| 模型是数据集[BAAI/IndustryCorpus2](https://huggingface.co/datasets/BAAI/IndustryCorpus2)中用来进行行业分类分类 | |
| 模型细节： | |
| 为了提升数据集中行业划分对实际行业的覆盖，并对齐国家标准中定义的行业目录，我们参考国家统计局制定的国民经济行业分类体系和世界知识体系，进行类目的合并和整合，设计了覆盖中英文的最终的31个行业类目。类目表名称如下所示 | |
| ``` | |
| { | |
| "数学_统计": {"zh": "数学与统计", "en": "Math & Statistics"}, | |
| "体育": {"zh": "体育", "en": "Sports"}, | |
| "农林牧渔": {"zh": "农业与渔业", "en": "Agriculture & Fisheries"}, | |
| "房地产_建筑": {"zh": "房地产与建筑", "en": "Real Estate & Construction"}, | |
| "时政_政务_行政": {"zh": "政治与行政", "en": "Politics & Administration"}, | |
| "消防安全_食品安全": {"zh": "安全管理", "en": "Safety Management"}, | |
| "石油化工": {"zh": "石油化工", "en": "Petrochemicals"}, | |
| "计算机_通信": {"zh": "计算机与通信", "en": "Computing & Telecommunications"}, | |
| "交通运输": {"zh": "交通运输", "en": "Transportation"}, | |
| "其他": {"zh": "其他", "en": "Others"}, | |
| "医学_健康_心理_中医": {"zh": "健康与医学", "en": "Health & Medicine"}, | |
| "文学_情感": {"zh": "文学与情感", "en": "Literature & Emotions"}, | |
| "水利_海洋": {"zh": "水利与海洋", "en": "Water Resources & Marine"}, | |
| "游戏": {"zh": "游戏", "en": "Gaming"}, | |
| "科技_科学研究": {"zh": "科技与研究", "en": "Technology & Research"}, | |
| "采矿": {"zh": "采矿", "en": "Mining"}, | |
| "人工智能_机器学习": {"zh": "人工智能", "en": "Artificial Intelligence"}, | |
| "其他信息服务_信息安全": {"zh": "信息服务", "en": "Information Services"}, | |
| "学科教育_教育": {"zh": "学科教育", "en": "Subject Education"}, | |
| "新闻传媒": {"zh": "新闻传媒", "en": "Media & Journalism"}, | |
| "汽车": {"zh": "汽车", "en": "Automobiles"}, | |
| "生物医药": {"zh": "生物医药", "en": "Biopharmaceuticals"}, | |
| "航空航天": {"zh": "航空航天", "en": "Aerospace"}, | |
| "金融_经济": {"zh": "金融与经济", "en": "Finance & Economics"}, | |
| "住宿_餐饮_酒店": {"zh": "住宿与餐饮", "en": "Hospitality & Catering"}, | |
| "其他制造": {"zh": "制造业", "en": "Manufacturing"}, | |
| "影视_娱乐": {"zh": "影视与娱乐", "en": "Film & Entertainment"}, | |
| "旅游_地理": {"zh": "旅游与地理", "en": "Travel & Geography"}, | |
| "法律_司法": {"zh": "法律与司法", "en": "Law & Justice"}, | |
| "电力能源": {"zh": "电力与能源", "en": "Power & Energy"}, | |
| "计算机编程_代码": {"zh": "编程", "en": "Programming"}, | |
| } | |
| ``` | |
| - 行业分类模型的数据构造 | |
| - 数据构建 | |
| 数据来源：预训练预训练语料抽样和开源文本分类数据，其中预训练语料占比90%，通过数据采样，保证中英文数据占比为1:1 | |
| 标签构造：使用LLM模型对数据进行多次分类判定，筛选多次判定一致的数据作为训练数据 | |
| 数据规模：36K | |
| 数据构造的整体流程如下： | |
|  | |
| - 模型训练： | |
| 参数更新：在预训练的bert模型上添加分类头进行文本分类模型训练 | |
| 模型选型：考虑的模型性能和推理效率，我们选用了0.5b规模的模型，通过对比实验最终最终选择了bge-m3并全参数训练的方式，作为我们的基座模型 | |
| 训练超参：全参数训练，max_length = 2048，lr=1e-5，batch_size=64,，验证集评估acc：86% | |
|  | |
| ### |