Machine Learning16 min reading time

How I Reproduced BM25, Dense Retrieval, and SPLADE on a 16GB MacBook

Towards Data Science
Read full post
A researcher successfully reproduced BM25, dense retrieval, and SPLADE document retrieval methods on a 16GB MacBook using Castorini's Anserini and Pyserini toolkits, matching expected benchmark scores. The process revealed practical challenges like memory crashes and gated model access issues not documented officially. This work provides a reliable baseline for evaluating retrieval quality in systems like RAG, emphasizing the importance of verifying document ranking before other enhancements.

More in Machine Learning

Machine Learning4 min read

Arlequin AI raises €28M to build novel AI models that learn complex relationships at scale

SiliconANGLE
Machine Learning4 min read

DeepSeek launches V4.1-Flash and retires V4-Pro, its flagship model

The Next Web
Machine Learning3 min read

Nvidia and Palantir fine-tune a 30B Nemotron model for Nvidia’s supply chain. It beats a model 18 times its size.

Covered by 3 sources