Leading ML teams are quickly capitalizing on the latest advances in large language models (LLMs) as a powerful starting point for their NLP use cases. However, they often realize that these base models need to be specifically tailored for their use case and have to be retrained with contextual annotated data in order to build production-grade AI.
Learn how ML teams in industries such as healthcare, retail, and beyond can enable breakthroughs faster with Labelbox, whether it’s summarizing medical research papers, categorizing customer sentiment, or delivering chatbots with human-like interactions.
Labelbox provides a seamless annotation experience that allows you to label, review, and manage your custom data in its native format: text, conversation, PDFs, etc. Generate ground truth to refine your large language models using our powerful text editor that supports classifications, entity recognition, relationships on raw text snippets or threaded conversations.Learn more
Use Labelbox Catalog to visualize all of your labeled and unlabeled text data using filters for metadata, model inferences, and other attributes like embedding similarity. Find the most relevant data to label from your large-scale datasets and send the data directly to your labeling project in just a few clicks.Learn more
Achieve up to 65% in labeling efficiency gains with model-assisted labeling – use your large language model to pre-label data, and let your team of labelers focus on corrective actions to generate ground truth so they don’t need to start from scratch.
Save time by uploading your foundational model predictions directly into your annotation experience. Simply pick any foundational model (GPT, BERT, etc) including ones that have already been re-trained which are closest to your use case (KeyBERT, finBERT, LEGAL-BERT).
Access the world’s best data labeling teams to label your data on demand, at scale. We offer support in numerous domains, including content moderation in over 20 languages.Learn more
Retail and ecommerce
How a Fortune 500 retailer improves training data quality for their conversational AI applications
The data science team wanted to find faster ways to annotate conversational text from shopping chatbots and label inventory images for their object detection and classification models, which included tens of millions of diverse product SKUs.
Labelbox Annotate, which provided intuitive text and image editors with the ability to tag entity relationships (NER) for conversations. Integrations with Google BigQuery via the Labelbox Python SDK to automate manual workflows and data import.
Labeled data accuracy improved by an estimated 25% through Labelbox's quality assurance systems, review workflows and real-time collaboration. Labelbox’s Boost team was able to deliver high-quality data (with 95% accuracy in labeled data) and at a 25% reduction in turnaround time compared to similar services.
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