This model was created to specialize on political speeches, interviews and press briefings of English-speaking politicians.

The model was initialized using the pre-trained weights of BERT_BASE and trained for 20 epochs on the standard MLM task with default parameters. The used learning rate was 5e-5 with a linearly decreasing schedule and AdamW. The used batch size is 8 per GPU while beeing trained on two Nvidia GTX TITAN X. The rest of the used configuration is the same as in AutoConfig.from_pretrained('bert-base-uncased'). As a tokenizer the default tokenizer of BERT was used (BertTokenizer.from_pretrained('bert-base-uncased'))

PolitBERT was trained on the following dataset, which has been split up into single sentences: https://www.kaggle.com/mauricerupp/englishspeaking-politicians

To predict a missing word of a sentence, the following pipeline can be applied:

from transformers import pipeline, BertTokenizer, AutoModel

fill_mask = pipeline("fill-mask", 
                     model=AutoModel.from_pretrained('maurice/PolitBERT'), 
                     tokenizer=BertTokenizer.from_pretrained('bert-base-uncased'))

print(fill_mask('Donald Trump is a [MASK].'))

Evaluation Loss: Training Loss: Learning Rate Schedule: