Data Labeling for LLMs and Text Applications
Sapien provides expert-driven data labeling to fuel your LLMs and natural language processing models
Use Cases
Our text data labeling experts support a wide range of NLP applications
Sentiment Analysis
Annotate text sentiment for social media monitoring, customer support, and product reviews.
Intent Detection
Tag customer service transcripts to identify user intents and goals.
Named Entity Recognition
Label people, organizations, locations and other entities for search, analytics, and recommendations.
Question Answering
Annotate FAQs, manuals, and documents to train QA systems.
Summarization
Tag key phrases and sentences to teach models to summarize lengthy text.
Translation
Label text in multiple languages to develop machine translation tools.
Advancing LLM Annotation Schemes
Our platform supports intent classification, sentiment scoring, semantic role labeling and other schema to capture linguistic nuances in your LLM.
Sentiment analysis
NER
Summarization
Why Sapien For LLM Data Labeling
Sapien provides specialized domain expertise in data labeling for LLMs and machine learning
Advanced Annotation Schemes
Our platform supports intent classification, sentiment scoring, semantic role labeling and other schema to capture linguistic nuances.
Industry-Specific Expertise
Our text labeling teams of domain experts understand the terminology and context within your LLM’s vertical like healthcare, legal, or marketing.
Secure and Scalable
We facilitate the secure transfer and storage of your confidential documents and can rapidly scale annotation capacity up or down to meet project needs.
Quality Assurance
Sapien utilizes statistical sampling, inter-annotator agreement, linters, heuristics, and expert reviews to ensure labeling quality.
Customized Protocols
Our experienced labelers design tailored labeling protocols for your unique text data and target LLM or model performance.
Ready to get started with your LLM data labeling project?
See how Sapien’s data labeling team can help you fine-tune your LLMs to minimize trial-and-error and accelerate model deployment.