cluesurf/leipzig-frequency

Leipzig Corpora Frequency Data

Word frequency lists and co-occurrence data from the Leipzig Corpora Collection, converted to Parquet.

Covers hundreds of languages across news, web, Wikipedia, and mixed sources. Each corpus includes token frequencies, source provenance, and statistical co-occurrence pairs.

Contents

base/
  <language>/
    <source>-<date>-<size>/
      metadata.json
      string.0001.parquet
      source.0001.parquet
      cooccurrence.sentence.0001.parquet
      cooccurrence.neighbor.0001.parquet

Shards are split at ~200-400 MB each. Small corpora may have a single shard. Large corpora have multiple (0001.parquet, 0002.parquet, etc.).

Example: base/afr/news-2020-30K/string.0001.parquet

Languages use ISO 639-3 codes, sometimes with region suffixes (e.g. ara-eg for Egyptian Arabic).

Files per corpus

File	Format	Description
metadata.json	JSON	Language, source type, date, size, original filename
string.NNNN.parquet	Parquet	Token frequency list (words and punctuation)
source.NNNN.parquet	Parquet	Source article URLs and dates
cooccurrence.sentence.NNNN.parquet	Parquet	Word pairs appearing in the same sentence
cooccurrence.neighbor.NNNN.parquet	Parquet	Word pairs appearing adjacent to each other

"Strings" instead of "words" because the list includes punctuation, special characters, and other non-word tokens alongside actual words.

Parquet files use ZSTD compression for ~4x smaller size than equivalent JSONL, with column-wise reads for fast filtering.

Usage

from datasets import load_dataset

ds = load_dataset("cluesurf/leipzig-frequency")

Or query directly with DuckDB:

SELECT text, frequency
FROM 'base/afr/news-2020-30K/string.*.parquet'
ORDER BY frequency DESC
LIMIT 20;

Record schemas

metadata.json

{
  "language": "afr",
  "source": "news",
  "date": "2020",
  "size": "30K",
  "file": "afr_news_2020_30K"
}

string.NNNN.parquet

Column	Type
id	int32
text	string
frequency	int64

Example row: { id: 101, text: "die", frequency: 30994 }

source.NNNN.parquet

Column	Type
id	int32
url	string
date	string

Example row: { id: 1, url: "https://carletonvilleherald.com/...", date: "2020-05-17" }

cooccurrence.sentence.NNNN.parquet

Column	Type
string_1_id	int32
string_2_id	int32
frequency	int64
significance	float64

Example row: { string_1_id: 116, string_2_id: 4688, frequency: 5, significance: 7.61 }

cooccurrence.neighbor.NNNN.parquet

Same schema as cooccurrence.sentence, but for words appearing adjacent to each other rather than in the same sentence.

Source

Downloaded from the Leipzig Corpora Collection at the University of Leipzig.

Original archives are .tar.gz files containing tab-delimited .txt data following the Wortschatz database schema.

Sources

• Leipzig Corpora Collection
• Wortschatz project
• D. Goldhahn, T. Eckart, U. Quasthoff: Building Large Monolingual Dictionaries at the Leipzig Corpora Collection: From 100 to 200 Languages. In: Proceedings of LREC, 2012.

License

CC-BY-4.0, as specified by the Leipzig Corpora Collection.