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Distilkobert

DistilKoBERT is a distilled Korean-language BERT model derived from KoBERT, with three layers instead of the original 12. The repository provides pretrained-model loading through Hugging Face Transformers and a KoBERT tokenizer for Korean text.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Distilkobert — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use it when a project needs a compact Korean BERT-family model and can accommodate the documented Transformers interface and tokenizer setup.

Who it is for

  • Developers building Korean-language NLP systems
  • Researchers prototyping Korean text classification, named-entity recognition or question-answering tasks

Documented uses

  • Load pretrained Korean text representations in a Transformers-based Python project.
  • Tokenize Korean text and convert tokens to IDs.
  • Use the model as a starting point for task-specific Korean NLP work.
Before you choose

Requirements & limits

The public GitHub repository specifies an Apache-2.0 license.

  • The repository documents PyTorch 1.1.0 and Transformers 2.9.1 as dependencies.
  • The model has three layers and a reported size of 108 MB.
  • DistilBERT omits token-type embeddings and a pooler, so existing BertModel code may require changes.
  • The repository reports task benchmark results, but performance on a particular dataset is not assured.

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

Load DistilKoBERT through Transformers

Load the pretrained DistilKoBERT model using the repository’s documented Transformers interface.

Before you start

  • PyTorch 1.1.0
  • Transformers 2.9.1

Steps

  1. Import DistilBertModel from transformers.

  2. Call DistilBertModel.from_pretrained with the model identifier monologg/distilkobert.

Expected outcome

The pretrained DistilKoBERT model is loaded as a Transformers DistilBertModel.

Guide limitations

  • The repository describes model loading, not a complete task pipeline or fine-tuning procedure.
  • DistilBERT does not use token_type_ids or a pooler; code written for BertModel may need adjustment.
Documentation 1 ↗
Source-based practical guide

Prepare Korean text with the KoBERT tokenizer

Tokenize Korean text using the tokenizer provided for KoBERT and DistilKoBERT.

Before you start

  • The repository’s tokenization_kobert.py file
  • Transformers 2.9.1

Steps

  1. Copy tokenization_kobert.py from the repository root into your project.

  2. Import KoBertTokenizer from tokenization_kobert.

  3. Load the tokenizer with KoBertTokenizer.from_pretrained using monologg/distilkobert.

  4. Call tokenize on Korean text; use convert_tokens_to_ids to map tokens to IDs if needed.

Expected outcome

Korean text is segmented into tokens and can be converted to token IDs.

Guide limitations

  • The README says KoBERT and DistilKoBERT share the same tokenizer.
  • This guide covers tokenization, not model inference or a downstream application.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
17 October 2026
Original website title
monologg/DistilKoBERT · Official homepage title · retained in its source language
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