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Try out some of our hosted demos to get a feel for Zeno.

Image Classification with CIFAR-10

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For this classic image classification task, we are using Zeno to compare 4 simple PyTorch CNN models trained for different number of epochs. It includes multiple distill functions for image features such as brightness and certain colors. This model includes model projections, which can be used to find potential model errors.

Audio Transcription

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We wanted to compare Open AI's Whisper model with existing off-the-shelf audio transcription models, in this case the Silero model. For our evaluation dataset we used the Speech Accent Archive, a collection of audio clips of people from around the world saying the same phrase.

Can you find differences in model performance across geographic regions and other speaker features?

Sensor Data Exploration

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Zeno can also be used for unstructured data exploration. In this demo, we explore the MotionSense dataset of IPhone sensor data. This demo could be extended to include activity classification models.

Can you find interesting sensor patterns between the different activities?

Q&A Chatbots

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Chatbots like ChatGPT are an increasingly popular application of language models, and libraries like LangChain are making it much easier to implement LLM-based applications. In this demo we use Zeno to explore how well a LangChain model for answering questions over a Notion database performs.

Auditing Image Generation Models

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Zeno can also be used for analyses of generative models. In this example we are exploring the DiffusionDB dataset. Instead of a typical aggregate metric, we measure the average NSFW level of the prompt and images.

Can you find potential biases in diffusion models, e.g. different levels of NSFW for different types of prompt keywords?

Sentiment Analysis

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We can compare off-the-shelf hugging face sentiment classification models.

Tabular Logistic Regression

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You can also use Zeno for classic tabular model analysis. Here we trained multiple different models to predict people's income on the adult dataset.