readme: ---
  language:
  - en
  tags:
  - automotive
  ---
  
  WG-BERT (Warranty and Goodwill) is a pretrained encoder based model to analyze automotive entities in automotive-related texts. WG-BERT is trained by continually 
  pretraining the BERT language model in the automotive domain by using a corpus of automotive (workshop feedback) texts via the masked language modeling (MLM) approach.
  WG-BERT is further fine-tuned for automotive entity recognition (subtask of Named Entity Recognition (NER)) to extract components and their complaints out of automotive texts.
  The dataset for continual pretraining consists of ~ 4 million sentences. 
  The dataset for fine-tuning consists of ~5.500 gold annotated sentences by automotive domain experts.
  We choose as the training architecture the BERT-base-uncased version.
  
  Please contact Lukas Weber lukas-weber[at]hotmail[dot]de / lukas.l.weber[at]mercedes-benz[dot]com about any WG-BERT related issues and questions.
  