---
language:
- iu
base_model:
- cis-lmu/glot500-base
tags:
- inuktitut
- morpheme
---

The model Glot500-m-iuseg is a fine-tuned version of the Glot500-m model. It was fine-tuned to segment Inuktitut words by morpheme boundaries and is intended to be used as a pre-processing tool for the language.


The model found in this repository is our best performing fine-tuned model described in the paper: "Surface-Level Morphological Segmentation of Low-resource Inuktitut Using Pre-trained Large Language Models": https://aclanthology.org/2025.nodalida-1.69/
```bibtex
@inproceedings{stenlund-etal-2025-surface,
    title = "Surface-Level Morphological Segmentation of Low-resource {Inuktitut} Using Pre-trained Large Language Models",
    author = "Stenlund, Mathias  and
      Myneni, Hemanadhan  and
      Riedel, Morris",
    editor = "Johansson, Richard  and
      Stymne, Sara",
    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
    month = mar,
    year = "2025",
    address = "Tallinn, Estonia",
    publisher = "University of Tartu Library",
    url = "https://aclanthology.org/2025.nodalida-1.69/",
    pages = "688--696",
    ISBN = "978-9908-53-109-0",
    abstract = "Segmenting languages based on morpheme boundaries instead of relying on language independent segmenting algorithms like Byte-Pair Encoding (BPE) has shown to benefit downstream Natural Language Processing (NLP) task performance. This can however be tricky for polysynthetic languages like Inuktitut due to a high morpheme-to-word ratio and the lack of appropriately sized annotated datasets. Through our work, we display the potential of using pre-trained Large Language Models (LLMs) for surface-level morphological segmentation of Inuktitut by treating it as a binary classification task. We fine-tune on tasks derived from automatically annotated Inuktitut words written in Inuktitut syllabics. Our approach shows good potential when compared to previous neural approaches. We share our best model to encourage further studies on down stream NLP tasks for Inuktitut written in syllabics."
}
```

**Datasets used:**
The Nunavut Hansard Inuktitut–English Parallel Corpus 3.0 with Preliminary Machine Translation Results: https://aclanthology.org/2020.lrec-1.312/

**Method used:**
LLMSegm: Surface-level Morphological Segmentation Using Large Language Model: https://aclanthology.org/2024.lrec-main.933/