2 ms·

Thanks for the link! I compared BERT[1], distilbert[2], mpnet[3] and minilm[4] in the past. But the results I got "out of the box" for semantic search were not

by leobg 5y ago

Thanks for the link! I compared BERT[1], distilbert[2], mpnet[3] and minilm[4] in the past. But the results I got "out of the box" for semantic search were not better than using fastText, which is orders of magnitude faster. BERT and distilbert are 400x slower than fastText, minilm 300x, and mpnet 700x. At least if you are using a CPU-only machine. USE, xlmroberta and elmo were even worse (5,000 - 18,000x slower). I also love how fast and easy it is to train your own fastText model. [1]: distiluse-base-multilingual-cased-v2 [2]: multi-qa-MiniLM-L6-cos-v1 [3]: multi-qa-mpnet-base-cos-v1 [4]: multi-qa-MiniLM-L6-cos-v1

jkb79 5y ago
Vector models are nothing but representation learning and applying the model out-of-domain usually gives worse results than plain old BM25. See https://arxiv.org/abs/2104.08663 https://arxiv.org/abs/2104.08663 A concrete example is DPR which is a state of the art dense retriever model for wikipedia for question answering, when applying that model on MS Marco passage ranking it performs worse than plain BM25.