Maarten Van Segbroeck
GLiNER Models for PII Detection through Fine-Tuning on Gretel-Generated Synthetic Documents
Gretel fine-tuned, synthetically-enhanced GLiNER models for better PII & PHI detection—datasets included.
Read more...An Awesome Synthetic Multilingual Prompts Dataset
Gretel's latest open synthetic dataset aims to enhance LLM interactions and contributes to the popular 'awesome-chatGPT-prompts' GitHub repository.
Read more...Fine-Tuning CodeLlama on Gretel's Synthetic Text-to-SQL Dataset using Amazon SageMaker JumpStart
Fine-tune CodeLlama with Gretel's Synthetic Text-to-SQL on BIRDBench, achieving a 36% relative improvement in EX and 38% in VES.
Read more...RAG Model Evaluation with Azure AI and Gretel Navigator
Leveraging Gretel Navigator to Create Diverse and Quality-Driven Question-Truth Pairs for RAG Evaluation
Read more...Introducing Gretel MLOps
Use Gretel's synthetic data platform to replace, augment, or balance training datasets within MLOps pipelines like Vertex AI, Azure ML, and Amazon SageMaker.
Read more...Generate Question-Truth Pairs from Documents with Gretel Navigator
Learn how to generate question-answer pairs from documents using a unique application built using Gretel Navigator.
Read more...Synthetic Data, Real Privacy: Automating Secure Workflows with Gretel and Amazon SageMaker
Generate private and shareable data automatically by triggering Gretel jobs in Amazon SageMaker
Read more...Synthesizing dialogs for better conversational AI
Create high-quality synthetic datasets of conversational dialogs safely tuned on your private, sensitive data with Gretel.
Read more...How to Generate Best-in-Class Synthetic Time Series Data
Use Gretel DGAN and Gretel Tuner to generate time series data that accurately mirrors complex business rules and sequences
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