
Matryoshka Embeddings with Aditya Kusupati, Zach Nussbaum, and Zain Hasan - Weaviate Podcast #89!
Weaviate Podcast
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.
00:00
Transcript
Play full episode
Remember Everything You Learn from Podcasts
Save insights instantly, chat with episodes, and build lasting knowledge - all powered by AI.