{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/trankit-a-light-weight-transformer-based","title":"Trankit: A Light-Weight Transformer-based Toolkit for Multilingual Natural Language Processing","arxiv_id":"2101.03289","date":"2021-01-09","proceeding":"EACL 2021 2","authors":["Minh Van Nguyen","Viet Dac Lai","Amir Pouran Ben Veyseh","Thien Huu Nguyen"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2101.03289v5","url_pdf":"https://arxiv.org/pdf/2101.03289v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"trankit-a-light-weight-transformer-based","repo_url":"https://github.com/nlp-uoregon/trankit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"dependency-parsing","task_name":"Dependency Parsing"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"lemmatization","task_name":"Lemmatization"},{"task_slug":"morphological-tagging","task_name":"Morphological Tagging"},{"task_slug":"multilingual-nlp","task_name":"Multilingual NLP"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"part-of-speech-tagging","task_name":"Part-Of-Speech Tagging"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-segmentation","task_name":"Sentence segmentation"},{"task_slug":"sequential-sentence-segmentation","task_name":"Sequential sentence segmentation"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"adapter","method_name":"Adapter"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[{"slug":"adapter","name":"Adapter","full_name":"Adapter"}],"results":[{"leaderboard":"/sota/dependency-parsing-on-ud2-5-test","task":"Dependency Parsing","dataset":"UD2.5 test","model":"Trankit","rank_in_archive_order":1,"of":2,"metrics":{"Macro-averaged F1":"87.06"},"uses_additional_data":false},{"leaderboard":"/sota/dependency-parsing-on-ud2-5-test","task":"Dependency Parsing","dataset":"UD2.5 test","model":"Stanza","rank_in_archive_order":2,"of":2,"metrics":{"Macro-averaged F1":"83.06"},"uses_additional_data":false},{"leaderboard":"/sota/part-of-speech-tagging-on-ud2-5-test","task":"Part-Of-Speech Tagging","dataset":"UD2.5 test","model":"Trankit","rank_in_archive_order":1,"of":2,"metrics":{"Macro-averaged F1":"95.65"},"uses_additional_data":false},{"leaderboard":"/sota/part-of-speech-tagging-on-ud2-5-test","task":"Part-Of-Speech Tagging","dataset":"UD2.5 test","model":"Stanza","rank_in_archive_order":2,"of":2,"metrics":{"Macro-averaged F1":"94.21"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2101.03289","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}