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A LSTM/Transformer/dilated convolution sequence labeler
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sticker is a sequence labeler using neural networks.


sticker is a sequence labeler that uses either recurrent neural networks or dilated convolution networks. In principle, it can be used to perform any sequence labeling task, but so far the focus has been on:

  • Part-of-speech tagging
  • Topological field tagging
  • Dependency parsing

Where to go from here


sticker uses techniques from or was inspired by the following papers:


You can report bugs and feature requests in the sticker issue tracker.


sticker is licensed under the Blue Oak Model License version 1.0.0. The Tensorflow protocol buffer definitions in tf-proto are licensed under the Apache License version 2.0.

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