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README.md
@@ -558,8 +558,8 @@ needed to construct the training data.
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where we may be limited to < 1GB of RAM per process depending on the machine
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configuration. As much as possible libpostal uses contiguous arrays, tries
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(built on contiguous arrays), bloom filters and compressed sparse matrices to
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-keep memory usage low. It's conceivable that libpostal could even be used on
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-a mobile device, although that's not an explicit goal of the project.
+keep memory usage low. It's possible to use libpostal on a mobile device with
+models trained on a single country or a handful of countries.
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3. **Performance**: this is last on the list for a reason. Most of the
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optimizations in libpostal are for memory usage rather than performance.
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