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The model performs named entity recognition from text input in Finnish. It was trained by fine-tuning bert-base-finnish-cased-v1, using 10 named entity categories. Training data contains for instance the Turku OntoNotes Entities Corpus, the Finnish part of the NewsEye dataset as well as an annotated dataset consisting of Finnish document data from the 1970s onwards, digitized by the National Archives of Finland. Since the latter dataset contains also sensitive data, it has not been made publicly available. An example of how the model can be used for named entity recognition is provided in this Colab notebook. Motivations behind model development and the data selection and annotation processes have been described in more detail in the article Making sense of bureaucratic documents – Named entity recognition for state authority archives.
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