README.md

Installation

git clone https://github.com/ChenghaoMou/text-dedup
cd text-dedup
uv sync

Documentation

Github Pages

Features

This repository contains a collection of text deduplication scripts that are ready to use, or modify based on your needs:

• MinHash + MinHashLSH for near-duplicate detection
• 64 or 128 bit SimHash
• SuffixArray Substring exact deduplication
• Bloom Filter exact deduplication

All algorithms use a config-based approach with TOML files for easy customization.

Quick Start

All deduplication scripts read from a config.toml file in the project root.

1. Configure your settings

Edit config.toml with your input data and algorithm settings:

[input]
input_type = "local_files"
file_type = "parquet"

[input.read_arguments]
path = "data/your_data"
split = "train"

[algorithm]
algorithm_name = "minhash"
text_column = "text"
seed = 42
batch_size = 10000
num_perm = 240
threshold = 0.7
false_positive_weight = 0.5
false_negative_weight = 0.5
hash_bits = 64
ngram_size = 5
check_false_positive = true

[output]
output_dir = "output"
clean_cache = false
save_clusters = true

[debug]
enable_profiling = false

[input]
input_type = "local_files"
file_type = "parquet"

[input.read_arguments]
path = "data/your_data"
split = "train"

[algorithm]
algorithm_name = "simhash"
text_column = "text"
hash_bits = 64
ngram_size = 3
bit_diff = 3

[output]
output_dir = "output"
clean_cache = false

[debug]
enable_profiling = false

[input]
input_type = "local_files"
file_type = "parquet"

[input.read_arguments]
path = "data/your_data"
split = "train"

[algorithm]
algorithm_name = "bloom_filter"
text_column = "text"
error_rate = 1e-5
expected_elements = 100000

[output]
output_dir = "output"
clean_cache = false

[debug]
enable_profiling = false

[input]
input_type = "local_files"
file_type = "parquet"

[input.read_arguments]
path = "data/your_data"
split = "train"

[algorithm]
algorithm_name = "suffix_array"
text_column = "text"
google_repo_path = "third_party/deduplicate-text-datasets"
merge_strategy = "longest"
length_threshold = 100
cache_dir = ".cache"

[output]
output_dir = "output"
clean_cache = false

[debug]
enable_profiling = false

2. Run the deduplication

# MinHash
python -m text_dedup.minhash

# SimHash
python -m text_dedup.simhash

# Bloom Filter
python -m text_dedup.bloom_filter

# Suffix Array
python -m text_dedup.suffix_array

Benchmarks

Running Benchmarks

You can reproduce the benchmark results using the provided benchmark suite.

Quick Start with Just

# Run all benchmarks (both datasets, all algorithms)
just benchmark-all

# Run only CORE dataset benchmarks
just benchmark-core

# Run only NEWS-COPY dataset benchmarks
just benchmark-news

# Run specific algorithm on specific dataset
just benchmark-core-minhash
just benchmark-core-simhash
just benchmark-news-minhash
just benchmark-news-simhash

Configuration Files

Benchmark configuration files are located in configs/:

• benchmark_core_minhash.toml - MinHash on CORE dataset
• benchmark_core_simhash.toml - SimHash on CORE dataset
• benchmark_news_minhash.toml - MinHash on NEWS-COPY dataset
• benchmark_news_simhash.toml - SimHash on NEWS-COPY dataset

To customize benchmark parameters, edit the config files and adjust hyperparameters like num_perm, threshold, ngram_size, or bit_diff.

License

Apache 2.0

Citations

Generally, you can cite this repository as:

@software{chenghao_mou_2023_8364980,
  author       = {Chenghao Mou and
                  Chris Ha and
                  Kenneth Enevoldsen and
                  Peiyuan Liu},
  title        = {ChenghaoMou/text-dedup: Reference Snapshot},
  month        = sep,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {2023.09.20},
  doi          = {10.5281/zenodo.8364980},
  url          = {https://doi.org/10.5281/zenodo.8364980}
}

Acknowledgements

This repository is inspired by the following projects, and is heavily influenced by lessons learned from my own participation in BigScience (Apache 2.0) and BigCode (Apache 2.0). There is a blog post about the journey. Feedbacks are welcome!

• Datasketch (MIT)
• simhash-py and simhash-cpp (MIT)
• Deduplicating Training Data Makes Language Models Better (Apache 2.0)
• Gaoya (MIT)