# BenchmarkDataNLP.jl

**URL:** https://discourse.julialang.org/t/benchmarkdatanlp-jl/129393
**Category:** Package Announcements
**Tags:** nlp
**Created:** [May 27, 2025, 9:24pm UTC](https://discourse.julialang.org/t/benchmarkdatanlp-jl/129393 "2025-05-27T21:24:13Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![mantzaris](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/mantzaris/32/3852_2.png) [@mantzaris](https://discourse.julialang.org/u/mantzaris)
#### Post date: [May 27, 2025, 9:24pm UTC](https://discourse.julialang.org/t/benchmarkdatanlp-jl/129393/1 "2025-05-27T21:24:13Z")

</div>

**BenchmarkDataNLP.jl** , a Julia package for quickly generating **synthetic, complexity controlled NLP benchmark datasets**. Without having to search for large corpora or cleaning messy CSVs etc. GitHub repo → **[https://github.com/mantzaris/BenchmarkDataNLP.jl](https://github.com/mantzaris/BenchmarkDataNLP.jl)**

Key aspect: the complexity of the production are controllable. The vocabulary, grammar production number, punctuation number and more are all linearly proportional to a complexity parameter that can range from 1 to 100. At complexity 100 the vocabulary size is 10K words (complexity 1 has 5 letters and 10 words). Different generators can be used, Context Free Grammars, template strings, RDF, or even a finite state machine production approach. The number of sentences that can be produced are decided upon and testing and training/validation datasets are produced automatically. Aspects of the grammar like polysemy can be controlled or not.

ex:

```julia
# generate a dataset using Context Free Grammar, at complexity 20, 1000 sentences (800 lines in training, 100 testing, 100 validation) at the path you choose the files to be generated, eg. "/home/user/Documents"
generate_corpus_CFG(complexity = 20, 
                    num_sentences = 1_000, 
                    enable_polysemy = false, 
                    output_dir = "/home/user/Documents", 
                    base_filename = "MyDataset")

```

This writes three `.jsonl` files:  
MyDataset\_train.jsonl # 800 lines  
MyDataset\_test.jsonl # 100 lines  
MyDataset\_valid.jsonl # 100 lines

that inside look like:

```julia
{"text":"갃갇갊 갆 갇 갆 갃가갇."}

```

Each line is UTF-8 text—by default drawn from the Hangul block to avoid accidental real-word bias: