Whitepapers

The latest in AI and NLP insights and research from the Datasaur team

Private LLMs: Definition, Spectrum, and a Buyer’s Framework

Defines what a Private LLM really means and why a clear standard is critical for security, compliance, and usability. It also provides a practical framework to evaluate options and design solutions tailored to your organization’s workflows.
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Enhancing Data Quality: Finding and Fixing Label Errors with Datasaur

Building a high-quality dataset is crucial but time-consuming. Through experiments and case studies, our approach has proven to improve dataset quality and, consequently, machine learning model performance.
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Datasaur Predictive Labeling: Achieve High-Performing AI Models with 60% Less Data Labeling Effort

By using Datasaur Predictive Labeling powered by SetFit, organizations can achieve high-performing AI models with 60% less data labeling effort
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Choosing the Right LLM: An Exploration into How Different Models Stack Up in Performance

With new models emerging frequently, boasting superiority over OpenAI, we dive into their strengths, weaknesses, and performance differences to uncover what sets them apart.
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Enhancing Language Model Distillation with Datasaur

LLMs advance AI by revolutionizing language understanding. However, they demand heavy resources, making them expensive and hard to debug. Model distillation simplifies these models while retaining their capabilities.
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Shifting from Model-Centric to Data-Centric MLOps

Working with Machine Learning (ML) can be quite challenging. This is where MLOps (Machine Learning Operations) comes in. MLOps provides a valuable framework for ML engineers and data scientists.
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Mongabay: First Indonesian Weak Supervised Dataset - Curated by Data Programming

Read more on how we utilize our own Data Programming feature to construct a weakly curated dataset sourced from Mongabay, an Indonesian conservation portal. This discovery was also featured at the South East Asian Language Processing workshop 2023.
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"We compared Datasaur to 55 other tools, and in that exhaustive comparison -- we found Datasaur to have the most complete suite of tooling."

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"Datasaur enabled us to automate our entire QA pipeline, we know what has been labeled (and the quality of each label) every 5 minutes without touching anything. It's all automated."

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"Integrating the platform with our AWS environment has been seamless, providing us with scalable data labeling capabilities."

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"We found the entire platform incredibly intuitive and easy to navigate. Onboarding was smooth and we were able to quickly adopt their automation tooling which was very important for us when considering a labeling platform."

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"We [Consensus] had a very complex and specific set of annotation needs. Datasaur was able to address those needs efficiently and effectively all while maintaining the personal touch you would expect from a start-up."

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"Our experience was pretty great. I enjoyed my time on Datasaur."

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"Instead of manually creating each project, we’re able to automate the project creation. Instead of manually scrolling through hundreds of medical labels, we can rely on search functions. This has saved admins and team members a lot of time in their project workflows."

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