Smallest Possible Dataset for Text Classification using BERT
Sorry, but there’s no rule and amount we are able to quantify. 

I’ve used it (multilingual) for 700 texts with a 20 multilabel classification and I had worse results than with a custom deep net (but with pretrained word embeddings).

But you know, in fact these questions are hard to answer. Why? Because with very low quantities of data, you can’t accurately assess the performance of a model - it is statistically insignificant.