Extract any entity from text with GLiNER
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.
By the end of this article you will know what GLiNER is and how to use it in Python to do classification of any token, on any text.
In summary, by reading this article you will learn
- What is GLiNER
- Why it is potentially revolutionary
- How to implement it in Python
- Limitations of GLiNER
GLiNER was published in a scientific paper, present at the link below
