refactor(dataset): 重构数据集处理逻辑

utils.py

@@ -0,0 +1,39 @@
+from datasets import load_dataset
+import json
+import os
+
+def process_dataset(
+    dataset_name: str,
+    output_subdir: str,
+    dataset_config: str = None,
+    split_name: str = "train",
+    chunk_size: int = 1000000,
+    normalization_func=None
+):
+
+    dataset_dict = load_dataset(dataset_name, dataset_config) if dataset_config else load_dataset(dataset_name)
+    train_dataset = dataset_dict[split_name]
+
+    total_samples = len(train_dataset)
+    num_chunks = (total_samples // chunk_size) + 1
+
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    output_dir = os.path.join(script_dir, "dataset", output_subdir)
+    os.makedirs(output_dir, exist_ok=True)
+
+    for i in range(num_chunks):
+        start_idx = i * chunk_size
+        end_idx = min((i + 1) * chunk_size, total_samples)
+        chunk = train_dataset.select(range(start_idx, end_idx))
+
+        output_path = os.path.join(output_dir, f"{output_subdir}_text_chunk_{i}.jsonl")
+        with open(output_path, "w", encoding="utf-8") as f:
+            for example in chunk:
+                text = example["text"]
+                if normalization_func:
+                    text = normalization_func(text)
+                json_line = {"text": text}
+                f.write(json.dumps(json_line, ensure_ascii=False) + "\n")
+
+        print(f"Saved text chunk {i} to {output_path}")
+