https://github.com/stickeritis/sticker

Succeeded by SyntaxDot: https://github.com/tensordot/syntaxdot
https://github.com/stickeritis/sticker

dependency-parsing dilated-convolution finalfusion lstm part-of-speech-tagger rust tensorflow topological-fields transformer

Last synced: about 2 months ago
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Succeeded by SyntaxDot: https://github.com/tensordot/syntaxdot

• Host: GitHub
• URL: https://github.com/stickeritis/sticker
• Owner: stickeritis
• License: other
• Archived: true
• Created: 2019-02-15T09:35:54.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-03-19T10:08:03.000Z (over 3 years ago)
• Last Synced: 2024-04-21T20:38:05.484Z (7 months ago)
• Topics: dependency-parsing, dilated-convolution, finalfusion, lstm, part-of-speech-tagger, rust, tensorflow, topological-fields, transformer
• Language: Rust
• Homepage:
• Size: 1.13 MB
• Stars: 25
• Watchers: 4
• Forks: 2
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md
  • Codeowners: .github/CODEOWNERS

Awesome Lists containing this project

README

        
**Warning:** sticker is succeeded by

[SyntaxDot](https://github.com/tensordot/syntaxdot), which supports

*many* new features:

* Multi-task learning.

* Pretrained transformer models, suchs as BERT and XLM-R.

* Biaffine parsing in addition to parsing as sequence labeling.

* Lemmatization.

# sticker

**sticker** is a sequence labeler using neural networks.

## Introduction

sticker is a sequence labeler that uses either recurrent neural

networks, transformers, 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

* Named entity recognition

## Features

* Input representations:

  * [finalfusion](https://finalfusion.github.io/) embeddings with subword units

  * Bidirectional byte LSTMs

* Hidden representations:

  * Bidirectional recurrent neural networks (LSTM or GRU)

  * Transformers

  * Dillated convolutions

* Classification layers:

  * Softmax (best-N)

  * CRF

* Deployment:

  * Standalone binary that links against `libtensorflow`

  * Very liberal [license](LICENSE.md)

  * Docker containers with models

## Status

sticker is almost production-ready and we are preparing for release

1.0.0. Graphs and models crated with the current version **must** work

with sticker 1.x.y. There may still be breaking API or configuration

file changes until 1.0.0 is released.

## Where to go from here

* [Installing sticker](doc/INSTALL.md)

* [Pretrained models](doc/PRETRAINED.md)

* [Using sticker](doc/USAGE.md)

* [Training a sticker model](doc/TRAIN.md)

## References

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

* [Finding Function in Form: Compositional Character Models for Open

  Vocabulary Word

  Representation](https://aclweb.org/anthology/papers/D/D15/D15-1176/). Wang

  Ling, Chris Dyer, Alan W Black, Isabel Trancoso, Ramón Fermandez,

  Silvio Amir, Luís Marujo, Tiago Luís, 2015, Proceedings of the 2015

  Conference on Empirical Methods in Natural Language Processing

* [Transition-based dependency parsing with topological

  fields](https://aclweb.org/anthology/papers/P/P16/P16-2001/). Daniël

  de Kok, Erhard Hinrichs, 2016, Proceedings of the 54th Annual

  Meeting of the Association for Computational Linguistics

* [Viable Dependency Parsing as Sequence

  Labeling](https://www.aclweb.org/anthology/papers/N/N19/N19-1077/). Michalina

  Strzyz, David Vilares, Carlos Gómez-Rodríguez, 2019, Proceedings of

  the 2019 Conference of the North American Chapter of the Association

  for Computational Linguistics: Human Language Technologies

## Issues

You can report bugs and feature requests in the [sticker issue

tracker](https://github.com/stickeritis/sticker/issues).

## License

sticker is licensed under the [Blue Oak Model License version

1.0.0](LICENSE.md). The Tensorflow protocol buffer definitions in

`tf-proto` are licensed under the Apache License version 2.0. The

[list of contributors](CONTRIBUTORS) is also available.

## Credits

* sticker is developed by Daniël de Kok & Tobias Pütz.

* The Python precursor to sticker was developer by Erik Schill.

* Sebastian Pütz and Patricia Fischer reviewed a lot of code across

  the sticker projects.