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- 更新LLaMA2模型文档，修正图片引用和编号 - 添加Attention结构示意图 - 新增数据处理脚本download_dataset.sh和deal_dataset.py - 优化文档中的代码示例说明
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
import os
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import json
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from tqdm import tqdm
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# pretrain_data 为运行download_dataset.sh时，下载的pretrain_data本地路径
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pretrain_data = 'your local pretrain_data'
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output_pretrain_data = 'seq_monkey_datawhale.jsonl'
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# sft_data 为运行download_dataset.sh时，下载的sft_data本地路径
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sft_data = 'your local sft_data'
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output_sft_data = 'BelleGroup_sft.jsonl'
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# 1 处理预训练数据
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def split_text(text, chunk_size=512):
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"""将文本按指定长度切分成块"""
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return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]
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with open(output_pretrain_data, 'a', encoding='utf-8') as pretrain:
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with open(pretrain_data, 'r', encoding='utf-8') as f:
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data = f.readlines()
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for line in tqdm(data, desc=f"Processing lines in {pretrain_data}", leave=False): # 添加行级别的进度条
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line = json.loads(line)
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text = line['text']
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chunks = split_text(text)
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for chunk in chunks:
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pretrain.write(json.dumps({'text': chunk}, ensure_ascii=False) + '\n')
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# 2 处理SFT数据
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def convert_message(data):
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"""
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将原始数据转换为标准格式
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"""
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message = [
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{"role": "system", "content": "你是一个AI助手"},
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]
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for item in data:
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if item['from'] == 'human':
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message.append({'role': 'user', 'content': item['value']})
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elif item['from'] == 'assistant':
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message.append({'role': 'assistant', 'content': item['value']})
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return message
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with open(output_sft_data, 'a', encoding='utf-8') as sft:
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with open(sft_data, 'r') as f:
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data = f.readlines()
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for item in tqdm(data, desc="Processing", unit="lines"):
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item = json.loads(item)
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message = convert_message(item['conversations'])
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sft.write(json.dumps(message, ensure_ascii=False) + '\n') |