遇见数据集

librarian-bots/model_cards_with_metadata_with_embeddings

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Hugging Face2023-12-18 更新2024-03-04 收录
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--- size_categories: - 100K<n<1M task_categories: - text-retrieval pretty_name: Model Card dataset_info: features: - name: modelId dtype: string - name: author dtype: string - name: last_modified dtype: timestamp[us, tz=UTC] - name: downloads dtype: int64 - name: likes dtype: int64 - name: library_name dtype: string - name: tags sequence: string - name: pipeline_tag dtype: string - name: createdAt dtype: timestamp[us, tz=UTC] - name: card dtype: string - name: embedding sequence: float32 splits: - name: train num_bytes: 2104883666.6585019 num_examples: 442651 download_size: 1243809305 dataset_size: 2104883666.6585019 configs: - config_name: default data_files: - split: train path: data/train-* --- # Dataset Card for Hugging Face Hub Model Cards with Embeddings This dataset consists of [model cards](https://huggingface.co/docs/hub/model-cards) for models hosted on the Hugging Face Hub. The model cards are created by the community and provide information about the model, its performance, its intended uses, and more. This dataset is updated on a daily basis and includes publicly available models on the Hugging Face Hub. This dataset is made available to help support users wanting to work with a large number of Model Cards from the Hub. We hope that this dataset will help support research in the area of Model Cards and their use but the format of this dataset may not be useful for all use cases. If there are other features that you would like to see included in this dataset, please open a new [discussion](https://huggingface.co/datasets/librarian-bots/model_cards_with_metadata/discussions/new). This dataset is the same as the [Hugging Face Hub Model Cards](https://huggingface.co/datasets/librarian-bots/model_cards) dataset but with the addition of embeddings for each model card. The embeddings are generated using the [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) model. ## Dataset Details ### Dataset Description - **Curated by:** Daniel van Strien - **Language(s) (NLP):** Model cards on the Hugging Face Hub are predominantly in English but may include other languages. ## Uses There are a number of potential uses for this dataset including: - text mining to find common themes in model cards - analysis of the model card format/content - topic modelling of model cards - analysis of the model card metadata - training language models on model cards - build a recommender system for model cards - build a search engine for model cards ### Out-of-Scope Use [More Information Needed] ## Dataset Structure This dataset has a single split. ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> The dataset was created to assist people in working with model cards. In particular. it was created to support research in the area of model cards and their use. It is possible to use the Hugging Face Hub API or client library to download model cards and this option may be preferable if you have a very specific use case or require a different format. ### Source Data The source data is `README.md` files for models hosted on the Hugging Face Hub. We do not include any other supplementary files that may be included in the model card directory. #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> The data is downloaded using a CRON job on a daily basis. #### Who are the source data producers? The source data producers are the creators of the model cards on the Hugging Face Hub. This includes a broad variety of people from the community ranging from large companies to individual researchers. We do not gather any information about who created the model card in this repository although this information can be gathered from the Hugging Face Hub API. ### Annotations [optional] There are no additional annotations in this dataset beyond the model card content. #### Annotation process N/A #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> N/A #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> We make no effort to anonymize the data. Whilst we don't expect the majority of model cards to contain personal or sensitive information, it is possible that some model cards may contain this information. Model cards may also link to websites or email addresses. ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Model cards are created by the community and we do not have any control over the content of the model cards. We do not review the content of the model cards and we do not make any claims about the accuracy of the information in the model cards. Some model cards will themselves discuss bias and sometimes this is done by providing examples of bias in either the training data or the responses provided by the model. As a result this dataset may contain examples of bias. Whilst we do not directly download any images linked to the model cards, some model cards may include images. Some of these images may not be suitable for all audiences. ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> ## Citation No formal citation is required for this dataset but if you use this dataset in your work, please include a link to this dataset page. ## Dataset Card Authors [@davanstrien](https://huggingface.co/davanstrien) ## Dataset Card Contact [@davanstrien](https://huggingface.co/davanstrien)

提供机构:
librarian-bots
原始信息汇总

数据集卡片概述

数据集描述

数据集详情

  • 数据集名称: Hugging Face Hub Model Cards with Embeddings
  • 数据集大小类别: 100K<n<1M
  • 任务类别: 文本检索
  • 数据集特征:
    • modelId: 字符串类型
    • author: 字符串类型
    • last_modified: 时间戳类型(微秒,UTC时区)
    • downloads: 64位整数类型
    • likes: 64位整数类型
    • library_name: 字符串类型
    • tags: 字符串序列类型
    • pipeline_tag: 字符串类型
    • createdAt: 时间戳类型(微秒,UTC时区)
    • card: 字符串类型
    • embedding: 浮点32位序列类型
  • 数据集分割:
    • train: 包含442651个样本,总大小为2104883666.6585019字节
  • 下载大小: 1243809305字节
  • 数据集大小: 2104883666.6585019字节

数据集配置

  • 配置名称: default
    • 数据文件:
      • split: train
      • path: data/train-*

数据集创建

  • 数据来源: Hugging Face Hub上的模型卡片README.md文件
  • 数据收集和处理: 使用CRON作业每日下载数据
  • 数据生产者: 模型卡片的创建者,包括社区中的各种人员,从大型公司到个人研究人员

数据集用途

  • 潜在用途:
    • 文本挖掘以发现模型卡片中的常见主题
    • 分析模型卡片的格式/内容
    • 模型卡片的话题建模
    • 分析模型卡片的元数据
    • 在模型卡片上训练语言模型
    • 构建模型卡片的推荐系统
    • 构建模型卡片的搜索引擎

数据集限制

  • 数据集内容: 模型卡片由社区创建,内容不受控制,可能包含偏见或不适合所有受众的图像
  • 个人和敏感信息: 未进行匿名化处理,模型卡片可能包含个人或敏感信息

推荐

  • 使用建议: 在使用数据集时,应考虑其可能包含的偏见和敏感信息

数据集作者和联系人

搜集汇总
数据集介绍
构建方式
该数据集源自Hugging Face Hub上社区创建的模型卡片,由Daniel van Strien策划,通过每日运行的CRON任务自动下载模型仓库中的README.md文件,并利用jinaai/jina-embeddings-v2-base-en模型为每张模型卡片生成向量嵌入,最终整合为包含元数据与嵌入表示的结构化数据集。数据集包含单一训练集,涵盖442,651个样本,特征包括模型ID、作者、下载量、点赞数、标签、管道标签及卡片文本等丰富字段。
使用方法
研究人员可直接通过Hugging Face Datasets库加载数据集,利用嵌入字段实现语义搜索或推荐系统开发,例如基于卡片内容向用户推荐相关模型。元数据字段(如标签、库名称)可用于分类分析或趋势挖掘,而卡片文本则支持自然语言处理任务如语言模型预训练。对于特定需求,用户也可通过Hugging Face Hub API获取原始卡片数据,但本数据集提供了更高效的批量处理方式。
背景与挑战
背景概述
在人工智能与机器学习领域,模型文档化作为提升透明度与可复现性的关键环节,日益受到研究界的重视。模型卡片(Model Card)作为一种标准化的文档形式,旨在系统记录模型的预期用途、性能评估、局限性及伦理考量等信息。由Daniel van Strien于近期创建的“librarian-bots/model_cards_with_metadata_with_embeddings”数据集,聚焦于Hugging Face Hub上社区贡献的模型卡片,并通过每日更新的方式持续收录。该数据集的核心研究问题在于如何利用大规模模型卡片文本及其嵌入表示,推动模型文档的自动分析、主题建模、推荐系统构建及语义检索等方向的发展。作为首个整合元数据与嵌入特征的模型卡片集合,该数据集为理解模型生态的演化规律、优化文档格式以及促进人机交互中的信息透明提供了重要数据基础,对模型治理与负责任AI研究具有显著的推动作用。
当前挑战
该数据集面临的核心领域挑战在于模型卡片内容的异构性与质量不一。由于模型卡片由社区自由撰写,缺乏统一的内容规范,导致文本长度、信息粒度及术语使用差异显著,为自动化分析与比较带来了困难。此外,模型卡片中可能隐含偏见、不准确描述或敏感信息,数据集未做审查,进一步增加了下游任务中的伦理风险。在构建过程中,挑战主要体现在数据获取与处理的规模与效率上:数据集依赖每日定时爬取Hugging Face Hub上的公开模型卡片,需应对海量模型的频繁更新与版本变动,确保数据的一致性与时效性。同时,嵌入特征的生成依赖于特定语言模型,不同嵌入模型的选择可能影响后续检索与聚类任务的性能,如何平衡计算成本与嵌入质量也是实际构建中的技术难点。
常用场景
经典使用场景
该数据集汇集了Hugging Face Hub上数十万份模型卡片及其元数据,并附带了基于jina-embeddings-v2-base-en模型生成的语义嵌入向量。其经典使用场景在于构建面向模型库的语义搜索引擎与推荐系统——通过计算嵌入向量间的余弦相似度,研究者能够高效检索与特定任务或领域高度相关的模型,从而突破传统基于关键词匹配的局限,实现更加精准的模型发现与知识关联。
解决学术问题
本数据集着力破解模型卡片研究中长期存在的规模化分析难题与信息孤岛困境。它使得对海量模型卡片进行文本挖掘、主题建模与格式演变分析成为可能;同时,嵌入向量的引入为探索模型功能间的隐性关联、量化模型描述语义相似性提供了坚实的数据基础,有力推动了模型卡片标准化评估与元知识体系构建等学术前沿议题的深入发展。
实际应用
在实际应用中,该数据集赋能了模型库的智能化管理工具开发。例如,开发者可基于嵌入向量构建模型推荐引擎,根据用户输入的自然语言需求自动匹配最适配的预训练模型;亦可搭建跨模型的性能对比看板,辅助从业者在模型选型、迁移学习与多模态融合等任务中做出高效决策,从而降低模型搜索的时间成本,提升AI研发流程的自动化水平。
数据集最近研究
最新研究方向
该数据集聚焦于模型卡片(Model Card)的语义化分析与智能检索,通过为Hugging Face Hub上的模型卡片嵌入稠密向量,开创了模型元数据与自然语言处理深度融合的前沿方向。当前研究热点包括基于嵌入向量的模型卡片主题建模、内容聚类与推荐系统构建,以及利用大规模模型卡片语料训练领域专用语言模型。这一方向与AI治理透明化运动紧密相连——随着全球监管机构对模型可解释性提出更高要求,结构化、可检索的模型卡片成为构建负责任AI生态的关键基础设施。该数据集还支持模型卡片内容的跨语言语义匹配与偏见模式挖掘,为模型审计与公平性研究提供数据基础。其每日更新的机制确保了研究能追踪模型生态的实时演变,对于理解开源模型社区的知识传播规律与开发者行为模式具有独特价值。
以上内容由遇见数据集搜集并总结生成
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