[D] Relying solely on sentence embeddings for vector search is yielding abysmal results. Coworker is saying he's experiencing the same but wondering if we're doing it wrong or if this is normal.

My team and I are currently trying to implement a search functionality for one of our products. As of now, we're trying to create a language model-based method and are comparing it against an Elasticsearch baseline (i.e., BM25).

The model that we've trained is a publicly available ELECTRA-based checkpoint. The model's been pre-trained on English and Korean data. We trained the model using sentence-level contrastive learning techniques introduced in various papers (e.g., the SimCSE model from EMNLP 2020). As of …

embeddings language language model machinelearning normal products search team vector vector search