lots-o/

gliner-bi-ko-small-v1

GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.

This particular version utilize bi-encoder architecture, where textual encoder is team-lucid/DeBERTa v3 small and entity label encoder is sentence transformer - BAAI/bge-m3.

Such architecture brings several advantages over uni-encoder GLiNER:

However, it has some drawbacks such as a lack of inter-label interactions that make it hard for the model to disambiguate semantically similar but contextually different entities.

Install or update the gliner package:

Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using GLiNER.from_pretrained and predict entities with predict_entities.

크리스토퍼 놀란 => 사람 이름
Christopher Nolan => 사람 이름
영국 => 국가
영화 감독 => 직업명
각본가 => 직업명
영화 프로듀서 => 직업명
2008년 => 날짜_연(년)
다크 나이트 => 영화/소설 작품명
다크 나이트 => 영화/소설 작품명
2008 => 날짜_연(년)
감독 => 직업명
배트맨 => 캐릭터 이름
히스 레저 => 사람 이름
조커 => 캐릭터 이름
2010년 => 날짜_연(년)
인셉션 => 영화/소설 작품명
2010 => 날짜_연(년)
크리스토퍼 놀란 => 사람 이름
감독 => 직업명

If you have a large amount of entities and want to pre-embed them, please, refer to the following code snippet:

Evaluate with the konne dev set :
The evaluation results presented in the table below, except for the values I provided, were derived from the following source: taeminlee/gliner_ko.