54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
from datasets import load_dataset
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import json
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import os
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import re
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def comprehensive_normalization(text):
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replacements = {
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'\u2018': "'", '\u2019': "'", '\u0060': "'",
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'\u201C': '"', '\u201D': '"',
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'\u2013': '-', '\u2014': '--', '\u2212': '-',
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'\u00A0': ' ',
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'\u2026': '...'
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}
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pattern = re.compile('|'.join(re.escape(k) for k in replacements))
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return pattern.sub(lambda m: replacements[m.group()], text)
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def process_dataset(
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dataset_name: str,
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output_subdir: str,
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dataset_config: str = None,
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split_name: str = "train",
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chunk_size: int = 1000000,
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normalization_func=comprehensive_normalization
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):
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dataset_dict = load_dataset(dataset_name, dataset_config)
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train_dataset = dataset_dict[split_name]
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total_samples = len(train_dataset)
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num_chunks = (total_samples // chunk_size) + 1
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script_dir = os.path.dirname(os.path.abspath(__file__))
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output_dir = os.path.join(script_dir, "dataset", output_subdir)
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os.makedirs(output_dir, exist_ok=True)
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for i in range(num_chunks):
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start_idx = i * chunk_size
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end_idx = min((i + 1) * chunk_size, total_samples)
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chunk = train_dataset.select(range(start_idx, end_idx))
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output_path = os.path.join(output_dir, f"{output_subdir}_text_chunk_{i}.jsonl")
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with open(output_path, "w", encoding="utf-8") as f:
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for example in chunk:
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text = example["text"]
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if normalization_func:
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text = normalization_func(text)
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json_line = {"text": text}
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f.write(json.dumps(json_line, ensure_ascii=False) + "\n")
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print(f"Saved text chunk {i} to {output_path}")
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