The purpose of the study was to investigate how different types of Natural Language Processing (NLP) models can be fine-tuned for use within Named Entity Recognition (NER) and how these models work differently. The aim of this was to identify which type of model works best when it comes to entity extraction. To test the models, they were fine-tuned using PyTorch and Huggingface Transformers libraries. Here, it was also investigated which other techniques are not based on machine learning that can be used to solve the problem of entity extraction. The results of these tests showed that the KB/BERT-base-swedish-cased model worked best. Also, there are other techniques that can be used to solve the problem of entity extraction but that it is machine learning that is the most effective. KB/BERT-base-swedish-cased was shown to be the best model but it also proved that it may be due to the purpose that you need to use the model within. The study limitations have been that there was no large data set that could be used for training and that there was a lack of time to produce a new dataset.