A fast, lightweight and easy-to-use Python library for splitting text into semantically meaningful chunks.
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            Updated
            Oct 28, 2025 
- Python
A fast, lightweight and easy-to-use Python library for splitting text into semantically meaningful chunks.
The code used to evaluate embedding models on the Massive Legal Embedding Benchmark (MLEB).
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.
Scripts for evaluating extractive question answering models on the LegalQAEval legal question answering benchmark.
A server-side JavaScript/TypeScript library for interacting with the Isaacus API, the world's first legal AI API.
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