This project is a wrapper for integrating GLiNER, a Named Entity Recognition (NER) model, with the SpaCy Natural Language Processing (NLP) library. GLiNER, which stands for Generalized Language INdependent Entity Recognition, is an advanced model for recognizing entities in text. The SpaCy wrapper enables easy integration and use of GLiNER within the SpaCy environment, enhancing NER capabilities with GLiNER's advanced features.

For GliNER to work properly, you need to use a Python version 3.7-3.10

• Integrates GLiNER with SpaCy for advanced NER tasks.
• Customizable chunk size for processing large texts.
• Support for specific entity labels like 'person' and 'organization'.
• Configurable output style for entity recognition results.

To install this library, install it via pip:

pip install gliner-spacy

To use this wrapper in your SpaCy pipeline, follow these steps:

1. Import SpaCy.
2. Create a SpaCy Language instance.
3. Add the gliner_spacy component to the SpaCy pipeline.
4. Process text using the pipeline.

Example code:

import spacy

nlp = spacy.blank("en")
nlp.add_pipe("gliner_spacy")
text = "This is a text about Bill Gates and Microsoft."
doc = nlp(text)

for ent in doc.ents:
    print(ent.text, ent.label_)

Bill Gates person
Microsoft organization

import spacy

custom_spacy_config = { "gliner_model": "urchade/gliner_multi",
                            "chunk_size": 250,
                            "labels": ["people","company"],
                            "style": "ent"}
nlp = spacy.blank("en")
nlp.add_pipe("gliner_spacy", config=custom_spacy_config)

text = "This is a text about Bill Gates and Microsoft."
doc = nlp(text)

for ent in doc.ents:
    print(ent.text, ent.label_, ent._.score)

#Output
# Bill Gates people 0.9967108964920044
# Microsoft company 0.9966742992401123    

The default configuration of the wrapper can be modified according to your requirements. The configurable parameters are:

• gliner_model: The GLiNER model to be used.
• chunk_size: Size of the text chunk to be processed at once.
• labels: The entity labels to be recognized.
• style: The style of output for the entities (either 'ent' or 'span').
• threshold: The threshold of the GliNER model (controls the degree to which a hit is considered an entity)
• map_location: The device on which to run the model: cpu or cuda

Contributions to this project are welcome. Please ensure that your code adheres to the project's coding standards and include tests for new features.