Datasets:
Languages:
Finnish
Multilinguality:
monolingual
Size Categories:
unknown
Source Datasets:
extended|mc4
Tags:
# 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_dataset("csv", data_files={"train": "train.csv", "validation": "valid.csv"}) | |
# TRAIN SPLIT DEDUPLICATION | |
len_train = len(dataset["train"]) | |
print(f"Size of original dataset train: {len_train}") | |
dataset["train"] = dataset["train"].map(get_hash, num_proc=64, writer_batch_size=100000) | |
# 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}) | |
print(f"Size of filtered dataset train: {len(dataset_train_deduplicated)}") | |
# VALIDATION SPLIT DEDUPLICATION | |
len_val = len(dataset["validation"]) | |
print(f"Size of original dataset valid: {len_val}") | |
dataset["validation"] = dataset["validation"].map(get_hash, num_proc=64, writer_batch_size=100000) | |
# 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}) | |
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"]) | |
dataset_train_deduplicated.to_csv("train.csv", num_proc=64, index=False) | |
dataset_valid_deduplicated.to_csv("valid.csv", num_proc=64, index=False) |