TalentCLEF 2026 corpus: Skill and Job Title Intelligence for Human Capital Management

TalentCLEF2026 corpus - sample set. To check when new data will be uploaded, please consult the calendar of the task Introduction: Given the positive response to the inaugural edition of TalentCLEF in 2025, with 76 registered teams, 15 submitted working notes, and 280 runs across both task. TalentCLEF continues to focus on developing and evaluating models that support three key objectives: Finding/ranking candidates for job positions based on their experience and professional skills. Implementing upskilling and reskilling strategies that promote the coninuous development of workers Detecting emerging skills and skills gaps of importance in organizations. This year’s edition includes two tasks: Task A - Contextualized Job-Person Matching. Develop systems that identify and rank the most suitable candidate résumés for a given job offer. For each job description in the test set, participants must submit a ranked list of candidate profiles relevant to the position. Task B - Job-Skill Matching with Skill Type Classification. Develop systems that retrieve the relevant skills associated with a given job title and classify each retrieved skill as core, complementary, or transversa This repo contains the data for these two tasks. File structure: The files will be organized into two *.zip files, TaskA and TaskB, each containing training, validation and test folders to support different stages of model development. Until the official release of the full training set, users can access a sample version of the data that will be used through the sampleset_TaskA.zip and sampleset_TaskB.zip files to visualize the type of dataset they will encounter during the development of TalentCLEF TaskA includes language-specific subfolders within the directories, covering English and Spanish data. Development folders include two essential folders (queries, corpus_elements), and a q_rels file for evaluating model relevance to search queries. TaskA/ │ ├── development/│ ├── english/ │ │ └── queries/│ │ └── corpus_elements/│ │ └── qrels.tsv │ └── spanish/ │ └── test/ TaskB follows a similar structure but without language-specific subfolders, providing general .tsv files for training, validation, and testing. This consistent file organization enables efficient data access and structured updates as new data versions are published. The training set (and the sample set) does not include information about the labels, that will be shown with the release of the training set. TaskB/ │ ├── training/ │ └── taskB_training.tsv │ ├── validation/ │ └── test/ Resources: Web Additional resources More resources soon.TalentCLEF2026语料库（TalentCLEF2026）——样本集。如需查询新数据上传时间，请查阅赛事日程表。 引言： 鉴于2025年首届人才CLEF（TalentCLEF）赛事收获了积极反馈，共有76支注册队伍、15篇工作笔记提交以及两项任务累计280次提交运行，本届人才CLEF赛事将持续聚焦开发与评估支持三大核心目标的模型： 1. 基于应聘者工作经历与专业技能，为岗位匹配并排序候选人 2. 制定技能提升与转岗培训策略，助力从业者持续职业发展 3. 识别组织内亟需关注的新兴技能与技能缺口 今年的赛事包含两项任务： 任务A——上下文感知岗位-应聘者匹配。开发可针对给定招聘启事识别并排序最优候选人简历的系统。针对测试集中的每份岗位描述，参赛队伍需提交一份与该岗位相关的候选人简历排序列表。 任务B——岗位-技能匹配与技能类型分类。开发可检索与给定职位名称相关的技能，并将检索到的技能划分为核心技能、辅助技能与横向技能（transversa）。 本仓库包含上述两项任务所需的数据集。 文件结构： 数据集将分为两个压缩包：TaskA与TaskB，每个压缩包均包含训练、验证与测试文件夹，以支持模型开发的不同阶段。在完整训练集正式发布前，参与者可通过sampleset_TaskA.zip与sampleset_TaskB.zip获取本次赛事将使用的样本数据集，以熟悉开发过程中即将接触的数据集格式。 任务A包含语言专属子文件夹，覆盖英语与西班牙语数据。开发文件夹包含两个核心子目录（queries、corpus_elements），以及用于评估模型与搜索查询相关性的qrels.tsv文件。 TaskA/ │ ├── development/ │ ├── english/ │ │ ├── queries/ │ │ ├── corpus_elements/ │ │ └── qrels.tsv │ └── spanish/ │ └── test/ 任务B采用类似的目录结构，但无需语言专属子文件夹，仅提供适用于训练、验证与测试的通用.tsv格式文件。统一的文件架构可实现高效的数据访问，并便于在发布新版本数据集时进行结构化更新。训练集（含样本集）暂未包含标签信息，相关标签将随完整训练集一同发布。 TaskB/ │ ├── training/ │ └── taskB_training.tsv │ ├── validation/ │ └── test/ 资源： 网络资源 附加参考资源 更多资源即将上线。