In this tutorial, learn how to improve the performance of large language models (LLMs) by utilizing a proxy tuning approach, which enables more efficient fine-tuning and better integration with the AI model.
Learn how to build an efficient pipeline with Hydra and MLflow
So, if you only need word-vectors, sure, just use Word2Vec. If you only need doc-vectors, use Doc2Vec in a mode that doesn't create or word-vectors (pure PV-DBOW, dm=0, dbow_words=1) or a Doc2Vec mode that also happens to create word-vectors but just choose to ignore them. If you need both from the same data, use a Doc2Vec mode that also creates word-vectors (like PV-DM dm=1 or PV-DBOW-with-interleaved-skip-gram-word-training, dm=0, dbow_words=1). If you need both but do it in two separate steps, you'll spend more time training, and the vectors won't be inherently compatible. –
gojomo
Nov 29 '18 at 12:54
gojomo
Nov 29 '18 at 12:54
-set hive.exec.orc.split.strategy=ETL; -- this will work only for specific values scan, if full table scan will be required anyway, use default (HYBRID) or BI.
