Polite Bert is, as the name implies, a BERT model trained to classify a given sentence on a scale of politeness:
Polite Bert was trained by fine-tuning a
BERT model on annotated politeness-level data.
The model was trained using SFT for 4 epochs, with a batch size 16, and a max sequence length of 128 tokens.
The training data consisted of 2000 annotated sentences. This training data was composed of the following:
While we manually labelled the first 1000 sentences, the 1000 sentences from 4ChanPol were automatically set to Not Polite.
These source datasets were chosen due to their likelihood of containing distinct, but pronounced, politeness levels (hate speech from 4chan, formal and polite speech from hotel staff and parliament members, etc)
Apache 2.0 license.
Made by Diogo Glória-Silva. PhD Student at NOVA FCT and Affiliated PhD Student at CMU
