| --- |
| 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 |
|
|
| ```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`. |
|
|
