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Matryoshka Embeddings with Aditya Kusupati, Zach Nussbaum, and Zain Hasan - Weaviate Podcast #89!

Weaviate Podcast

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Investing Time in Data Preparation for Model Training

Investing significant time in data work and cleaning is crucial for training embedding models. The process involves two stages: large-scale contrastive pre-training with around 240 million pairs of semantically related sentences, and smaller scale contrastive fine-tuning including hard negatives to enhance retrieval performance. The addition of hard negatives aids in pushing model performance further.

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