97 lines
3.7 KiB
Python
97 lines
3.7 KiB
Python
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import json
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import os
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import torch
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from transformers import AutoTokenizer
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def analyze_database(json_path, tokenizer_path='./model/minimind_tokenizer'):
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"""分析database_init.json文件中的数据条目数量和质量"""
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print(f"开始分析数据库文件: {json_path}")
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# 1. 加载tokenizer
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try:
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
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print(f"成功加载tokenizer: {tokenizer_path}")
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except Exception as e:
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print(f"加载tokenizer失败: {e}")
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return
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# 2. 加载JSON文件
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try:
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with open(json_path, 'r', encoding='utf-8') as f:
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database_data = json.load(f)
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# 提取sentences列表
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sentences_data = database_data.get('sentences', [])
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print(f"加载了 {len(sentences_data)} 条sentences数据")
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except Exception as e:
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print(f"加载JSON文件失败: {e}")
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return
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# 3. 分析句子长度分布
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if len(sentences_data) == 0:
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print("没有找到有效的句子数据")
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return
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# 按照importance_score排序
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sorted_sentences = sorted(sentences_data, key=lambda x: x.get('importance_score', 0.0), reverse=True)
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print(f"按importance_score排序完成，最高分: {sorted_sentences[0].get('importance_score', 0.0)}, 最低分: {sorted_sentences[-1].get('importance_score', 0.0)}")
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# 统计句子长度分布
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token_lengths = []
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pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0
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# 4. 分析token长度分布
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for i, sentence_data in enumerate(sorted_sentences):
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sentence = sentence_data.get('corrected_sentence', '')
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if not sentence:
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print(f"警告: 第 {i+1} 条数据没有corrected_sentence字段")
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continue
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# 将句子转换为tokens
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sentence_tokens = tokenizer.encode(sentence, add_special_tokens=False)
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token_lengths.append(len(sentence_tokens))
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if i < 5: # 显示前5条数据样例
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print(f"样例 {i+1}: {sentence[:50]}... (tokens: {len(sentence_tokens)})")
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# 5. 统计分析结果
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token_lengths = torch.tensor(token_lengths)
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stats = {
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"总条目数": len(sorted_sentences),
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"有效条目数": len(token_lengths),
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"token长度-平均值": token_lengths.float().mean().item(),
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"token长度-最小值": token_lengths.min().item(),
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"token长度-最大值": token_lengths.max().item(),
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"token长度-中位数": token_lengths.median().item(),
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"token长度-标准差": token_lengths.float().std().item(),
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}
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# 统计长度分布
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length_bins = {
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"小于16": (token_lengths < 16).sum().item(),
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"16-32": ((token_lengths >= 16) & (token_lengths < 32)).sum().item(),
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"32-64": ((token_lengths >= 32) & (token_lengths < 64)).sum().item(),
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"64-128": ((token_lengths >= 64) & (token_lengths < 128)).sum().item(),
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"128-256": ((token_lengths >= 128) & (token_lengths < 256)).sum().item(),
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"256及以上": (token_lengths >= 256).sum().item(),
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}
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# 打印统计信息
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print("\n===== 数据库分析结果 =====")
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for key, value in stats.items():
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print(f"{key}: {value}")
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print("\n===== Token长度分布 =====")
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for bin_name, count in length_bins.items():
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percentage = (count / len(token_lengths)) * 100
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print(f"{bin_name}: {count} ({percentage:.1f}%)")
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print(f"\n结论: 该数据库文件包含 {stats['有效条目数']} 条有效数据，可以全部填充到知识库中。")
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return stats, length_bins
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if __name__ == "__main__":
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# 指定数据库文件路径
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database_path = "./dataset/database_init.json"
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analyze_database(database_path)
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