Usage

🚀 Basic Use Case

After the installation of the GLiNER library, import the GLiNER class. Following this, you can load your chosen model with GLiNER.from_pretrained and utilize predict_entities to discern entities within your text.

from gliner import GLiNER

# Initialize GLiNER with the base model
model = GLiNER.from_pretrained("knowledgator/gliner-multitask-large-v0.5")

# Sample text for entity prediction
text = """
Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.
"""

# Labels for entity prediction
labels = ["Person", "Award", "Date", "Competitions", "Teams"]

# Perform entity prediction
entities = model.predict_entities(text, labels, threshold=0.5)

# Display predicted entities and their labels
for entity in entities:
    print(entity["text"], "=>", entity["label"])

Expected Output

Cristiano Ronaldo dos Santos Aveiro => Person
5 February 1985 => Date
Al Nassr => Teams
Portugal national team => Teams
UEFA Men's Player of the Year Awards => Award
European Golden Shoes => Award
UEFA Champions Leagues => Competitions
UEFA European Championship => Competitions
UEFA Nations League => Competitions

Local Run

You can load your local model and tokenizer with cache_dir parameter.

from gliner import GLiNER

# Initialize GLiNER with the base model
model = GLiNER.from_pretrained("gliner-multitask-large-v0.5", cache_dir="local_model")

# Sample text for entity prediction
text = """
Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time.
"""

# Labels for entity prediction
labels = ["Person", "Award", "Date", "Competitions", "Teams"]

# Perform entity prediction
entities = model.predict_entities(text, labels, threshold=0.5)

# Display predicted entities and their labels
for entity in entities:
    print(entity["text"], "=>", entity["label"])

Expected Output

Cristiano Ronaldo dos Santos Aveiro => Person
5 February 1985 => Date
Al Nassr => Teams
Portugal national team => Teams
UEFA Men's Player of the Year Awards => Award
European Golden Shoes => Award
UEFA Champions Leagues => Competitions
UEFA European Championship => Competitions
UEFA Nations League => Competitions

🏃‍♀️Using FlashDeBERTa

Most GLiNER models use the DeBERTa encoder as their backbone. This architecture offers strong token classification performance and typically requires less data to achieve good results. However, a major drawback has been its slower inference speed, and until recently, there was no flash attention implementation compatible with DeBERTa's disentangled attention mechanism.

To address this, FlashDeBERTa was introduced.

To use FlashDeBERTa with GLiNER, install it:

pip install flashdeberta -U

tip

Before using FlashDeBERTa, please make sure that you have transformers>=4.47.0.

GLiNER will automatically detect and use FlashDeBERTa. If needed, you can switch to the standard eager attention mechanism by specifying the attention implementation:

model = GLiNER.from_pretrained("urchade/gliner_mediumv2.1", _attn_implementation="eager")

FlashDeBERTa provides up to a 3× speed boost for typical sequence lengths—and even greater improvements for longer sequences.

🛠️Pipelines

GLiNER-Multitask models are designed to extract relevant information from plain text based on a user-provided custom prompt. The advantage of such encoder-based multitask models is that they enable efficient and more controllable information extraction with a single model that reduces costs on computational and storage resources. Moreover, such encoder models are more interpretable, efficient and tunable than LLMs, which are hard to fine-tune and use for information extraction.

Supported tasks::

Named Entity Recognition (NER): Identifies and categorizes entities such as names, organizations, dates, and other specific items in the text.
Relation Extraction: Detects and classifies relationships between entities within the text.
Summarization: Extract the most important sentences that summarize the input text, capturing the essential information.
Sentiment Extraction: Identify parts of the text that signal a positive, negative, or neutral sentiment;
Key-Phrase Extraction: Identifies and extracts important phrases and keywords from the text.
Question-answering: Finding an answer in the text given a question;
Open Information Extraction: Extracts pieces of text given an open prompt from a user, for example, product description extraction;
Text classification: Classifying text by matching labels specified in the prompt;

We prepared high-level classes that simplify the usage and evaluation of GLiNER multi-task models for different task types.

Classification

The GLiNERClassifier is a pipeline for text classification tasks based on the GLiNER model. It evaluates input text against a set of predefined labels, supporting both single-label and multi-label classification. It also calculates F1 scores for evaluation on datasets.

Initialize the Classifier
Load a pretrained model and initialize the GLiNERClassifier.

from gliner import GLiNER
from gliner.multitask import GLiNERClassifier

model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
classifier = GLiNERClassifier(model=model)

Classify a Text
Classify a single text into a list of labels.

text = "SpaceX successfully launched a new rocket into orbit."
labels = ['science', 'technology', 'business', 'sports']
predictions = classifier(text, classes=labels, multi_label=False)
print(predictions)

Expected Output

[[{'label': 'technology', 'score': 0.3839840292930603}]]

Evaluate on a Dataset
Evaluate the model on a dataset from Hugging Face.

metrics = classifier.evaluate('dair-ai/emotion')
print(metrics)

Expected Output

{'micro': 0.4465, 'macro': 0.42431600571236294, 'weighted': 0.48842385572766434}

Question-Answering

The GLiNERQuestionAnswerer is a pipeline for question-answering tasks based on the GLiNER model. It extracts answers based on questions and input text. You can leverage GLiNERSquadEvaluator to evaluate a model on the SQuAD dataset.

Initialize the Question-Answerer
Load a pretrained model and initialize the GLiNERQuestionAnswerer.

from gliner import GLiNER
from gliner.multitask import GLiNERQuestionAnswerer

model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
answerer = GLiNERQuestionAnswerer(model=model)

Extract an Answer from Text
Extract an answer to the input question.

text = "SpaceX successfully launched a new rocket into orbit."
question = 'Which company launched a new rocker?'
predictions = answerer(text, questions=question)
print(predictions)

Expected Output

[[{'answer': 'SpaceX', 'score': 0.998126208782196}]]

Evaluate on a Dataset
Evaluate the model on a dataset from Hugging Face.

from gliner.multitask import GLiNERSquadEvaluator
model_id = 'knowledgator/gliner-multitask-v1.0'
evaluator = GLiNERSquadEvaluator(model_id=model_id)
metrics = evaluator.evaluate(threshold=0.25)
print(metrics)

Expected Output

{'exact': 29.411269266402762, 'f1': 29.80174667785213, 'total': 11873, 'HasAns_exact': 0.6916329284750338, 'HasAns_f1': 1.473707541521307, 'HasAns_total': 5928, 'NoAns_exact': 58.048780487804876, 'NoAns_f1': 58.048780487804876, 'NoAns_total': 5945, 'best_exact': 50.08001347595385, 'best_exact_thresh': 0.0, 'best_f1': 50.08001347595385, 'best_f1_thresh': 0.0}

Relation Extraction

The GLiNERRelationExtractor is a pipeline for extracting relationships between entities in a text using the GLiNER model. The pipeline combines both zero-shot named entity recognition and relation extraction. It identifies entity pairs and their relations based on a specified by user set of relation types.

Initialize the Relation Extractor
Load a pretrained model and initialize the GLiNERRelationExtractor.

from gliner import GLiNER
from gliner.multitask import GLiNERRelationExtractor

model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
relation_extractor = GLiNERRelationExtractor(model=model)

Extract Relations from Text
Identify relationships between entities in a given text.

text = "Elon Musk founded SpaceX in 2002 to reduce space transportation costs."
relations = ['founded', 'owns', 'works for']
entities = ['person', 'company', 'year']
predictions = relation_extractor(text, entities=entities, relations=relations)
print(predictions)

Expected Output

[[{'source': 'Elon Musk', 'relation': 'founded', 'target': 'SpaceX', 'score': 0.9583475589752197}]]

For more nuanced tuning of the relation extraction pipeline, we recommend using the utca framework.

Open Information Extraction

The GLiNEROpenExtractor is a pipeline designed to extract information from a text given a user query. By default in terms of GLiNER labels match tag is used, however, we recommend combining prompting and selecting appropriate tags for your tasks.

Initialize the Information Extractor
Load a pretrained model and initialize the GLiNEROpenExtractor.

from gliner import GLiNER
from gliner.multitask import GLiNEROpenExtractor

model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
extractor = GLiNEROpenExtractor(model=model, prompt="Extract all companies related to space technologies")

Extract Information from Text
Identify relevant information from a given text.

text = "Elon Musk founded SpaceX in 2002 to reduce space transportation costs. Also Elon is founder of Tesla, NeuroLink and many other companies."
labels = ['company']
predictions = extractor(text, labels=labels)
print(predictions)

Expected Output

[[{'start': 72, 'end': 78, 'text': 'SpaceX', 'label': 'company', 'score': 0.9622299075126648}, {'start': 149, 'end': 154, 'text': 'Tesla', 'label': 'company', 'score': 0.9357912540435791}, {'start': 156, 'end': 165, 'text': 'NeuroLink', 'label': 'company', 'score': 0.9122058749198914}]]

Summarization

The GLiNERSummarizer pipeline leverages the GLiNER model for performing summarization tasks as extraction process.

Initialize the Summarizer
Load a pretrained model and initialize the GLiNERSummarizer.

from gliner import GLiNER
from gliner.multitask import GLiNERSummarizer

model_id = 'knowledgator/gliner-multitask-v1.0'
model = GLiNER.from_pretrained(model_id)
summarizer = GLiNERSummarizer(model=model)

Summarize the Text
Extract the most important information from a given text and construct summary.

text = "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014."
summary = summarizer(text, threshold=0.1)
print(summary)

Expected Output

['Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014.']

Relations extraction pipeline with utca

Install utca

Important

First of all, we need import necessary components of the library and initialize predictor - GLiNER model and construct pipeline that combines NER and relations extraction:

pip install -U utca

Initialize the Pipeline

from utca.core import RenameAttribute
from utca.implementation.predictors import (
    GLiNERPredictor,
    GLiNERPredictorConfig
)
from utca.implementation.tasks import (
    GLiNER,
    GLiNERPreprocessor,
    GLiNERRelationExtraction,
    GLiNERRelationExtractionPreprocessor,
)

predictor = GLiNERPredictor( # Predictor manages the model that will be used by tasks
    GLiNERPredictorConfig(
        model_name = "knowledgator/gliner-multitask-v1.0", # Model to use
        device = "cuda:0", # Device to use
    )
)

pipe = (
    GLiNER( # GLiNER task produces classified entities in the "output" key.
        predictor=predictor,
        preprocess=GLiNERPreprocessor(threshold=0.7) # Entities threshold
    ) 
    | RenameAttribute("output", "entities") # Rename output entities from GLiNER task to use them as inputs in GLiNERRelationExtraction
    | GLiNERRelationExtraction( # GLiNERRelationExtraction is used for relation extraction.
        predictor=predictor,
        preprocess=(
            GLiNERPreprocessor(threshold=0.5) # Relations threshold
            | GLiNERRelationExtractionPreprocessor()
        )
    )
)

Run the pipeline

To run pipeline we need to specify entity types and relations with their parameters:

text = "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975 to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014."

r = pipe.run({
    "text": text, # Text to process
    "labels": ["organisation", "founder", "position", "date"],
    "relations": [{ # Relation parameters
        "relation": "founder", # Relation label. Required parameter.
        "pairs_filter": [("organisation", "founder")], # Optional parameter. It specifies possible members of relations by their entity labels.
        "distance_threshold": 100, # Optional parameter. It specifies the max distance between spans in the text (i.e., the end of the span that is closer to the start of the text and the start of the next one).
    }, {
        "relation": "inception date",
        "pairs_filter": [("organisation", "date")],
    }, {
        "relation": "held position",
        "pairs_filter": [("founder", "position")],
    }]
})

print(r["output"])

Expected Output

[{'source': {'start': 0, 'end': 9, 'span': 'Microsoft', 'score': 0.997672975063324, 'entity': 'organisation'}, 'relation': 'founder', 'target': {'start': 25, 'end': 35, 'span': 'Bill Gates', 'score': 0.9939975738525391, 'entity': 'founder'}, 'score': 0.9683231711387634}, {'source': {'start': 0, 'end': 9, 'span': 'Microsoft', 'score': 0.997672975063324, 'entity': 'organisation'}, 'relation': 'founder', 'target': {'start': 40, 'end': 50, 'span': 'Paul Allen', 'score': 0.9759964942932129, 'entity': 'founder'}, 'score': 0.8631041049957275}, {'source': {'start': 149, 'end': 158, 'span': 'Microsoft', 'score': 0.9986124038696289, 'entity': 'organisation'}, 'relation': 'founder', 'target': {'start': 40, 'end': 50, 'span': 'Paul Allen', 'score': 0.9759964942932129, 'entity': 'founder'}, 'score': 0.8631041049957275}, {'source': {'start': 0, 'end': 9, 'span': 'Microsoft', 'score': 0.997672975063324, 'entity': 'organisation'}, 'relation': 'inception date', 'target': {'start': 54, 'end': 67, 'span': 'April 4, 1975', 'score': 0.9899335503578186, 'entity': 'date'}, 'score': 0.9971309304237366}, {'source': {'start': 149, 'end': 158, 'span': 'Microsoft', 'score': 0.9986124038696289, 'entity': 'organisation'}, 'relation': 'inception date', 'target': {'start': 54, 'end': 67, 'span': 'April 4, 1975', 'score': 0.9899335503578186, 'entity': 'date'}, 'score': 0.9971309304237366}, {'source': {'start': 25, 'end': 35, 'span': 'Bill Gates', 'score': 0.9939975738525391, 'entity': 'founder'}, 'relation': 'held position', 'target': {'start': 188, 'end': 196, 'span': 'chairman', 'score': 0.9755746126174927, 'entity': 'position'}, 'score': 0.9663875102996826}, {'source': {'start': 25, 'end': 35, 'span': 'Bill Gates', 'score': 0.9939975738525391, 'entity': 'founder'}, 'relation': 'held position', 'target': {'start': 198, 'end': 221, 'span': 'chief executive officer', 'score': 0.9835125803947449, 'entity': 'position'}, 'score': 0.9466949105262756}, {'source': {'start': 25, 'end': 35, 'span': 'Bill Gates', 'score': 0.9939975738525391, 'entity': 'founder'}, 'relation': 'held position', 'target': {'start': 223, 'end': 232, 'span': 'president', 'score': 0.9777029156684875, 'entity': 'position'}, 'score': 0.9731078147888184}, {'source': {'start': 25, 'end': 35, 'span': 'Bill Gates', 'score': 0.9939975738525391, 'entity': 'founder'}, 'relation': 'held position', 'target': {'start': 237, 'end': 261, 'span': 'chief software architect', 'score': 0.9814156293869019, 'entity': 'position'}, 'score': 0.9499841332435608}]

🚀 Basic Use Case​

Local Run​

🏃‍♀️Using FlashDeBERTa​

🛠️Pipelines​

Classification​

Question-Answering​

Relation Extraction​

Open Information Extraction​

Summarization​

Relations extraction pipeline with utca​

🚀 Basic Use Case

Local Run

🏃‍♀️Using FlashDeBERTa

🛠️Pipelines

Classification

Question-Answering

Relation Extraction

Open Information Extraction

Summarization

Relations extraction pipeline with utca