import json import re from collections import Counter from itertools import islice filename = "/root/test/weitiao/data_process_bq/data/train4_chatml_closed_numdel.json" n = 5 # n-gram 短语长度 top_n = 30 # 输出前多少个高频短语 # === 清理与分词函数 (保持不变) === def clean_and_tokenize(text): if not isinstance(text, str): return [] text = text.lower() text = re.sub(r'\d+', '', text) # 去掉数字 text = re.sub(r'[^a-z\s]', '', text) # 去掉非字母字符 text = re.sub(r'\s+', ' ', text).strip() return text.split() # === 生成 n-gram (保持不变) === def get_ngrams(tokens, n): if len(tokens) < n: return [] return [' '.join(tokens[i:i+n]) for i in range(len(tokens)-n+1)] chosen_phrases = [] reject_phrases = [] with open(filename, 'r', encoding='utf-8') as f: data = json.load(f) # === 遍历数据 (针对新格式调整) === for item in data: # 1. 处理 chosen 列表 chosen_conversations = item.get("chosen", []) for msg in chosen_conversations: # 只检查 role 为 assistant 的内容 if msg.get("role") == "assistant": content = msg.get("content", "") tokens = clean_and_tokenize(content) chosen_phrases.extend(get_ngrams(tokens, n)) # 2. 处理 rejected 列表 (注意: 样本中键名为 "rejected") rejected_conversations = item.get("rejected", []) for msg in rejected_conversations: # 只检查 role 为 assistant 的内容 if msg.get("role") == "assistant": content = msg.get("content", "") tokens = clean_and_tokenize(content) reject_phrases.extend(get_ngrams(tokens, n)) # === 统计 n-gram 频率 === chosen_counter = Counter(chosen_phrases) reject_counter = Counter(reject_phrases) # === 打印结果 === print(f"\n=== {n}-gram 高频短语统计 (仅统计 Assistant 回复) ===") print(f"共分析 {len(chosen_phrases)} 个 chosen 短语,{len(reject_phrases)} 个 rejected 短语。") print(f"\n Chosen (Assistant) 的前 {top_n} 个高频短语:") for phrase, count in islice(chosen_counter.most_common(top_n), top_n): print(f"{count:>5} × {phrase}") print(f"\n Rejected (Assistant) 的前 {top_n} 个高频短语:") for phrase, count in islice(reject_counter.most_common(top_n), top_n): print(f"{count:>5} × {phrase}")