Papers › Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing

Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing

9 Jan 2021EACL 2021 2arXiv:2101.03289archive 2025-07-28

Minh Van Nguyen, Viet Dac Lai, Amir Pouran Ben Veyseh, Thien Huu Nguyen

We introduce Trankit, a light-weight Transformer-based Toolkit for multilingual Natural Language Processing (NLP). It provides a trainable pipeline for fundamental NLP tasks over 100 languages, and 90 pretrained pipelines for 56 languages. Built on a state-of-the-art pretrained language model, Trankit significantly outperforms prior multilingual NLP pipelines over sentence segmentation, part-of-speech tagging, morphological feature tagging, and dependency parsing while maintaining competitive performance for tokenization, multi-word token expansion, and lemmatization over 90 Universal Dependencies treebanks. Despite the use of a large pretrained transformer, our toolkit is still efficient in memory usage and speed. This is achieved by our novel plug-and-play mechanism with Adapters where a multilingual pretrained transformer is shared across pipelines for different languages. Our toolkit along with pretrained models and code are publicly available at: https://github.com/nlp-uoregon/trankit. A demo website for our toolkit is also available at: http://nlp.uoregon.edu/trankit. Finally, we create a demo video for Trankit at: https://youtu.be/q0KGP3zGjGc.

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Code

nlp-uoregon/trankit officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Dependency Parsing Language Modeling Language Modelling Lemmatization Morphological Tagging Multilingual NLP Named Entity Recognition (NER)Part-Of-Speech Tagging Sentence Sentence segmentation Sequential sentence segmentation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Dependency Parsing	UD2.5 test	Trankit	Macro-averaged F1	87.06	#1 of 2	Archive leaderboard	report
Dependency Parsing	UD2.5 test	Stanza	Macro-averaged F1	83.06	#2 of 2	Archive leaderboard	report
Part-Of-Speech Tagging	UD2.5 test	Trankit	Macro-averaged F1	95.65	#1 of 2	Archive leaderboard	report
Part-Of-Speech Tagging	UD2.5 test	Stanza	Macro-averaged F1	94.21	#2 of 2	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: Adapter

Absolute Position Encodings Adam Adapter Attention BPE Dense Connections Dropout Label Smoothing Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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