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license: apache-2.0
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- BAAI/bge-m3
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---
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该模型是数据集[BAAI/IndustryCorpus2](https://huggingface.co/datasets/BAAI/IndustryCorpus2)用于31个行业分类
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## 模型细节：
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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
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```
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{
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"数学_统计": {"zh": "数学与统计", "en": "Math & Statistics"},
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"体育": {"zh": "体育", "en": "Sports"},
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"农林牧渔": {"zh": "农业与渔业", "en": "Agriculture & Fisheries"},
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"房地产_建筑": {"zh": "房地产与建筑", "en": "Real Estate & Construction"},
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"时政_政务_行政": {"zh": "政治与行政", "en": "Politics & Administration"},
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"消防安全_食品安全": {"zh": "安全管理", "en": "Safety Management"},
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"石油化工": {"zh": "石油化工", "en": "Petrochemicals"},
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"计算机_通信": {"zh": "计算机与通信", "en": "Computing & Telecommunications"},
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"交通运输": {"zh": "交通运输", "en": "Transportation"},
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"其他": {"zh": "其他", "en": "Others"},
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"医学_健康_心理_中医": {"zh": "健康与医学", "en": "Health & Medicine"},
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"文学_情感": {"zh": "文学与情感", "en": "Literature & Emotions"},
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"水利_海洋": {"zh": "水利与海洋", "en": "Water Resources & Marine"},
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"游戏": {"zh": "游戏", "en": "Gaming"},
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"科技_科学研究": {"zh": "科技与研究", "en": "Technology & Research"},
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"采矿": {"zh": "采矿", "en": "Mining"},
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"人工智能_机器学习": {"zh": "人工智能", "en": "Artificial Intelligence"},
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"其他信息服务_信息安全": {"zh": "信息服务", "en": "Information Services"},
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"学科教育_教育": {"zh": "学科教育", "en": "Subject Education"},
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"新闻传媒": {"zh": "新闻传媒", "en": "Media & Journalism"},
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"汽车": {"zh": "汽车", "en": "Automobiles"},
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"生物医药": {"zh": "生物医药", "en": "Biopharmaceuticals"},
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"航空航天": {"zh": "航空航天", "en": "Aerospace"},
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"金融_经济": {"zh": "金融与经济", "en": "Finance & Economics"},
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"住宿_餐饮_酒店": {"zh": "住宿与餐饮", "en": "Hospitality & Catering"},
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"其他制造": {"zh": "制造业", "en": "Manufacturing"},
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"影视_娱乐": {"zh": "影视与娱乐", "en": "Film & Entertainment"},
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"旅游_地理": {"zh": "旅游与地理", "en": "Travel & Geography"},
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"法律_司法": {"zh": "法律与司法", "en": "Law & Justice"},
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"电力能源": {"zh": "电力与能源", "en": "Power & Energy"},
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"计算机编程_代码": {"zh": "编程", "en": "Programming"},
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}
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```
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## 行业分类模型的数据构造
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- 数据构建
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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
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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
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数据规模：36K
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数据构造的整体流程如下：
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## 模型训练：
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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
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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
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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%
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