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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.
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language:
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- en
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metrics:
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- f1
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pipeline_tag: token-classification
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tags:
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- automotive
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---
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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.
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