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@@ -23,6 +23,9 @@ Since the latter dataset contains also sensitive data, it has not been made publ
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  An example of how the model can be used for named entity recognition is provided in this [Colab notebook](https://colab.research.google.com/drive/1-koUCwz4aU_UvyZxSW-Awwf5RQftFk5m).
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  ## Intended uses & limitations
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  The model has been trained to recognize the following named entities from a text in Finnish:
 
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  An example of how the model can be used for named entity recognition is provided in this [Colab notebook](https://colab.research.google.com/drive/1-koUCwz4aU_UvyZxSW-Awwf5RQftFk5m).
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+ 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
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+ recognition for state authority archives](https://library.imaging.org/archiving/articles/21/1/2).
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  ## Intended uses & limitations
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  The model has been trained to recognize the following named entities from a text in Finnish: