Update README.md

README.md

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 # Unsilencing Dutch Colonial Archives via Automated Entity Recognition 
 
-Colonial archives are at the center of increased interest from a variety of perspectives, as they contain traces of historically marginalized people. Unfortunately, like most archives, they remain difficult to access due to significant persisting barriers. We focus here on one of them: the biases to be found in historical findings aids, such as indexes of person names, which remain in use to this day. In colonial archives, indexes can perpetrate silences by omitting to include mentions of historically marginalized persons. In order to overcome such limitation and pluralize the scope of existing finding aids, we propose using automated entity recognition. To this end, we contribute a fit-for-purpose annotation typology and apply it on the colonial archive of the Dutch East India Company (VOC). **We release a corpus of nearly 70,000 annotations as a shared task, for which we provide strong baselines using state-of-the-art neural network models.**
+Colonial archives are at the center of increased interest from a variety of perspectives, as they contain traces of historically marginalized people. Unfortunately, like most archives, they remain difficult to access due to significant persisting barriers. We focus here on one of them: the biases to be found in historical findings aids, such as indices of person names, which remain in use to this day. In colonial archives, indexes can perpetrate silences by omitting to include mentions of historically marginalized persons. In order to overcome such limitation and pluralize the scope of existing finding aids, we propose using automated entity recognition. To this end, we contribute a fit-for-purpose annotation typology and apply it on the colonial archive of the Dutch East India Company (VOC). **We release a corpus of nearly 70,000 annotations as a shared task, for which we provide strong baselines using state-of-the-art neural network models.**
 
 # Paper
 
 The associated paper is currently under peer review.
 
 # Data
 
+The dataset, that is the annotations are available in 2 formats:
+
+1) brat annotation output: [here](data/annotated_data)
+2) data in iob format: [here](processed_data)
 
 ## Annotation Schema
 Please refer to the documentation of the annotation typology [here](data/README.md)