Transfer learning has led to large gains in performance for nearly all NLP tasks while making downstream models easier and faster to train. This has also been extended to low-resourced languages, with some success. We investigate the properties of cross-lingual transfer learning between ten low-resourced languages, from the perspective of a named entity recognition task. We specifically investigate how much adaptive fine-tuning and the choice of transfer language affect zero-shot transfer performance. We find that models that perform well on a single language often do so at the expense of geneResearch goal: How does the typological distance between source and target languages influence the optimal order of sequential fine-tuning tasks for cross-lingual transfer on low-resource NLP benchmarks?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.