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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
lang: string
type: string
bigram: string
count: int64
pmi: double
npmi: double
score: double
variants: int64
to
{'lang': Value('string'), 'type': Value('string'), 'bigram': Value('string'), 'count': Value('int64'), 'pmi': Value('float64'), 'npmi': Value('float64'), 'score': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 265, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 120, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
lang: string
type: string
bigram: string
count: int64
pmi: double
npmi: double
score: double
variants: int64
to
{'lang': Value('string'), 'type': Value('string'), 'bigram': Value('string'), 'count': Value('int64'), 'pmi': Value('float64'), 'npmi': Value('float64'), 'score': Value('float64')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1922, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
lang string | type string | bigram string | count int64 | pmi float64 | npmi float64 | score float64 |
|---|---|---|---|---|---|---|
en | VERB+ADP | come on | 408,628 | 5.51 | 0.582 | 10.85 |
en | VERB+ADP | talk about | 115,459 | 6.58 | 0.583 | 9.81 |
en | VERB+ADP | sit down | 43,156 | 7.9 | 0.622 | 9.58 |
en | VERB+ADP | shut up | 51,375 | 7.42 | 0.596 | 9.32 |
en | VERB+ADP | look at | 145,762 | 5.89 | 0.538 | 9.23 |
en | VERB+ADP | wake up | 30,205 | 7.62 | 0.576 | 8.58 |
en | VERB+ADP | calm down | 14,157 | 8.18 | 0.571 | 7.88 |
en | VERB+ADP | pick up | 28,096 | 6.72 | 0.504 | 7.45 |
en | VERB+ADP | worry about | 29,781 | 6.6 | 0.499 | 7.41 |
en | VERB+ADP | hurry up | 21,796 | 6.6 | 0.482 | 6.95 |
en | VERB+ADP | find out | 51,391 | 5.32 | 0.427 | 6.68 |
en | VERB+ADP | grow up | 15,345 | 6.54 | 0.461 | 6.41 |
en | VERB+ADP | slow down | 6,480 | 7.49 | 0.485 | 6.15 |
en | VERB+ADP | deal with | 21,026 | 5.79 | 0.422 | 6.06 |
en | VERB+ADP | wait for | 54,130 | 4.72 | 0.381 | 6 |
en | VERB+ADP | depend on | 10,180 | 6.66 | 0.45 | 6 |
en | VERB+ADP | talk to | 124,621 | 3.81 | 0.341 | 5.77 |
en | VERB+ADP | figure out | 12,342 | 6.15 | 0.424 | 5.76 |
en | VERB+ADP | piss off | 4,248 | 7.56 | 0.471 | 5.68 |
en | VERB+ADP | hold on | 32,618 | 4.91 | 0.375 | 5.62 |
en | VERB+ADP | get out | 107,801 | 3.74 | 0.329 | 5.5 |
en | VERB+ADP | stand by | 10,442 | 6.08 | 0.412 | 5.5 |
en | VERB+ADP | look like | 57,433 | 4.26 | 0.347 | 5.48 |
en | VERB+ADP | sound like | 15,561 | 5.58 | 0.394 | 5.48 |
en | VERB+ADP | think about | 59,654 | 4.21 | 0.345 | 5.47 |
en | VERB+ADP | surround by | 2,620 | 7.9 | 0.472 | 5.36 |
en | VERB+ADP | bump into | 1,817 | 8.55 | 0.495 | 5.36 |
en | VERB+ADP | end up | 15,779 | 5.36 | 0.379 | 5.28 |
en | VERB+ADP | rid of | 13,590 | 5.47 | 0.381 | 5.23 |
en | VERB+ADP | settle down | 3,950 | 7.07 | 0.437 | 5.23 |
en | VERB+ADP | listen to | 66,283 | 3.94 | 0.326 | 5.22 |
en | VERB+ADP | care about | 16,866 | 5.13 | 0.365 | 5.12 |
en | VERB+ADP | hang on | 15,253 | 5.23 | 0.368 | 5.11 |
en | VERB+ADP | accord to | 13,018 | 5.31 | 0.368 | 5.03 |
en | VERB+ADP | cut off | 6,378 | 6.14 | 0.397 | 5.02 |
en | VERB+ADP | look for | 69,884 | 3.73 | 0.311 | 5 |
en | VERB+ADP | forget about | 14,137 | 5.18 | 0.362 | 4.99 |
en | VERB+ADP | come along | 10,775 | 5.42 | 0.368 | 4.94 |
en | VERB+ADP | set up | 10,987 | 5.36 | 0.366 | 4.91 |
en | VERB+ADP | turn into | 7,126 | 5.85 | 0.383 | 4.9 |
en | VERB+ADP | go on | 140,982 | 3.11 | 0.283 | 4.84 |
en | VERB+ADP | testify against | 771 | 9.35 | 0.505 | 4.84 |
en | VERB+ADP | pay for | 19,546 | 4.62 | 0.334 | 4.76 |
en | VERB+ADP | rely on | 2,774 | 6.87 | 0.413 | 4.72 |
en | VERB+ADP | run into | 7,137 | 5.63 | 0.368 | 4.71 |
en | VERB+ADP | screw up | 4,952 | 6.06 | 0.383 | 4.7 |
en | VERB+ADP | feel like | 20,455 | 4.46 | 0.324 | 4.64 |
en | VERB+ADP | belong to | 16,101 | 4.69 | 0.332 | 4.64 |
en | VERB+ADP | wipe out | 3,155 | 6.53 | 0.397 | 4.61 |
en | VERB+ADP | fall into | 4,549 | 6.02 | 0.377 | 4.58 |
en | VERB+ADP | live in | 29,369 | 4.05 | 0.306 | 4.54 |
en | VERB+ADP | stand up | 11,243 | 4.92 | 0.336 | 4.53 |
en | VERB+ADP | tag along | 587 | 9.31 | 0.493 | 4.53 |
en | VERB+ADP | bicker amongst | 95 | 14.8 | 0.687 | 4.52 |
en | VERB+ADP | show up | 14,324 | 4.61 | 0.323 | 4.46 |
en | VERB+ADP | hang out | 7,830 | 5.22 | 0.344 | 4.45 |
en | VERB+ADP | mix up | 3,053 | 6.36 | 0.385 | 4.45 |
en | VERB+ADP | watch out | 12,129 | 4.74 | 0.326 | 4.43 |
en | VERB+ADP | sleep with | 12,696 | 4.7 | 0.325 | 4.42 |
en | VERB+ADP | turn around | 5,085 | 5.66 | 0.359 | 4.42 |
en | VERB+ADP | stumble onto | 218 | 11.55 | 0.568 | 4.41 |
en | VERB+ADP | pass through | 2,771 | 6.38 | 0.383 | 4.38 |
en | VERB+ADP | open up | 11,193 | 4.76 | 0.325 | 4.37 |
en | VERB+ADP | interfere with | 2,478 | 6.51 | 0.387 | 4.36 |
en | VERB+ADP | clean up | 6,135 | 5.36 | 0.346 | 4.35 |
en | VERB+ADP | turn out | 12,660 | 4.61 | 0.318 | 4.34 |
en | VERB+ADP | hide behind | 1,414 | 7.31 | 0.414 | 4.34 |
en | VERB+ADP | revolve around | 415 | 9.69 | 0.5 | 4.34 |
en | VERB+ADP | laugh at | 6,900 | 5.2 | 0.339 | 4.32 |
en | VERB+ADP | blow up | 6,931 | 5.18 | 0.338 | 4.31 |
en | VERB+ADP | suffer from | 3,015 | 6.16 | 0.372 | 4.3 |
en | VERB+ADP | base on | 5,974 | 5.33 | 0.343 | 4.3 |
en | VERB+ADP | doze off | 468 | 9.33 | 0.485 | 4.3 |
en | VERB+ADP | come from | 36,225 | 3.65 | 0.282 | 4.27 |
en | VERB+ADP | hold onto | 1,114 | 7.54 | 0.42 | 4.25 |
en | VERB+ADP | lighten up | 1,125 | 7.52 | 0.419 | 4.24 |
en | VERB+ADP | agree with | 6,280 | 5.17 | 0.334 | 4.22 |
en | VERB+ADP | check out | 8,955 | 4.79 | 0.32 | 4.2 |
en | VERB+ADP | fill with | 5,043 | 5.37 | 0.34 | 4.18 |
en | VERB+ADP | ™ ª | 113 | 13.05 | 0.613 | 4.18 |
en | VERB+ADP | obsess with | 1,526 | 6.87 | 0.392 | 4.15 |
en | VERB+ADP | caption by | 567 | 8.6 | 0.454 | 4.15 |
en | VERB+ADP | rise above | 488 | 8.86 | 0.462 | 4.13 |
en | VERB+ADP | yell at | 3,025 | 5.89 | 0.356 | 4.12 |
en | VERB+ADP | stay with | 16,609 | 4.13 | 0.293 | 4.11 |
en | VERB+ADP | count on | 7,459 | 4.83 | 0.317 | 4.08 |
en | VERB+ADP | stare at | 1,262 | 7.04 | 0.395 | 4.07 |
en | VERB+ADP | go through | 19,836 | 3.93 | 0.284 | 4.06 |
en | VERB+ADP | take off | 15,882 | 4.11 | 0.291 | 4.06 |
en | VERB+ADP | walk into | 3,724 | 5.55 | 0.342 | 4.05 |
en | VERB+ADP | split up | 2,751 | 5.89 | 0.353 | 4.03 |
en | VERB+ADP | fool around | 2,077 | 6.22 | 0.364 | 4.01 |
en | VERB+ADP | fall in | 14,529 | 4.12 | 0.289 | 4 |
en | VERB+ADP | act like | 6,031 | 4.95 | 0.318 | 4 |
en | VERB+ADP | break up | 9,278 | 4.52 | 0.303 | 3.99 |
en | VERB+ADP | abide by | 602 | 8.1 | 0.429 | 3.96 |
en | VERB+ADP | transform into | 566 | 8.2 | 0.433 | 3.96 |
en | VERB+ADP | lie down | 4,689 | 5.16 | 0.324 | 3.95 |
en | VERB+ADP | refer to | 4,739 | 5.15 | 0.324 | 3.95 |
en | VERB+ADP | hook up | 2,530 | 5.85 | 0.349 | 3.94 |
End of preview.
OpenSubtitles Collocations
NPMI-scored bigram collocations extracted from the OpenSubtitles parallel corpus. Three languages, three relation types, ~43K bigrams total.
Languages & Corpus Size
| Language | Code | Corpus lines | Bigrams |
|---|---|---|---|
| English | en | ~100M | 15,000 |
| Dutch | nl | ~105M | 15,000 |
| Serbian | sr | ~50M | 13,586 |
Relation Types
- ADJ+NOUN — adjective-noun pairs: "slim contract", "kreditan kartica"
- VERB+ADP — phrasal verbs / verb-preposition: "come on", "houden van"
- VERB+NOUN — verb-object pairs: "earn money", "verdienen geld"
Fields
| Field | Type | Description |
|---|---|---|
lang |
str | Language code (en/nl/sr) |
type |
str | Relation type (ADJ+NOUN, VERB+ADP, VERB+NOUN) |
bigram |
str | Lemmatized bigram |
count |
int | Co-occurrence count in corpus |
pmi |
float | Pointwise mutual information |
npmi |
float | Normalized PMI (0–1 scale) |
score |
float | Composite ranking score (PMI + log frequency) |
variants |
int | Number of surface form variants (Serbian only) |
Usage
from datasets import load_dataset
# Load one language
ds = load_dataset("vladvlasov256/opensubs-collocations", "en", split="train")
# Load all languages
ds = load_dataset("vladvlasov256/opensubs-collocations", "all", split="train")
# Filter
ds.filter(lambda x: x["type"] == "VERB+ADP" and x["npmi"] > 0.5)
Extraction Method
- Source: OPUS OpenSubtitles v2018 (Lison & Tiedemann, 2016)
- NLP: Stanza (tokenize, POS, lemma, depparse)
- Patterns: ADJ+NOUN, VERB+NOUN, VERB+ADP extracted via dependency relations
- Filtering: MIN_COUNT=3, TOP_N=5000 per pattern per language
- Scoring: PMI and NPMI, ranked by composite score (PMI + log frequency)
Demo
See these collocations used in a live vocabulary extraction pipeline: vocab-nlp Space
Use Cases
- Collocation whitelists for NLP pipelines
- Language learning applications (phrase extraction, vocabulary selection)
- Linguistic research on multi-word expressions
Citation
If you use this data, please cite the underlying corpus:
@inproceedings{lison2016opensubtitles,
title={OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles},
author={Lison, Pierre and Tiedemann, J{\"o}rg},
booktitle={Proceedings of the 10th LREC},
year={2016}
}
License
CC-BY 4.0 (following OpenSubtitles licensing).
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