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import datasets |
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from clean_funcs import clean_text |
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from filter_stats_funcs import filter_stats |
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fi_mc4 = datasets.load_from_disk("/researchdisk/mc4_3.1.0_fi") |
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print(fi_mc4) |
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min_alphabet_ratio = 0.75 |
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max_upper_ratio = 0.10 |
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max_number_ratio = 0.05 |
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min_pred_lang_percentage = 0.95 |
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print(f"Original dataset train rows {fi_mc4['train'].num_rows}") |
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fi_mc4["train"] = fi_mc4["train"].map( |
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clean_text, num_proc=64, batched=False |
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) |
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fi_train_only_longer = fi_mc4["train"].filter( |
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lambda example: len(example["text"].split()) >= 20, num_proc=64 |
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) |
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print(f"Only longer texts dataset train rows {fi_train_only_longer.num_rows}") |
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fi_train_only_longer = fi_train_only_longer.map( |
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filter_stats, num_proc=64, batched=False |
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) |
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fi_train_cleaned = fi_train_only_longer.filter( |
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lambda example: example["alphabet_ratio"] > min_alphabet_ratio |
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and example["upper_ratio"] < max_upper_ratio |
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and example["number_ratio"] < max_number_ratio |
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and example["predicted_lang"] == "__label__fi" |
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and example["predicted_lang_percentage"] > min_pred_lang_percentage, |
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num_proc=64, |
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) |
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print(f"Final cleaned dataset train rows {fi_train_cleaned.num_rows}") |
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print(f"Original dataset val rows {fi_mc4['validation'].num_rows}") |
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fi_mc4["validation"] = fi_mc4["validation"].map( |
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clean_text, num_proc=32, batched=False |
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) |
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fi_val_only_longer = fi_mc4["validation"].filter( |
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lambda example: len(example["text"].split()) >= 20, num_proc=32 |
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) |
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print(f"Only longer texts dataset val rows {fi_val_only_longer.num_rows}") |
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fi_val_only_longer = fi_val_only_longer.map(filter_stats, num_proc=32, batched=False) |
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fi_val_cleaned = fi_val_only_longer.filter( |
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lambda example: example["alphabet_ratio"] > min_alphabet_ratio |
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and example["upper_ratio"] < max_upper_ratio |
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and example["number_ratio"] < max_number_ratio |
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and example["predicted_lang"] == "__label__fi" |
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and example["predicted_lang_percentage"] > min_pred_lang_percentage, |
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num_proc=32, |
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) |
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print(f"Final cleaned dataset val rows {fi_val_cleaned.num_rows}") |
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fi_train_cleaned = fi_train_cleaned.remove_columns( |
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[ |
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"alphabet_len", |
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"number_len", |
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"upper_len", |
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"total_len", |
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"predicted_lang", |
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"predicted_lang_percentage", |
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"alphabet_ratio", |
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"number_ratio", |
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"upper_ratio", |
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] |
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) |
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fi_val_cleaned = fi_val_cleaned.remove_columns( |
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[ |
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"alphabet_len", |
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"number_len", |
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"upper_len", |
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"total_len", |
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"predicted_lang", |
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"predicted_lang_percentage", |
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"alphabet_ratio", |
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"number_ratio", |
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"upper_ratio", |
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] |
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) |
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fi_mc4_cleaned = datasets.DatasetDict() |
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fi_mc4_cleaned["train"] = fi_train_cleaned |
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fi_mc4_cleaned["validation"] = fi_val_cleaned |
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fi_mc4_cleaned.save_to_disk("/researchdisk/mc4_3.1.0_fi_cleaned", num_proc=32) |
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