Update README.md

README.md

@@ -5,11 +5,11 @@ tags:
 - 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 
-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.
-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.
+WG-BERT is a pre-trained 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.
+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.