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README.md
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- automotive
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WG_BERT is a pre-trained
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pretraining the BERT language model in the automotive domain by using a corpus of automotive (workshop feedback) texts via the masked language modelling (MLM) approach.
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WG_BERT is further fine-tuned for automotive entity recognition (subtask of Named Entity Recognition (NER)) to extract components and its complaints out of automotive texts.
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The dataset for continual pretraining consists of ~ 4 million sentences.
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The dataset for fine-tuning consists of ~5.500 gold annotated sentences by automotive domain experts.
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- automotive
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WG_BERT is a pre-trained model to analyze automotive entities in automotive-related texts. WG_BERT is build by continual
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pretraining the BERT language model in the automotive domain by using a corpus of automotive (workshop feedback) texts via the masked language modelling (MLM) approach.
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WG_BERT is further fine-tuned for automotive entity recognition (subtask of Named Entity Recognition (NER)) to extract components and its complaints out of automotive texts.
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The dataset for continual pretraining consists of ~ 4 million sentences.
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The dataset for fine-tuning consists of ~5.500 gold annotated sentences by automotive domain experts.
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We choose as the training architecture the BERT-base-uncased version.
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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.
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