The toolkit allows users to perform tasks such as semantic matching, fuzzy deduplication, ranking, and clustering. It can be used for exploratory analysis and utility applications due to its fast and portable size. WordLlama also provides the ability to extract token embeddings from a model. The project is licensed under the MIT License and the creators request that users cite the software in their research or projects.
Key takeaways:
- WordLlama is a fast, lightweight NLP toolkit optimized for CPU hardware, capable of tasks like fuzzy-deduplication, similarity and ranking.
- It recycles components from large language models to create efficient and compact word representations, improving on benchmarks while being substantially smaller in size.
- WordLlama offers features like Matryoshka Representations, Low Resource Requirements, Binarization, and Numpy-only inference.
- It can be used for tasks like semantic matching, fuzzy deduplication, ranking, clustering, and can be trained on consumer GPUs in a few hours.