hf-clean-benchmarks 0.0.1

This repository contains code for cleaning your training data of benchmark data to help combat data snooping.

hf_clean_benchmarks

This repository is heavily inspired by the BigCode repository and is mostly a refactoring of their code. Specifically, the main person who worked on this repository is Chenghao Mou (Awesome work!).

Install

pip install hf_clean_benchmarks

How to use

Using the API

First you need to specify which benchmarks you want to clean your data of. You can do this by creating dictionary with the benchmark name in huggingface’s datasets repository as the key and the name of the column containing the benchmark data as the value. For example, if you want to clean your data of the HumanEval and LAMBADA benchmarks, you would do the following:

# Benchmarks to clean
benchmarks = [
    {
        "name": "openai_humaneval",
        "splits": ["test"],
        "columns": ["prompt", "canonical_solution", "test"],
    },
    {
        "name": "lambada",
        "splits": ["test"],
        "columns": ["text"],
    },
]

You then pass this dictionary to the BenchmarkCleaner class. This class will download the benchmarks and construct the suffix array for each benchmark. You can then use the clean method to clean a huggingface dataset. For example:

from datasets import load_dataset
from hf_clean_benchmarks.core import BenchmarkCleaner

cleaner = BenchmarkCleaner(benchmarks, threshold=0.1, num_perm=128)

# load your dataset
dataset = load_dataset("bigcode/the-stack-smol", data_dir="data/python", split="train")

# clean the dataset
cleaned_dataset = cleaner.clean(dataset, column="content")

Checking for false positives...: 100%|██████████| 8780/8780 [00:34<00:00, 251.05it/s]
Checking for false positives...: 100%|██████████| 8805/8805 [07:34<00:00, 19.39it/s]

[11/06/22 10:34:43] INFO     Data Number                   : 10000                                      core.py:210

                    INFO     Duplicate Number              : 4033                                       core.py:211

                    INFO     Duplicate Rate                : 40.33%                                     core.py:212

                    INFO     Total Time                    : 493.73 seconds                             core.py:213

cleaned_dataset

Dataset({
    features: ['content', 'avg_line_length', 'max_line_length', 'alphanum_fraction', 'licenses', 'repository_name', 'path', 'size', 'lang', '__id__'],
    num_rows: 5967
})

Using the CLI

First you need to specify which benchmarks you want to clean your data of. You can do this by creating a json file with the benchmark name in huggingface’s datasets repository as the key and the name of the column containing the benchmark data as the value. For example, if you want to clean your data of the HumanEval and LAMBADA benchmarks, you would do the following:

file: benchmarks.json

[
    {
        "name": "openai_humaneval",
        "splits": ["test"],
        "columns": ["prompt", "canonical_solution", "test"],
    },
    {
        "name": "lambada",
        "splits": ["test"],
        "columns": ["text"],
    },
]

You then pass this json file to the clean_dataset command. This command will download the benchmarks and construct the suffix array for each benchmark. You can then use the clean command to clean a huggingface dataset. For example:

clean_dataset \
    --dataset_name bigcode/the-stack-smol \
    --column_name content \
    --benchmark_configs_path benchmarks.json \
    --output_path /tmp/test.jsonl \
    --data_dir data/python \
    --dataset_split train \
    --save_json