Update README.md

README.md

@@ -83,7 +83,6 @@ year="2022",
 publisher="Springer International Publishing",
 address="Cham",
 pages="333--342",
-abstract="Currently, there is a vast amount of archival finding aids in Portuguese archives, however, these documents lack structure (are not annotated) making them hard to process and work with. In this way, we intend to extract and classify entities of interest, like geographicallocations, people's names, dates, etc. For this, we will use an architecture that has been revolutionizing several NLP tasks, Transformers, presenting several models in order to achieve high results. It is also intended to understand what will be the degree of improvement that this new mechanism will present in comparison with previous architectures. Can Transformer-based models replace the LSTMs in NER? We intend to answer this question along this paper.",
 isbn="978-3-031-04819-7"
 }