To train meaningful vectors, you typically want to use a lot of text, like 1 billion words. So your 7000 likely won't be enough. Maybe you can find other similar texts from a different source that you can use.
Once you have a sense2vec model, you can then use the vectors to find more similar terms. Not sure if "since" is a good seed term here, because there are not that many similar expressions. It works better for things like (proper) nouns.
like always, very informative! you are right. I have only around 150000 tokens, many thanks, let me suppose that I will find another corpus, can I use the prodigy comments instead of scripts for pre-processing?
is there any other usage of sense2vec that I can use with the combination of NER to expand and improve my entities?
@robertto@ines hope you all are doing well, i am struggling on training custom sense2vec model for language other than English, please help me out, i have already prepared my data as expected, but couldnt figure out to feed the data to the model, any help is appreciated thank you,