Datasets:
metadata
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: federmeier2007_n400
data_files: federmeier2007_n400.parquet
- config_name: geco
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
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_l1_w2_ba
data_files: meco_l1_w2_ba.parquet
- config_name: meco_l1_w2_bp
data_files: meco_l1_w2_bp.parquet
- config_name: meco_l1_w2_ch_s
data_files: meco_l1_w2_ch_s.parquet
- config_name: meco_l1_w2_ch_t
data_files: meco_l1_w2_ch_t.parquet
- config_name: meco_l1_w2_da
data_files: meco_l1_w2_da.parquet
- config_name: meco_l1_w2_en_uk
data_files: meco_l1_w2_en_uk.parquet
- config_name: meco_l1_w2_ge_po
data_files: meco_l1_w2_ge_po.parquet
- config_name: meco_l1_w2_ge_zu
data_files: meco_l1_w2_ge_zu.parquet
- config_name: meco_l1_w2_hi_iiith
data_files: meco_l1_w2_hi_iiith.parquet
- config_name: meco_l1_w2_hi_iitk
data_files: meco_l1_w2_hi_iitk.parquet
- config_name: meco_l1_w2_ic
data_files: meco_l1_w2_ic.parquet
- config_name: meco_l1_w2_no
data_files: meco_l1_w2_no.parquet
- config_name: meco_l1_w2_ru_mo
data_files: meco_l1_w2_ru_mo.parquet
- config_name: meco_l1_w2_se
data_files: meco_l1_w2_se.parquet
- config_name: meco_l1_w2_sp_ch
data_files: meco_l1_w2_sp_ch.parquet
- config_name: meco_l1_w2_tr
data_files: meco_l1_w2_tr.parquet
- config_name: meco_l2_w1
data_files: meco_l2_w1.parquet
- config_name: meco_l2_w2
data_files: meco_l2_w2.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: michaelov_n400
data_files: michaelov_n400.parquet
- config_name: natural_stories
data_files: natural_stories.parquet
- config_name: natural_stories_maze
data_files: natural_stories_maze.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: szewczyk2022_n400
data_files: szewczyk2022_n400.parquet
- config_name: szewczyk_federmeier2022_n400
data_files: szewczyk_federmeier2022_n400.parquet
- config_name: szewczyk_n400
data_files: szewczyk_n400.parquet
- config_name: ucl_et
data_files: ucl_et.parquet
- config_name: ucl_spr
data_files: ucl_spr.parquet
- config_name: wlotko_federmeier2012_n400
data_files: wlotko_federmeier2012_n400.parquet
- config_name: zuco1
data_files: zuco1.parquet
- config_name: zuco2
data_files: zuco2.parquet
- config_name: zuco_n400
data_files: zuco_n400.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
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.