Tables & Artifacts Reports

This article introduces Weights & Biases Artifacts, which offer lightweight dataset versioning, and gives an easy-to-follow example so that you can see how they work.

How to version and interactively explore data and predictions across train/val/test with W&B's new Tables feature

Visualize and analyze protein sequences and 3D structures with W&B Tables

Interactively exploring ML data and predictions in the audio domain with our new Tables feature

A guide on how to log and organize text data and language model predictions with our old friend William Shakespeare

Visualize and explore named entities with spaCy in W&B

In this article, we analyze the sentiment of stock market news headlines with the HuggingFace framework using a BERT model fine-tuned on financial texts, FinBERT.

This article includes first runs and observations in the SafeLife benchmark in Weights & Biases, which was developed in collaboration with the Partnership on AI (PAI).

This article introduces the SafeLife Benchmark Launch with Partnership on AI and explains its importance for the future development of ethical AI.

This article explores semantic segmentation for scene parsing on Berkeley Deep Drive 100K (BDD100K) including how to distinguish people from vehicles.

This article explains how to log and explore semantic segmentation masks, and how to interactively visualize models' predictions with Weights & Biases.

In this article, we take a look at how to log and explore bounding boxes with Weights & Biases

Visualize LIDAR point clouds from the Lyft dataset, annotate with 3D bounding boxes, and explore interactively!

Set up powerful and flexible analysis across runs logging structured data

Log, explore, and dynamically query model predictions across training epochs

Log, visualize, and dynamically explore your data