuco2`
-
-**maze** (1): `natural_stories_maze`
-
-**self-paced reading** (4): `brothers_kuperberg`, `brown_spr`, `natural_stories`, `ucl_spr`
-<!-- /TABLE:corpora -->
-
-## Files
-
-<!-- TABLE:files -->
-| path | files | cells | rows |
-|---|---|---|---|
-| `(root)` | 58 | 1827 | 121,571,006 |
-| `cont_entropy_samples/` | 170 | 170 | 3,211,755 |
-| `fwd_word_lookahead_entropy_samples/` | 22 | 22 | 700,591 |
-| `lens_layers/` | 204 | 204 | 6,492,037 |
-| `mcword_samples/` | 312 | 312 | 7,057,470 |
-| `next_log_probs/` | 21 | 21 | 62,355 |
-| `responsivity/` | 58 | 58 | 2,463,456 |
-| `simadj_esim/` | 25 | 25 | 606,073 |
-<!-- /TABLE:files -->
+| `verification/wave1-tokens.json` | check | 406 |
+| `verification/wave1-tokens.json` | pass | 6 |
+| `verification/wave2.json` | check | 269 |
+| `verification/wave2.json` | pass | 626 |
 
-| path | contents |
+| flag | tables |
 |---|---|
-| `tags.parquet` | the cell index described above |
-| `history.parquet` | one row per file ever published: the commit and time that first added it and that last changed it, and whether it is still present |
-| `manifests/<tag>.json` | the runner's full provenance sidecar for cells computed since 2026-09-06: resolved options, model snapshot, corpus hash, source-tree hash, package versions, and the environment (commit, device, CUDA build, time) |
-| `<dataset>.parquet` | scalar per-word predictors for one corpus, one file per corpus |
-| `responsivity/` | embedding-distance predictors, same long schema, one file per (model, corpus). Has its own README |
-| `next_log_probs/` | the model's predictive distribution over word types at each scored position |
-| `mcword_samples/` | the per-word Monte-Carlo sample pool behind `mc_word_entropy` |
-| `cont_entropy_samples/` | the per-word sample pool behind `continuation_entropy` |
-| `fwd_word_lookahead_entropy_samples/` | word-level look-ahead entropies at depths `1..K`, plus the raw per-sample step log-probabilities |
-| `owt/unigram_counts.tsv` | OpenWebText unigram count table |
-| `modelblocks_kenlm_models/` | order-1 KenLM ARPA trained on OpenWebText |
-| `kenlm_o1/` | per-word OpenWebText unigram surprisal, one pickle per corpus |
-
-Only `tags.parquet` and the per-corpus files are dataset-viewer configs. A row
-holding a vocabulary-sized array does not render usefully, so the array folders
-are loaded by path.
+| manifest identity lacks storage_class | 405 |
+| manifest layer is None | 405 |
+| words empty or containing whitespace | 274 |
+| context scope story (layer 1 conditions on the whole stimulus) | 49 |
+| BOS disabled | 42 |
+| context scope story | 28 |
+| context scope sentence (layer 1 conditions on the whole stimulus) | 14 |
+| unit characters | 9 |
+| manifest lacks the window rule | 8 |
+| manifest sampling block does not record positions: all | 8 |
+| manifest sampling block records no seed | 8 |
+| context scope sentence | 7 |
+| token spans differ from the independent tokenization | 7 |
+| NaN beyond the first token | 7 |
+| NaN beyond the first word | 4 |
+| unexpected producer commit | 2 |
+| non-finite values | 2 |
+| independent per-token recomputation differs by 0.0017 | 1 |
+| stimuli whose stored words differ from re-aggregating gpt2_simadj_surprisal_noncontextual_tokens_sbsat.pkl | 1 |
+| stimuli whose stored words differ from re-aggregating gpt2_simadj_surprisal_noncontextual_tokens_zuco1.pkl | 1 |
+
+## Coverage and gaps
+
+`coverage.parquet` has one row per planned (method, model, corpus, unit) table and one per produced table no plan names. `status` is `exported` when the table comes from the latest release, `carried` when the store ships it from an earlier one, `quarantined` when the compute driver refused to publish it (a stimulus failed or the values failed an audit) and the store has no earlier table, and `absent` when the store has none. `reason_class` is the plan's class for the table, one of `complete`, `excluded_by_decision`, `expected_refusal`, `held_decision`, `known_gap_beam_empty`, `missing`, `running`. `expected_refusal` is a whitespace-word estimator on a corpus whose words are not the model's words, and `known_gap_beam_empty` is a character beam that emptied on a stimulus. `detail` holds the failure log of a quarantined table or the command of an absent one.
+
+| `status` | tables |
+|---|---|
+| absent | 1 |
+| carried | 43 |
+| exported | 1309 |
+| quarantined | 5 |
 
-## Loading
+| `status` | `reason_class` | tables |
+|---|---|---|
+| absent | held_decision | 1 |
+| carried | excluded_by_decision | 1 |
+| carried | expected_refusal | 6 |
+| carried | held_decision | 24 |
+| carried | known_gap_beam_empty | 2 |
+| carried | missing | 8 |
+| carried | running | 2 |
+| exported | complete | 855 |
+| exported | expected_refusal | 2 |
+| exported | not planned | 452 |
+| quarantined | held_decision | 5 |
+
+| quarantined `method` | tables |
+|---|---|
+| `modelblocks_surprisal` | 1 |
+| `wordsprobability_surprisal` | 2 |
+| `wordsprobability_surprisal_buggy` | 2 |
 
-One corpus, one cell:
+## What changed in this release
 
-```python
-import pandas as pd
+Built on the published store at commit `2508a73d9431d94a3b776ced13c3fc44f49d9427`; every published cell this release does not replace is carried over unchanged.
 
-REPO = "hf://datasets/replicaverse/replica-predictors"
-provo = pd.read_parquet(f"{REPO}/provo.parquet")
-cell = provo[provo.tag == "gpt2_surprisal_provo"]
-cell[["stimulus_id", "unit_index", "text_unit", "value", "value_unit"]].head()
-```
+| wave | cells exported |
+|---|---|
+| wave1-tokens | 412 |
+| wave2 | 895 |
 
-One predictor per column, ready to model:
+- 825 published cells replaced by a recomputed table of the same tag
+- 0 published files deleted because their cell was recomputed without the array they held
+- 1895 files written
+- new: `tokens/` (token-grain tables), `characters/` (character-grain tables), `coverage.parquet`, `verification/`, and the storage columns of the word tables and the index
+- array keys without a store folder, left in the pickles only: `orth_similarity_samples`, `pos_match_samples`, `sampled_words`, `similarity_samples_tokens`
 
-```python
-wide = (provo[provo.tag.isin(["gpt2_surprisal_provo", "gpt2_ft_entropy_provo"])]
-        .pivot_table(index=["stimulus_id", "unit_index", "text_unit"],
-                     columns="predictor", values="value")
-        .reset_index())
-```
+### Carried cells on an older unit grid
 
-Through the dataset viewer:
+133 cells this release carries were computed against an earlier version of their corpus, so their `stimulus_id` and `unit_index` do not line up with the cells this release stages into the same table. Join them to a recomputed predictor only after checking the units match. `tags.parquet` gives each cell's `n_stimuli` and `n_rows`, and a recomputed cell of the same corpus gives the current shape.
 
-```python
-from datasets import load_dataset
-idx = load_dataset("replicaverse/replica-predictors", "tags", split="train")
-provo = load_dataset("replicaverse/replica-predictors", "provo", split="train")
-```
+| table | cells on an older grid |
+|---|---|
+| `dundee.parquet` | 20 |
+| `meco_de.parquet` | 2 |
+| `meco_ee.parquet` | 7 |
+| `meco_fi.parquet` | 5 |
+| `meco_gr.parquet` | 6 |
+| `meco_it.parquet` | 5 |
+| `meco_l1_w2_ch_s.parquet` | 6 |
+| `meco_l1_w2_ch_t.parquet` | 6 |
+| `meco_l2_w2.parquet` | 10 |
+| `meco_tr.parquet` | 5 |
+| `onestop.parquet` | 17 |
+| `provo.parquet` | 15 |
+| `sbsat.parquet` | 20 |
+| `szewczyk_n400.parquet` | 9 |
 
-Raw distributions and sample pools, by path:
+## Loading
 
 ```python
-import numpy as np, pyarrow.parquet as pq
-
-lp = pq.read_table(f"{REPO}/next_log_probs/gpt2_ft_entropy_provo.parquet")
-print(lp.schema.field("logprobs").metadata)          # {b'unit': b'nats'}
-dist = np.asarray(lp["logprobs"][0].as_py())         # (n_word_types,) log-probs
-np.exp(dist).sum()                                   # 1, to float32 rounding
+import pandas as pd
 
-la = pq.read_table(f"{REPO}/fwd_word_lookahead_entropy_samples/"
-                   "ai-forever_mGPT_fwd_word_lookahead_entropy_N512_M30_K5_s0_meco_de.parquet")
-row = la.slice(0, 1).to_pylist()[0]
-steps = np.asarray(row["log_p_step_nats"]).reshape(row["n_samples"], row["k_words"])
+REPO = "hf://datasets/replicaverse/replica-predictors"
+idx = pd.read_parquet(f"{REPO}/tags.parquet")
+provo = pd.read_parquet(f"{REPO}/provo.parquet")
+cell = provo[provo.tag == "gpt2_surprisal_provo"]
+tokens = pd.read_parquet(f"{REPO}/tokens/provo.parquet")
 ```
 
-## Data notes
-
-**Text units.** `unit_index` indexes the corpus's own unit layer, which is
-words everywhere except the twelve `meco_char_*` corpora, where it is single
-characters (including spaces). `text_unit` is that layer's surface string, so
-for a multi-word interest area it is the whole area, with internal whitespace
-written as `_`.
-
-**Missing text units.** A stimulus whose stored unit count disagrees with the
-corpus was scored against a different version of that text, so no position in
-it can be named and every one of its rows gets `text_unit = null`. Six
-`brothers_kuperberg` stimuli are in that state: `bk_009_{H,L,M}` and
-`bk_058_{H,L,M}`, in all of that corpus's cells. Their values do not correspond
-to the shipped sentences, so filter them out rather than joining them.
-
-**Stimulus ids.** MECO first-wave rows previously carried a float-formatted id
-(`meco_en_1.0`). They are now written as `meco_en_1`, matching the corpus.
-
-**Distributions.** `next_log_probs/` holds the model's next-position
-distribution after marginalising the subword vocabulary onto unique surface
-forms and renormalising, so a row is one log-probability per word type: 32,761
-entries for `gpt2` and 79,265 for `ai-forever/mGPT`. Each row sums to 1 under
-`exp`, to float32 rounding. It re-derives any distributional statistic over
-word types offline (entropy, Renyi at any order, top-k mass, the observed
-word's surprisal).
-
-**Precision.** Scalar predictors are float64 as computed. The stored
-distributions and sample pools are float32, so a statistic re-derived from them
-is not bit-identical to one computed in float64.
-
-**Mixed-unit files.** A row of `fwd_word_lookahead_entropy_samples/` carries six array
-columns rather than one value, and they are not all in the same unit:
-
-| column | unit | contents |
-|---|---|---|
-| `joint_word_entropy` | bits | H of the next `k` words jointly, for `k = 1..K` |
-| `conditional_word_entropy` | bits | the step from `k-1` to `k`, same length |
-| `marginal_word_entropy` | bits | H of the word at depth `k` alone |
-| `marginal_word_entropy_plugin` | bits | the same without the small-sample correction |
-| `log_p_step_nats` | nats | raw per-sample step log-probabilities, row-major `[n_samples, k_words]` |
-| `id_hash` | identifier | word-id hashes for those samples, same shape |
-
-Its per-row `value_unit` reports `bits`, the unit of the entropy columns, and
-`tags.parquet` names `joint_word_entropy` as the cell's predictor. The exact
-unit of every column is in that column's Parquet field metadata, so read that
-rather than the row when you use one of the other columns.
-
-**Coverage.** Not every (model, method, corpus) combination exists. Check
-`tags.parquet` rather than assuming a cell is present.
+Values from different models are on different scales and vocabularies;
+compare within a model.

celer.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:fb00d307a0f0419f6448afb9049bb45fc3c8344be3d5f512381563ab91425b57
-size 183044631
+oid sha256:bdd50cec403dddae96d386f1ffbf4e1131d1235a0308caad6125098656adfb09
+size 175160031

characters/celer.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48c5e3809b1128f40fd4b79da06ad102fa5b9018b785939e4e20c4383aeb5f4e
+size 16119649

characters/geco.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:508a4c983b6630f6604c7c7af35ff87de6f211b19020313b849528d9423bf258
+size 2774048

characters/meco_l1_w2_ch_s.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c7b3dbe92ccb8b61a43ef6f6e5ac40e8ebc9a76edc76e9c1276c3519f3b67474
+size 47373

characters/meco_l1_w2_ch_t.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fca32d4e0596a077d930dfae1f819816616606d88621afb2590d39f9a62b3be2
+size 47751

characters/provo.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13892c844264b0816f23a3fd2ff177668cdbe83cc0470ad7e1deaf953d5dc1e1
+size 317086

characters/sbsat.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d0c4920ea596b70fea74ad405ee6e91d6d044fa116bf67cbd3e8b1d50c28696
+size 277078

characters/zuco1.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f59934a58a13ed7148948f45f7063b2d987d895eb070974f9c1ce25ed5be5609
+size 392503

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_copco.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:15710ac3b13170302bfe912069fb2b99994a1b51b91f9d6449d132c756427cd5
-size 58235397
+oid sha256:851f8feeeb278d74f8e8e870363ae8e5e56e9054286c7db2397f0dd52c3d05ab
+size 58233348

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_geco.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e5167de17a9c2ad04da782050c7a654f99143597d713ecca454d0ae1013ad08a
-size 102039855
+oid sha256:c3fdff73dd3ede0cda3f8679df1ad798a8d622bd2946127dc81b9fc61a7b0556
+size 102037436

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_meco_de.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:beeb3c1c5230d37faa961cb34ff2d3a31e755692e3e2e74586ae4f3899a7280a
-size 4266903
+oid sha256:6dc92af684e5078827a0c4d0d7f524a264481d75e57d5d88b32b085858b4077a
+size 4266131

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_meco_ee.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:915c58037288daca91dfcefe899609e55a72ff4000e4f7e41a25694f8065279c
-size 3355044
+oid sha256:ee9ef4618e7d0b51de08820aed0b023861e052610d4602397b70d541b06e8e20
+size 3351995

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_meco_fi.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:415a7d554d5734dd341a809ca456c5484e167fb8fbb60d5afdc2bd53a4400fe5
-size 3292674
+oid sha256:2aa563395222f6b677783e464f258bc4b669cfbf4f2aff0b6ae9c8ede5c57a96
+size 3290289

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_meco_gr.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f9ec77b135f9cbf90aa2a2787404140cf9b1de17ab3e1b9d92436777a8865343
-size 4320468
+oid sha256:1751121de40994333b00e3762850715cbd561fbb2de29ee1ade384b3fc215900
+size 4316239

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_meco_it.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d00239e39d04e8e471dc64fd47e9c7891fc2dc3d527ada841e8222946cc92a86
-size 4417652
+oid sha256:de280422d9c4b7101a6765b6e9454da14df419ab41efe6e0eb5526f13d15246d
+size 4416321

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_meco_ko.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b164d46a94cd777662f155e0ae6d511a90d5d3bf59bc091b5422acb98031ad9e
-size 3844341
+oid sha256:198b35c7523cc7b92b578c04fb4a7d520960eb485782fb496808c4beade7835d
+size 3843030

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_meco_tr.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:28a9c3dc091df458a88cf6fcf5a7cd86010928809eac00146dded5fd1502b68b
-size 3664599
+oid sha256:1f1026e74de714d35daf819483691c9504b1d8f64dd97a0007228db0c1feefc3
+size 3662575

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_onestop.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c554a29bcffeee22cd6b3c3c70a4c174efd338099f9ff5461cb5c6174927bdca
-size 65463417
+oid sha256:3facb0580ad224bb3f579c6e213c8439ed0578389941d6414af97b9f90859580
+size 67206366

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_provo.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ce449bcd68eb95c2ee191123c311a49ea791a91a0a2012b31f01c9442189410e
-size 5456688
+oid sha256:329076a9030c5747521bcdc6d9d65fba92b61314e0d9f7b96e6fbea937afe401
+size 5554960

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_sbsat.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7381203f33bc836e50eb71b280cb75d8d801deaaa287b61d228ae472b9edfcbd
-size 5064724
+oid sha256:646ddca0ef397af52d029c983ce80a6b2858c9363335502b1712f5cbee95e447
+size 5062645

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_story_sbsat.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07f169e222f33ca1fae8bbd8e3f6c2664eb5e553f5ea11ffc3e2e7b9a31c582c
+size 5063222

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_copco.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a94dfbc6fc3943400323364a759f343bc07e29d3f975d84cd942bad3c8289f8b
+size 100358719

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_geco.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6843d42c884822b23a3236eec58d566898054c7851327143044e81adbe487a5a
+size 149141671

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_meco_de.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28df2e44f0af9ce1f35ac717f813c9b9090f6ad8f76be21cdb1d65ae0af040fe
+size 6424230

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_meco_ee.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e251b7c9d88fa8b6f43e05559d1b7ac1a26b95c15da6721131ca96daae99710c
+size 8973104

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_meco_fi.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a9077305657e6f6af4931346dcb27e882a67cd6f22ed69ed1459791598145106
+size 7671814

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_meco_gr.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19f2f1e65a245b0830342cfbe8f659b069d228802645594848915eb1fd028bfd
+size 9229370

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_meco_it.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f2a8c65589f352eace27e982d349a0af009cf3b64e500f98072176c0834c745b
+size 6504167

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_meco_ko.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7967bb92f4e70fd947e834a58da856aa38174476156412da8801040651c1e4ca
+size 8808824

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_meco_tr.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89fc78cc99e00e02eaafccd704dbf77ae58d087d721c6e7a27d6577e03ac13a5
+size 7614215

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_onestop.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:12b960392f3c9bf97cf48d35204468a43896beeff7d6ca6e15bc35019d9dc907
+size 88208328

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_provo.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cec3742a9ced108403e924864370b8730c7d05f29fe8b7e10026be776e454b28
+size 7013577

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_sbsat.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3e9135359c4c2331948075b222273cd80306fc36d72c61e3de1a27538876483e
+size 6548713

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L10_s0_tokens_story_sbsat.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:281fd9dd1f5680edfafd3ab8209db2aed3bf0c679579f7a855d84abb630f3b7e
+size 6536120

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_meco_de.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e3538b20d406e5e9f49dfbb0a685cba9d806fed8c27af10bf0a91cec368e966a
-size 4219091
+oid sha256:fdae73b0fd754edf58874d4db69d13a3872ae32b71b405419b8bf91ab7f02a3f
+size 4221557

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_meco_fi.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:687b87dadecf33f74c048ee448dd1f09829f4094757de3f98bc4ee1a8918c09d
-size 3274190
+oid sha256:0814bb70d3ff20ebdbcd93b53cc14b92ffc3b512d5246a77c5aad1e89b9eaba7
+size 3270206

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_meco_gr.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:aa98b53c9d5ff58aab2ca99fab212c85c07c22533c96acc984bae6fbf6059d57
-size 4372259
+oid sha256:f522d549564120150e2a0136debb8d3aa65311c47c62e0b7b600482534b7e944
+size 4368349

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_meco_it.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:028d15f4307f8aaf65f4ef7e33931ac720c5c3c2f516c8e99701d36437682208
-size 4402928
+oid sha256:bd3514dff08c5cc59a3801b49ad7d6a46eaa31b756d4f5bcd99b56c4cc6cdc6d
+size 4401279

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_meco_ko.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9dbed8262e752ec022350cc9af895e007a4a9585284632a8249f8f4a5c27d97b
-size 3832750
+oid sha256:46054cb8017bdfebf9e6aa72b675e6da9c79f6f9375f6ed4b3b7898e698811e8
+size 3830215

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_meco_tr.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b21fd0a3bcaa4e20bc2cca1a34ffc06f3af96af87a1062dfbd601c114f0131fd
-size 3670482
+oid sha256:b5ad3779b7dfa95e0cc383e6bdfb4e5cfe1444323b068e1e478c818ecaeef862
+size 3667847

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_tokens_meco_de.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b1260d1d774b3e850682d19f0ed6269534740622009237ea8c6de28d2e68d98
+size 6363351

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_tokens_meco_fi.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb90577d0914e3c932aff6a3ff7fdd98768ec3fe1012e56ef17f8618d5b6740a
+size 7657217

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_tokens_meco_gr.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1441b3e1a0033365e1e8a902293a41ce69e98e19d50afcaeb2a0635125830a7a
+size 9333792

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_tokens_meco_it.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5ee4e89834f61a862b62df2c8200774f788e506c37f7a5525c0ebcc23ba08501
+size 6492976

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_tokens_meco_ko.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a927c20da3a89d5b431a94b4489e1cc30c69bfe7de19960908d0c591e1dcf81f
+size 8800239

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L15_s0_tokens_meco_tr.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6752254f2dd756730f555a70d15f50fb74ab0b839709e2a11a5a984651d5733c
+size 7643846

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L5_s0_celer.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:7763a2823806dbadfc6ed2904a2c8a585403ba2db9fd48421cb93302b74b701e
-size 571826464
+oid sha256:10a3d3cb614e02a1ec899b3b7d5e7b0465f45d626d62abe04ced323788459dad
+size 570354449

cont_entropy_samples/ai-forever_mGPT_cont_entropy_N512_L5_s0_copco.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4c584af97b20fe8b2351b4c67999f1e73b21e4b6d8d4aea3139efa5208cec51e
-size 57754086
+oid sha256:2ca95af52a13a93f79ae17acb40ce9f76e486d87f36118601d952fa6719b1b87
+size 57756731