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 the BERT-base-uncased as the base model.
{"language": ["en"], "tags": ["automotive"], "metrics": ["f1"], "pipeline_tag": "token-classification"}
transformers pytorch bert token-classification automotive en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #automotive #en #autotrain_compatible #endpoints_compatible #region-us 

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 the BERT-base-uncased as the base model.
TAGS
#transformers #pytorch #bert #token-classification #automotive #en #autotrain_compatible #endpoints_compatible #region-us 

TAGS
#transformers #pytorch #bert #token-classification #automotive #en #autotrain_compatible #endpoints_compatible #region-us 
