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Esupar

Esupar is a Python library that loads language-specific NLP pipelines to tokenize text, assign part-of-speech tags and produce dependency parses using Transformers and SuPar.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Esupar — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Esupar when Python code needs tokenization and grammatical or dependency analysis for one of its documented languages.

Who it is for

  • Python developers adding linguistic analysis to text-processing applications
  • NLP practitioners working with supported languages and Universal Dependencies output
  • Notebook users who want to try a language pipeline in Google Colaboratory

Documented uses

  • Split text into tokens and assign grammatical categories.
  • Analyze syntactic relationships between words in a sentence.
  • Prepare language-annotated text for downstream NLP applications.
Before you choose

Requirements & limits

The repository identifies Esupar as MIT-licensed and documents installation on Linux and Google Colaboratory.

  • Model options vary by language and include distinct variants for some languages.
  • The documented interface loads a model by name, then processes text through the resulting pipeline.
  • The README documents Linux, Google Colaboratory and a separate Cygwin64 installation path.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install esupar on Linux and annotate Japanese text

Install esupar for the current user, then load its Japanese pipeline to produce token, part-of-speech, and dependency output.

Before you start

  • Linux
  • Python 3 with pip3

Steps

  1. Install the package with the command below.

    Documented command
    pip3 install esupar --user

  2. In Python, import esupar and load the Japanese pipeline with nlp = esupar.load("ja").

  3. Pass Japanese text to the pipeline, for example doc = nlp("太郎は花子が読んでいる本を次郎に渡した").

  4. Print the returned document with print(doc) to view its Universal Dependencies-formatted annotations.

Expected outcome

The example returns token-level output with part-of-speech tags and dependency relations in a Universal Dependencies-style format.

Guide limitations

  • The installation instructions here are specifically for Linux.
  • The documented example uses Japanese; pipeline availability varies by language and model option.
Documentation 1 ↗
Source-based practical guide

Install esupar in Google Colaboratory

Install the esupar package in a Colab notebook and open the project's example notebook.

Before you start

  • A Google Colaboratory notebook

Steps

  1. Run !pip install esupar in a notebook cell.

  2. Open the project notebook at the linked Colab URL for the documented usage example.

Expected outcome

esupar is installed in the notebook environment, and the project's example notebook is available to follow.

Guide limitations

  • The installation step is documented for Google Colaboratory; no other notebook environment is specified.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
KoichiYasuoka/esupar · Official homepage title · retained in its source language
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