I would like to apply fine-tuning Bert to calculate semantic similarity between sentences. I search a lot websites, but I almost not found downstream about this.
I just found STS benchmark. I wonder if I can use STS benchmark dataset to train a fine-tuning bert model, and apply it to my task. Is it reasonable?
As I know, there are a lot method to calculate similarity including cosine similarity, pearson correlation, manhattan distance, etc. How choose for semantic similarity?
If you look at a rather popular paper in the field by Mueller and Thyagarajan, which is concerned with learning sentence similarity on LSTMs, they use a closely related dataset (the SICK dataset), which is also hosted by the SemEval competition, and ran alongside the STS benchmark in 2014.
Either one of those should be a reasonable set to fine-tune on, but STS has run over multiple years, so the amount of available training data might be larger.
As a great primer on the topic, I can also highly recommend the Medium article by Adrien Sieg (see here, which comes with an accompanied GitHub reference.
For semantic similarity, I would estimate that you are better of with fine-tuning (or training) a neural network, as most classical similarity measures you mentioned have a more prominent focus on the token similarity (and thus, syntactic similarity, although not even that necessarily). Semantic meaning, on the other hand, can sometimes differ wildly on a single word (maybe a negation, or the swapped sentence position of two words), which is difficult to interpret or evaluate with static methods.