Smallest Possible Dataset for Text Classification using BERT What are your experiences for appropriate dataset sizes for usual text classification tasks using a finetuned BERT such as sentiment analysis?

~100 examples

~1000 examples

...

~10000000 examples

What are your experiences?
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.