A fast, lightweight and easy-to-use Python library for splitting text into semantically meaningful chunks.

The code used to create and update the Open Australian Legal Corpus, the first and only multijurisdictional open corpus of Australian legislative and judicial documents.

The code used to evaluate embedding models on the Massive Legal Embedding Benchmark (MLEB).

Guides and code illustrating how to use Isaacus AI models in practice to solve real problems.

The code used to create and update the Open Australian Legal Embeddings, the first open-source embeddings of Australian legislative and judicial documents.

An easy-to-use Python library for merging PyTorch models.

A Python library for interacting with the Isaacus API.

A reasoning-intensive benchmark and evaluation methodology for assessing the end-to-end, real-world performance of legal RAG systems.

A server-side JavaScript/TypeScript library for interacting with the Isaacus API.

The training code behind EmuBert, the largest open-source masked language model for Australian law.

Scripts for evaluating extractive question answering models on the LegalQAEval legal question answering benchmark.

The OpenAPI specification for the Isaacus API

A Python library for interacting with AWS SageMaker AI deployments of the Isaacus API.