Anna Models contributed to the embeddings-benchmark/mteb repository by integrating support for the LGAI-Embedding-Preview model, expanding the benchmark’s evaluation coverage. She implemented this feature using Python, focusing on model integration and benchmark contribution. Her work involved adding the new model’s metadata, training datasets, and configuration, then registering it within the MTEB model overview to enable standardized evaluation and comparison. Anna validated the integration with existing benchmarking pipelines, ensuring compatibility and reducing setup time for future models. This addition allows teams to make data-driven decisions about embedding model adoption and performance improvements, reflecting a focused and well-executed engineering effort.