Datasets:

BAAI
/

IndustryCorpus_finance

@@ -1,46 +1,34 @@
----
-license: apache-2.0
-language:
-- zh
-- en
-size_categories:
-- n>1T
-task_categories:
-- text-generation
----
-[[中文主页]](README_ZH.md)
-Industry models play a crucial role in driving enterprise intelligence transformation and innovative development. High-quality industry data is key to improving the performance of large models and realizing industry applications. However, datasets currently used for industry model training generally suffer from issues such as insufficient data volume, low quality, and lack of domain expertise.
-To address these problems, we constructed and applied 22 industry data processing operators to clean and filter 3.4TB of high-quality multi-industry classified Chinese and English language pre-training datasets from over 100TB of open-source datasets including WuDaoCorpora, BAAI-CCI, redpajama, and SkyPile-150B. The filtered data consists of 1TB of Chinese data and 2.4TB of English data. To facilitate user utilization, we annotated the Chinese data with 12 types of labels including alphanumeric ratio, average line length, language confidence score, maximum line length, and perplexity.
-Furthermore, to validate the dataset's performance, we conducted continued pre-training, SFT, and DPO training on a medical industry demonstration model. The results showed a 20% improvement in objective performance and a subjective win rate of 82%.
-Industry categories: 18 categories including medical, education, literature, finance, travel, law, sports, automotive, news, etc.
-Rule-based filtering: Traditional Chinese conversion, email removal, IP address removal, link removal, Unicode repair, etc.
-Chinese data labels: Alphanumeric ratio, average line length, language confidence score, maximum line length, perplexity, toxicity character ratio, etc.
-Model-based filtering: Industry classification language model with 80% accuracy
-Data deduplication: MinHash document-level deduplication
-Data size: 1TB Chinese, 2.4TB English
-Industry classification data size:
-| Industry Category | Data Size (GB) | Industry Category | Data Size (GB) |
-| :-------------------:|:----------------:|:-------------------:|:----------------:|
-| Programming       | 4.1            | Politics          | 326.4          |
-| Law               | 274.6          | Mathematics       | 5.9            |
-| Education         | 458.1          | Sports            | 442            |
-| Finance           | 197.8          | Literature        | 179.3          |
-| Computer Science  | 46.9           | News              | 564.1          |
-| Technology        | 333.6          | Film & TV         | 162.1          |
-| Travel            | 82.5           | Medicine          | 189.4          |
-| Agriculture       | 41.6           | Automotive        | 40.8           |
-| Emotion           | 31.7           | Artificial Intelligence | 5.6      |
-| Total (GB)        | 3386.5         |                   |                |
-For the convenience of users to download and use, we have split the large dataset into sub-datasets for 18 industries. The current one is the sub-dataset for the 金融 industry.
-Data processing workflow:
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6459c242abdbb77c4c6e1f8e/8okkYsiKvGcU_ssn--vpD.png)

+行业模型在推动企业智能化转型和创新发展中发挥着至关重要的作用。高质量的行业数据是提升大模型性能和实现行业应用落地的关键。然而，目前用于行业模型训练的数据集普遍存在数据量少、质量低、专业性不足等问题。
+为了解决这些问题，我们构建并应用了22个行业数据处理算子，从WuDaoCorpora、BAAI-CCI、redpajama、SkyPile-150B等超过100TB的开源数据集中，清洗过滤出3.4TB的高质量多行业分类中英文语言预训练数据集，其中中文数据占1TB，英文数据占2.4TB。为了方便用户使用，我们为中文数据标注了字母数字比例、平均行长度、语言的置信度得分、最大行长度、困惑度等12种标签。
+此外，为了验证数据集的性能，我们对医疗行业示范模型进行了继续预训练、SFT和DPO训练。结果显示，模型的客观性能提升了20%，主观胜率达到了82%。
+行业分类：医疗、教育、文学、金融、旅游、法律、体育、汽车、新闻等18类行业数据
+基于规则的过滤：繁体中文转换、删除邮箱信息、删除 IP 地址、删除链接、修复损坏的 Unicode等
+中文数据标签：字母数字比例、平均行长度、语言的置信度得分、最大行长度、困惑度、毒性等字符比率等
+基于模型的过滤：行业分类语言模型，分类准确率达到80%
+数据去重：minhash文档级去重
+数据大小：中文1TB， 英文2.4TB
+行业分类数据大小
+| 行业类别   | 数据大小（GB） | 行业类别 | 数据大小（GB） |
+| :----------: | :--------------: | :--------: | :--------------: |
+| 编程       | 4.1            | 时政     | 326.4          |
+| 法律       | 274.6          | 数学     | 5.9            |
+| 教育       | 458.1          | 体育     | 442            |
+| 金融       | 197.8          | 文学     | 179.3          |
+| 计算机     | 46.9           | 新闻     | 564.1          |
+| 科技       | 333.6          | 影视     | 162.1          |
+| 旅游       | 82.5           | 医学     | 189.4          |
+| 农业       | 41.6           | 汽车     | 40.8           |
+| 情感       | 31.7           | 人工智能 | 5.6            |
+| 合计（GB） | 3386.5         |          |                |
+为了方便用户下载使用，我们将大的数据集拆分为18个行业的子数据集，当前为金融行业子数据集
+数据处理流程图
+![数据处理流程图](https://data.baai.ac.cn/resources/projects/data_hub/IndustryCorpus1_0/20240613-170156.jpg)