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.
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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.
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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.
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RAG Model Evaluation with Azure AI and Gretel Navigator

Leveraging Gretel Navigator to Create Diverse and Quality-Driven Question-Truth Pairs for RAG Evaluation
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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.
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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.
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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
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Synthesizing dialogs for better conversational AI

Create high-quality synthetic datasets of conversational dialogs safely tuned on your private, sensitive data with Gretel.
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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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