Hi @eirikdahlen, from the docs 7 one sees that truncation=True is equivalent to the longest_first strategy which just truncates all tokens beyond the maximum context size of the model (e.g. 512 for BERT-base).
The other strategies are only_first and only_second which refer to whether one should apply the truncation exclusively on the first or second set of inputs, e.g. if you’re doing something like entailment 3 where the inputs are a premise and hypothesis.
Since you’re dealing with long texts, you might want to check out the LongFormer model 13 - it can handle input sequences of 4096 tokens so should be able to capture more context in your use case