Datasets:
Tasks:
Text Generation
Modalities:
Text
Formats:
parquet
Sub-tasks:
language-modeling
Languages:
code
Size:
100K - 1M
License:
add deduplication to preprocessing
Browse files- preprocessing.py +44 -1
preprocessing.py
CHANGED
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@@ -1,6 +1,14 @@
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from tqdm import tqdm
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from datasets import load_dataset, Dataset
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def parse_data(ds):
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"""Parse data into markdown-code pairs"""
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markdowns = []
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@@ -40,10 +48,45 @@ def parse_data(ds):
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licenses.extend([license] * len(inner_markdowns))
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return markdowns, code_snippets, paths, repo_names, licenses
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if __name__ == "__main__":
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ds = load_dataset("codeparrot/github-jupyter-parsed", split="train")
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print("Parsing data...")
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markdowns, code_snippets, paths, repo_names, licenses = parse_data(ds)
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data = {"markdown": markdowns, "code": code_snippets, "path": paths, "repo_name": repo_names, "license": licenses}
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parsed_data = Dataset.from_dict(data)
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from tqdm import tqdm
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from datasets import load_dataset, Dataset
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import hashlib
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import re
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import time
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from datasets import load_dataset
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PATTERN = re.compile(r"\s+")
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def parse_data(ds):
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"""Parse data into markdown-code pairs"""
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markdowns = []
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licenses.extend([license] * len(inner_markdowns))
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return markdowns, code_snippets, paths, repo_names, licenses
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def get_hash(example):
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"""Get hash of content field."""
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text = example["markdown"] + example["code"]
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return {"hash": hashlib.md5(re.sub(PATTERN, "", text).encode("utf-8")).hexdigest()}
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def preprocess(example):
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"""Chain all preprocessing steps into one function to not fill cache."""
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results = dict()
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results.update(get_hash(example))
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return results
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def check_uniques(example, uniques):
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"""Check if current hash is still in set of unique hashes and remove if true."""
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if example["hash"] in uniques:
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uniques.remove(example["hash"])
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return True
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else:
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return False
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def filter(example, uniques):
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if not check_uniques(example, uniques):
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return False
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else:
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return True
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if __name__ == "__main__":
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ds = load_dataset("codeparrot/github-jupyter-parsed", split="train")
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print("Parsing data...")
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markdowns, code_snippets, paths, repo_names, licenses = parse_data(ds)
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data = {"markdown": markdowns, "code": code_snippets, "path": paths, "repo_name": repo_names, "license": licenses}
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parsed_data = Dataset.from_dict(data)
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print("Deduplication...")
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parsed_data = parsed_data.map(preprocess)
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# Deduplicate hashes
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uniques = set(parsed_data.unique("hash"))
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frac = len(uniques) / len(parsed_data)
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print(f"Fraction of duplicates: {1-frac:.2%}")
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ds_filter = parsed_data.filter(filter, fn_kwargs={"uniques": uniques})
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ds_filter.push_to_hub("codeparrot/github-jupyter-text-code-pairs")
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