update ch05

docs/chapter5/code/download.py

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+import os 
+from tqdm import tqdm
+import json
+
+# 设置环境变量
+os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+
+
+# 下载预训练数据集
+os.system("modelscope download --dataset ddzhu123/seq-monkey mobvoi_seq_monkey_general_open_corpus.jsonl.tar.bz2 --local_dir your_local_dir")
+# 解压预训练数据集
+os.system("tar -xvf your_local_dir/mobvoi_seq_monkey_general_open_corpus.jsonl.tar.bz2")
+
+# 下载SFT数据集
+os.system(f'huggingface-cli download --repo-type dataset --resume-download BelleGroup/train_3.5M_CN --local-dir BelleGroup')
+
+
+
+# 1 处理预训练数据
+def split_text(text, chunk_size=512):
+    """将文本按指定长度切分成块"""
+    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
+
+input_file = 'mobvoi_seq_monkey_general_open_corpus.jsonl'
+
+with open('seq_monkey_datawhale.jsonl', 'a', encoding='utf-8') as pretrain:
+    with open(input_file, 'r', encoding='utf-8') as f:
+        data = f.readlines()
+        for line in tqdm(data, desc=f"Processing lines in {input_file}", leave=False):  # 添加行级别的进度条
+            line = json.loads(line)
+            text = line['text']
+            chunks = split_text(text)
+            for chunk in chunks:
+                pretrain.write(json.dumps({'text': chunk}, ensure_ascii=False) + '\n')
+
+# 2 处理SFT数据
+
+def convert_message(data):
+    """
+    将原始数据转换为标准格式
+    """
+    message = [
+        {"role": "system", "content": "你是一个AI助手"},
+    ]
+    for item in data:
+        if item['from'] == 'human':
+            message.append({'role': 'user', 'content': item['value']})
+        elif item['from'] == 'assistant':
+            message.append({'role': 'assistant', 'content': item['value']})
+    return message
+
+with open('BelleGroup_sft.jsonl', 'a', encoding='utf-8') as sft:
+    with open('BelleGroup/train_3.5M_CN.json', 'r') as f:
+        data = f.readlines()
+        for item in tqdm(data, desc="Processing", unit="lines"):
+            item = json.loads(item)
+            message = convert_message(item['conversations'])
+            sft.write(json.dumps(message, ensure_ascii=False) + '\n')