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mc4_3.1.0_fi_cleaned / deduplicate.py

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Add dataset creation scripts

248c176 about 1 year ago

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	# adapted from: https://github.com/huggingface/transformers/blob/master/examples/research_projects/codeparrot/scripts/preprocessing.py

	import datasets


	def get_hash(example):
	"""Get hash of text field."""
	return {"hash": hash(example["text"])}


	def check_uniques(example, uniques):
	"""Check if current hash is still in set of unique hashes and remove if true."""
	if example["hash"] in uniques:
	uniques.remove(example["hash"])
	return True
	else:
	return False


	def filter(example, uniques):
	"""Filter dataset with unique values."""
	if not check_uniques(example, uniques):
	return False
	else:
	return True


	dataset = datasets.load_from_disk("/researchdisk/mc4_3.1.0_fi_cleaned")

	# TRAIN SPLIT DEDUPLICATION

	print(f"Size of original dataset train: {len(dataset['train'])}")

	dataset["train"] = dataset["train"].map(get_hash, num_proc=96)

	# Deduplicate hashes
	uniques = set(dataset["train"].unique("hash"))
	frac = len(uniques) / len(dataset["train"])
	print(f"Fraction of duplicates: {1-frac:.2%}")

	# Deduplicate data
	dataset_train_deduplicated = dataset["train"].filter(
	filter, fn_kwargs={"uniques": uniques}, num_proc=64
	)
	print(f"Size of filtered dataset train: {len(dataset_train_deduplicated)}")

	# VALIDATION SPLIT DEDUPLICATION

	print(f"Size of original dataset valid: {len(dataset['validation'])}")

	dataset["validation"] = dataset["validation"].map(get_hash, num_proc=96)

	# Deduplicate hashes
	uniques = set(dataset["validation"].unique("hash"))
	frac = len(uniques) / len(dataset["validation"])
	print(f"Fraction of duplicates: {1-frac:.2%}")

	# Deduplicate data
	dataset_valid_deduplicated = dataset["validation"].filter(
	filter, fn_kwargs={"uniques": uniques}, num_proc=32
	)
	print(f"Size of filtered dataset valid: {len(dataset_valid_deduplicated)}")

	# SAVE DEDUPLICATED DATASET
	dataset_train_deduplicated = dataset_train_deduplicated.remove_columns(["hash"])
	dataset_valid_deduplicated = dataset_valid_deduplicated.remove_columns(["hash"])

	fi_mc4_cleaned = datasets.DatasetDict()
	fi_mc4_cleaned["train"] = dataset_train_deduplicated
	fi_mc4_cleaned["validation"] = dataset_valid_deduplicated

	fi_mc4_cleaned.save_to_disk("/researchdisk/mc4_3.1.0_fi_cleaned", num_proc=32)