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It's in the repo:
You first create embeddings. What is this? It's an n-dimensional vector space with your tweets 'embedded' in that space. Each word is an n-dimensional vector in this space. The vectorization is supposed to maintain 'semantic distance'. Basically, if two words are very close in meaning or related (by say frequently appearing next to each other in corpus) they should be 'close' in some of those n-dimensions as well. The result at the end is the '.bin' file, the 'semantic model' of your corpus.
https://github.com/dbasch/semantic-search-tweets/blob/main/e...
For semantic search, you run the same embedding algorithm against the query and take the resultant vectors and do similarity search via matrix ops, resulting in a set of results, with probabilities. These point back to the original source, here the tweets, and you just print the tweet(s) that you select from that result set.
https://github.com/dbasch/semantic-search-tweets/blob/main/s...
Experts can chime in here but there are knobs such as 'batch size' and the functions you use to index. (cosine was used here.)
So the various performance dimensions of the process should also be clear. There is a fixed cost of making the embeddings of your data. There is a per-op embedding of your query, and then running the similarity algorithm to find the result set.
You can find some comparisons and evaluation datasets/tasks here: https://www.sbert.net/docs/pretrained_models.html
Generally MiniLM is a good baseline. For faster models you want this library:
https://github.com/oborchers/Fast_Sentence_Embeddings
For higher quality ones, just take the bigger/slower models in the SentenceTransformers library
