Featured Blog Posts

📰 Taylor Jobs – A Good, Clean Jobs Dataset

Explore our new jobs database, providing clean, enriched job postings directly from the source.

🔧 Building a Résumé-Parsing Workflow with Taylor

Learn how to use classifiers and entity extraction models from Taylor to build a practical résumé parser.

📝 Case Study: Improving Ad Sales & Analytics with IAB and IPTC Content Categories

Learn how to use Taylor's classification service to improve ad sales and audience analytics with automatic content categorization.

📰 News: Announcing the Automatic IAB Content Classifier

Learn how to use Taylor's IAB Content Classifier to tag publisher content and ad copy for Google Ad Manager.

📰 News: Announcing the ICD-10 Classifier

Explore how to build automatic ICD-10 coding with Taylor's highest-performing ICD-10 classification model.

📰 News: Announcing the IPTC Classifier

Learn how to use Taylor's classification service to classify content by IPTC codes.

Radford, Mercer, WTW, and ONET Crosswalks

Radford, Mercer, WTW, and ONET job architectures are widely used in the HR industry to classify job roles and benchmark compensation. Each of these job architectures has its own unique taxonomy and leveling system to categorize job roles based on factors such as job function, seniority, education level, and industry. However, the lack of standardization across these taxonomies poses challenges for teams looking to integrate data across different surveys. Moreover, these job architectures change every year, making it difficult to maintain mappings between different versions of the taxonomies.

📝 Case Study: Automatically Extracting Skills from Job Descriptions and Resumes

Explore how to use Taylor's entity extraction toolkit to extract job skills from unstructured documents.

🔧 Developer Blog: LLM Moderation with PromptGuard

Explore how to use Meta's latest moderation model to protect against malicious inputs to your AI application.

🔧 Developer Blog: Training & Deploying Classifiers with Modal Labs

See how Taylor uses Modal Labs to train and deploy hundreds of classifiers.

📰 News: Announcing our Automatic ONET-SOC Coder

Explore how to build automatic ONET-SOC coding with Taylor's highest-performing ONET-SOC classification model.

📝 Case Study: Classifying Job Descriptions at Scale

See how Taylor helps job aggregators structure their job postings data with industry-standard job codes.

📝 Building crosswalks between taxonomies

Learn how to build a mapping between taxonomies. Typically, teams manually (and painstakingly) map every single label. In this post, we discuss how you can build a highly accurate mapping by leveraging out-of-the-box semantic and lexical characteristics in taxonomies.