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👑 GLiNER: Generalist and Lightweight Model for Named Entity Recognition

GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.

Models Status

📢 Updates

  • 🔍 Join the GLiNER discord server: https://discord.gg/Y2yVxpSQnG
  • Synthetic data generation example is available (examples/synthetic_data_generation.ipynb).
  • 🆕 gliner_multi_pii-v1 is available. This version has been optimized to recognize and classify Personally Identifiable Information (PII) within text. This version has been finetuned on six languages (English, French, German, Spanish, Italian, Portugese).
  • 🚀 gliner_multi-v2.1, gliner_small-v2.1, gliner_medium-v2.1, and gliner_large-v2.1 are available under the Apache 2.0 license.
  • 🆕 gliner-spacy is available. Install it with pip install gliner-spacy. See Example of usage below.
  • 🧬 gliner_large_bio-v0.1 is a gliner model specialized for biomedical text. It is available under the Apache 2.0 license.
  • 📚 Training dataset preprocessing scripts are now available in the data/ directory, covering both Pile-NER and NuNER datasets.

Finetuning GLiNER

🌟 Available Models on Hugging Face

🇬🇧 For English

  • GLiNER Base: urchade/gliner_base (CC BY NC 4.0)

  • GLiNER Small: urchade/gliner_small (CC BY NC 4.0)

  • GLiNER Small v2: urchade/gliner_small-v2 (Apache 2.0)

  • GLiNER Small v2.1: urchade/gliner_small-v2.1 (Apache 2.0)

  • GLiNER Medium: urchade/gliner_medium (CC BY NC 4.0)

  • GLiNER Medium v2: urchade/gliner_medium-v2 (Apache 2.0)

  • GLiNER Medium v2.1: urchade/gliner_medium-v2.1 (Apache 2.0)

  • GLiNER Large: urchade/gliner_large (CC BY NC 4.0)

  • GLiNER Large v2: urchade/gliner_large-v2 (Apache 2.0)

  • GLiNER Large v2.1: urchade/gliner_large-v2.1 (Apache 2.0)

  • GLiNER NuNerZero span: numind/NuNER_Zero-span (MIT) - +4.5% more powerful GLiNER Large v2.1

  • GLiNER News: EmergentMethods/gliner_medium_news-v2.1 (Apache 2.0) 9.5% improvement over GLiNER Large v2.1 on 18 benchmark datasets

🇬🇧 English word-level Entity Recognition

Word-level models work better for finding multi-word entities, highlighting sentences or paragraphs. They require additional output postprocessing that can be found in the corresponding model card.

  • GLiNER NuNerZero: numind/NuNER_Zero (MIT) - +3% more powerful GLiNER Large v2.1, better suitable to detect multi-word entities
  • GLiNER NuNerZero 4k context: numind/NuNER_Zero-4k (MIT) - 4k-long-context NuNerZero

🌍 For Other Languages

  • Korean: 🇰🇷 taeminlee/gliner_ko
  • Italian: 🇮🇹 DeepMount00/universal_ner_ita
  • Multilingual: 🌐 urchade/gliner_multi (CC BY NC 4.0) and urchade/gliner_multi-v2.1 (Apache 2.0)

🔬 Domain Specific Models

  • Personally Identifiable Information: 🔍 urchade/gliner_multi_pii-v1 (Apache 2.0)
    • This model is capable of recognizing various types of personally identifiable information (PII), including but not limited to these entity types: person, organization, phone number, address, passport number, email, credit card number, social security number, health insurance id number, date of birth, mobile phone number, bank account number, medication, cpf, driver's license number, tax identification number, medical condition, identity card number, national id number, ip address, email address, iban, credit card expiration date, username, health insurance number, registration number, student id number, insurance number, flight number, landline phone number, blood type, cvv, reservation number, digital signature, social media handle, license plate number, cnpj, postal code, passport_number, serial number, vehicle registration number, credit card brand, fax number, visa number, insurance company, identity document number, transaction number, national health insurance number, cvc, birth certificate number, train ticket number, passport expiration date, and social_security_number.
  • Biomedical: 🧬 urchade/gliner_large_bio-v0.1 (Apache 2.0)
  • Birds attribute extraction: 🐦 wjbmattingly/gliner-large-v2.1-bird (Apache 2.0)

📚 Multi-task Models

  • GLiNER multi-task large v0.5 knowledgator/gliner-multitask-large-v0.5 (Apache 2.0) - +4.5% on NER benchmarks over GLiNER Large v2.1, supports prompting, relation extraction, summarization and question-answering tasks.
  • GLiNER multi-task v1.0 knowledgator/gliner-multitask-v1.0 (Apache 2.0) - +5.0% on NER benchmarks over GLiNER Large v2.1, supports prompting, relation extraction, summarization, classification and question-answering tasks.
  • GLiNER Llama multi-task v1.0 knowledgator/gliner-llama-multitask-1B-v1.0 (Apache 2.0) - +3.5% on NER benchmarks over GLiNER Large v2.1, supports prompting, relation extraction, summarization, classification and question-answering tasks.

🛠 Installation & Usage

To provide instructions on how to install the GLiNER model from source, you can add steps for cloning the repository and installing it manually. Here’s how you can incorporate those instructions:

🛠 Installation & Usage

To begin using the GLiNER model, you can install the GLiNER Python library through pip, conda, or directly from the source.

Install via Pip

!pip install gliner

Install via Conda

conda install -c conda-forge gliner

Install from Source

To install the GLiNER library from source, follow these steps:

  1. Clone the Repository:

    First, clone the GLiNER repository from GitHub:

    git clone https://github.com/urchade/GLiNER

  2. Navigate to the Project Directory:

    Change to the directory containing the cloned repository:

    cd GLiNER

  3. Install Dependencies:

    It's a good practice to create and activate a virtual environment before installing dependencies:

    python -m venv venv
source venv/bin/activate   # On Windows use: venv\Scripts\activate

    Install the required dependencies listed in the requirements.txt file:

    pip install -r requirements.txt

  4. Install the GLiNER Package:

    Finally, install the GLiNER package using the setup script:

    pip install .

  5. Verify Installation:

    You can verify the installation by importing the library in a Python script:

    import gliner
print(gliner.__version__)

🚀 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("urchade/gliner_mediumv2.1")

# 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
Ballon d'Or => award
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
European Championship => competitions

🔌 Usage with spaCy

GLiNER can be seamlessly integrated with spaCy. To begin, install the gliner-spacy library via pip:

pip install gliner-spacy

Following installation, you can add GLiNER to a spaCy NLP pipeline. Here's how to integrate it with a blank English pipeline; however, it's compatible with any spaCy model.

import spacy
from gliner_spacy.pipeline import GlinerSpacy

# Configuration for GLiNER integration
custom_spacy_config = {
    "gliner_model": "urchade/gliner_mediumv2.1",
    "chunk_size": 250,
    "labels": ["person", "organization", "email"],
    "style": "ent",
    "threshold": 0.3,
    "map_location": "cpu" # only available in v.0.0.7
}

# Initialize a blank English spaCy pipeline and add GLiNER
nlp = spacy.blank("en")
nlp.add_pipe("gliner_spacy", config=custom_spacy_config)

# Example text for entity detection
text = "This is a text about Bill Gates and Microsoft."

# Process the text with the pipeline
doc = nlp(text)

# Output detected entities
for ent in doc.ents:
    print(ent.text, ent.label_, ent._.score) # ent._.score only available in v. 0.0.7

Expected Output

Bill Gates => person
Microsoft => organization

Multitask Usage

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 signalize 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.

Quick Usage Examples

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

    from gliner import GLiNER, GLiNERClassifier

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

  2. 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)

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

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

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.

Quick Usage Examples

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

    from gliner import GLiNER, GLiNERQuestionAnswerer

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

  2. Extract an answer from a 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)

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

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

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.

Quick Usage Examples

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

    from gliner import GLiNER, GLiNERRelationExtractor

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

  2. 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)

  3. Evaluate on a Dataset
    Evaluate the model on a relation extraction dataset.

    from datasets import load_dataset

dataset = load_dataset('docred', split='test')
metrics = relation_extractor.evaluate(dataset=dataset)
print(metrics)

For more nuance tuning of relation extraction pipeline, we recommend to use utca framework.

Construct relations extraction pipeline with utca

First of all, we need import neccessary components of the library and initalize predictor - GLiNER model and construct pipeline that combines NER and realtions extraction:

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 that will be at 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()
        )
    )
)

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

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"])

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.

Quick Usage Examples

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

    from gliner import GLiNER, 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")

  2. 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 = relation_extractor(text, labels=labels)
print(predictions)

Summariztion

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

Quick Usage Examples

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

    from gliner import GLiNER, GLiNERSummarizer

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

  2. 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 = relation_extractor(text, threshold=0.1)
print(summary)

📊 NER Benchmark Results

ONNX convertion:

To convert previously trained GLiNER models to ONNX format, you can use the convert_to_onnx.py script. You need to provide the model_path and save_path arguments to specify the location of the model and where to save the ONNX file, respectively. Additionally, if you wish to quantize the model, set the quantize argument to True (it quantizes to IntU8 by default).

Example usage:

python convert_to_onnx.py --model_path /path/to/your/model --save_path /path/to/save/onnx --quantize True

To load the converted ONNX models, you can use the following code snippet:

from gliner import GLiNER

model = GLiNER.from_pretrained("path_to_your_model", load_onnx_model=True, load_tokenizer=True)

The load_onnx_model argument ensures that the GLiNER class recognizes that it should load the ONNX model instead of a PyTorch model. Setting the `load_tokenizer`` argument to True loads the tokenizer from your model directory, including any additional tokens that were added during training.

🛠 Areas of Improvements / research

  • Extend the model to relation extraction. Our preliminary work GraphER.
  • Allow longer context (eg. train with long context transformers such as Longformer, LED, etc.)
  • Use Bi-encoder (entity encoder and span encoder) allowing precompute entity embeddings
  • Filtering mechanism to reduce number of spans before final classification to save memory and computation when the number entity types is large
  • Improve understanding of more detailed prompts/instruction, eg. "Find the first name of the person in the text"
  • Better loss function: for instance use Focal Loss (see this paper) instead of BCE to handle class imbalance, as some entity types are more frequent than others
  • Improve multi-lingual capabilities: train on more languages, and use multi-lingual training data
  • Decoding: allow a span to have multiple labels, eg: "Cristiano Ronaldo" is both a "person" and "football player"
  • Dynamic thresholding (in model.predict_entities(text, labels, threshold=0.5)): allow the model to predict more entities, or less entities, depending on the context. Actually, the model tend to predict less entities where the entity type or the domain are not well represented in the training data.
  • Train with EMAs (Exponential Moving Averages) or merge multiple checkpoints to improve model robustness (see this paper)

👨‍💻 Model Authors

The model authors are:

📚 Citation

If you find GLiNER useful in your research, please consider citing our paper:

@misc{zaratiana2023gliner,
      title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer}, 
      author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
      year={2023},
      eprint={2311.08526},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Support and funding

This project has been supported and funded by FI Group and Laboratoire Informatique de Paris Nord.

Over the past 20 years, FI Group has become a specialist in public funding strategies for R&D&I² (Research and Development, Innovation and Investment). FI Group's consultants, all engineers or PhDs, support customers from R&D through to the production of their innovations.

FI Group

We also extend our heartfelt gratitude to the open-source community for their invaluable contributions, which have been instrumental in the success of this project.