Use case
Text classification
Categorize text data, train models to produce contextually accurate responses, or fine-tune models to classify inputs into specialized categories and predefined classes of text.
Why Labelbox for text classification
Increase data quality
Use advanced tooling, on-demand experts, AI, and real-time quality metrics to generate high-quality data for text classification.
Accelerate time to value
Rapidly integrate data, create quality training data, and fine-tune models with text classification.
Access on-demand text classification expertise
Embrace on-demand, highly-skilled labeling services and industry-specific insights that help execute text classification tasks at scale.
Collaborate in real-time
Enjoy direct access to internal and external labelers with real-time feedback on text classification tasks via the Labelbox platform.
Understanding text classification
Text classification enhances a model's understanding of context and intent. By classifying text into categories, apps and generative models are able to produce more accurate, relevant, and coherent responses based on user inputs.
Challenges in text classification tasks
Language itself consists of diverse tones, contextual cues, and emotions—all which add to the complex nature of training AI models to understand and provide nuanced and accurate responses to user prompts.
Text classification with Labelbox
Labelbox’s advanced tools, coupled with our comprehensive network of annotators, enable you to create data that captures the nuances of language, leading to generative models that truly understand and respond to the needs of users.
Customer spotlight
Dialpad, a leading AI-powered customer intelligence platform company used fine-tuning to build a powerful LLM over five years via five billion minutes of business conversations. The model offers out of the box capabilities to businesses to accurately summarize business calls, extract important insights and offer in-the-moment coaching to sellers and customer reps. Advanced discovery, curation, and annotation capabilities were crucial for building high-quality training datasets. Human evaluation was also critical to ensure quality outcomes before the system was turned live. Labelbox enabled this organization to accelerate the creation of the LLM by 75% through rich, integrated capabilities for data preparation and human evaluation.
