Extract any entity from text with GLiNER

GLiNER is an NER model that can identify any type of entity using a bidirectional transformer encoder (similar to BERT) that outperforms ChatGPT and other LLMs in zero-shot token classification tasks

Andrea D'Agostino

6 min read

·

Mar 24, 2024

--

6

Listen

Share

Press enter or click to view image in full size

Those who have worked in the past with the NER (named entity recognition) paradigm know well the value of having a performing model for the task on which it has been trained.

In fact, NER models are extremely useful for data mining and textual analysis tasks — they are the foundation of every digital intelligence task and in myriad tasks linked to larger and more complex data science pipelines.

Those who do NER also know how complex it is to train such a model due to the enormous amount of labels to be specified during the training phase. Libraries like SpaCy and transformer-based Hugging Face models have greatly helped data scientists develop NER models in an increasingly efficient manner, which still improves the process up to a certain point.

In this article we will look together at the GLiNER paradigm, a new technique for entity extraction that combines the classic NER paradigm with the power of LLMs.