---
pretty_name: Per-word reading predictors
tags:
  - psycholinguistics
  - reading-times
  - surprisal
  - entropy
configs:
  - config_name: brothers_kuperberg
    data_files: brothers_kuperberg.parquet
  - config_name: brown_spr
    data_files: brown_spr.parquet
  - config_name: bsc
    data_files: bsc.parquet
  - config_name: celer
    data_files: celer.parquet
  - config_name: copco
    data_files: copco.parquet
  - config_name: devarda2023
    data_files: devarda2023.parquet
  - config_name: dundee
    data_files: dundee.parquet
  - config_name: emtec
    data_files: emtec.parquet
  - config_name: geco
    data_files: geco.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
    data_files: meco_du.parquet
  - config_name: meco_ee
    data_files: meco_ee.parquet
  - config_name: meco_en
    data_files: meco_en.parquet
  - config_name: meco_fi
    data_files: meco_fi.parquet
  - config_name: meco_gr
    data_files: meco_gr.parquet
  - config_name: meco_he
    data_files: meco_he.parquet
  - config_name: meco_it
    data_files: meco_it.parquet
  - config_name: meco_ko
    data_files: meco_ko.parquet
  - config_name: meco_no
    data_files: meco_no.parquet
  - config_name: meco_ru
    data_files: meco_ru.parquet
  - config_name: meco_sp
    data_files: meco_sp.parquet
  - config_name: meco_tr
    data_files: meco_tr.parquet
  - config_name: natural_stories
    data_files: natural_stories.parquet
  - config_name: onestop
    data_files: onestop.parquet
  - config_name: potec
    data_files: potec.parquet
  - config_name: provo
    data_files: provo.parquet
  - config_name: sbsat
    data_files: sbsat.parquet
  - config_name: ucl_et
    data_files: ucl_et.parquet
  - config_name: ucl_spr
    data_files: ucl_spr.parquet
---
# Per-word predictors for reading-time modeling

A collection of precomputed per-word predictors for computational models of
human sentence processing: surprisal, entropy (Shannon and Renyi), unigram
surprisal, and related quantities. One Parquet file per reading corpus.

## Schema

| column | type | meaning |
|---|---|---|
| `dataset` | string | corpus name (also the file name) |
| `tag` | string | cell id (model + method + config), e.g. `gpt2_standard_provo` |
| `predictor` | string | predictor key, e.g. `log_p_observed`, `token_entropy`, `mc_word_entropy` |
| `stimulus_id` | string | stimulus id within the dataset |
| `unit_index` | int32 | 0-based word index within the stimulus |
| `value` | double | predictor value (NaN where undefined) |

## Loading

```python
import pandas as pd
provo = pd.read_parquet("hf://datasets/samuki-hf/psycholing-predictors/provo.parquet")
cell = provo[provo.tag == "gpt2_standard_provo"]
```

## Directory layout

| path | contents |
|---|---|
| `<dataset>.parquet` | scalar per-word predictors, one file per corpus (schema above) |
| `next_log_probs/` | full per-word next-token log-prob distributions (float32) |
| `mcword_samples/` | Monte-Carlo sample pools behind `mc_word_entropy` |
| `cont_entropy_samples/` | Monte-Carlo sample pools behind `continuation_entropy` |
| `fwd_word_lookahead_samples/` | word-level look-ahead entropy samples (joint / conditional / marginal at depths k=1..K) |
| `owt/unigram_counts.tsv` | OpenWebText unigram count table (count-MLE unigram-surprisal baseline) |
| `modelblocks_kenlm_models/` | order-1 KenLM ARPA trained on OpenWebText |
| `kenlm_o1/` | per-word OpenWebText unigram surprisal, one pickle per dataset |

Files under the sample and distribution directories hold `list<float>` arrays and
are not dataset-viewer configs; load them by path with `pandas.read_parquet`.