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) |