GLiNER: A Generalist and Lightweight Model for Named Entity Recognition
GLiNER stands out as an innovative model designed for Named Entity Recognition (NER), employing a bidirectional transformer encoder similar to BERT. Its primary ability is to recognize any type of entity, which makes it a practical alternative to more conventional models that are limited to pre-determined entities. While Large Language Models (LLMs) offer flexibility, they can be cumbersome and costly in environments with limited resources. GLiNER bridges this gap by providing a scalable and efficient solution for NER tasks.
Key Features of GLiNER
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Versatile Entity Recognition: GLiNER is not restricted to a fixed set of entity types, unlike traditional models. Its adaptability allows it to identify diverse entities across different domains.
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Efficient and Scalable: Designed to be lightweight, GLiNER offers a resource-friendly alternative to large, costly models, making it suitable for various practical applications.
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Built on Modern Architecture: By utilizing a bidirectional transformer encoder, GLiNER leverages state-of-the-art technology for natural language processing, ensuring robust performance.
Installation and Usage
Getting started with GLiNER is straightforward. Users can install the library via pip:
!pip install gliner
After installation, the GLiNER class can be imported and utilized to predict entities within a text. This involves loading a pre-trained model and calling specific functions to extract named entities:
from gliner import GLiNER
# Load the GLiNER model
model = GLiNER.from_pretrained("urchade/gliner_mediumv2.1")
# Define sample text
text = "Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985)..."
# Specify entity labels
labels = ["Person", "Award", "Date", "Competitions", "Teams"]
# Predict entities
entities = model.predict_entities(text, labels, threshold=0.5)
for entity in entities:
print(entity["text"], "=>", entity["label"])
This straightforward process enables users to extract relevant entities from text efficiently, supporting varied needs in research or application settings.
Example Notebooks
GLiNER offers a range of example notebooks to help users explore its capabilities. These notebooks cover areas such as model fine-tuning, ONNX conversion, and synthetic data generation.
- To access these resources, visit the Example Notebooks on their GitHub repository.
Collaborative and Community-Driven
The development of GLiNER has been supported by the collaboration of several entities and the open-source community. Contributions from experts and enthusiasts alike have been integral to the project's success, ensuring its growth and sustainability.
Support and Funding
GLiNER is backed by both F.initiatives and the Laboratoire Informatique de Paris Nord. F.initiatives has a long-standing reputation in public funding strategies relating to R&D, Innovation, and Investments (R&D&I), providing expert guidance at every development stage of public funding strategies.
In summary, GLiNER represents a significant advancement in the field of Named Entity Recognition. Its adaptability, efficiency, and reliance on cutting-edge technology make it a vital tool for professionals and researchers seeking reliable and scalable NER solutions.