⭐ GLiClass: Generalist and Lightweight Model for Sequence Classification

This is an efficient zero-shot classifier inspired by GLiNER work. It demonstrates the same performance as a cross-encoder while being more compute-efficient because classification is done at a single forward path.

It can be used for topic classification, sentiment analysis and as a reranker in RAG pipelines.

The model was trained on synthetic and licensed data that allow commercial use and can be used in commercial applications.

This version of the model uses a layer-wise selection of features that enables a better understanding of different levels of language. The backbone model is ModernBERT-base, which effectively processes long sequences.

How to use:

First of all, you need to install GLiClass library:

pip install gliclass
pip install -U transformers>=4.48.0

Than you need to initialize a model and a pipeline:

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

model = GLiClassModel.from_pretrained("alexandrlukashov/gliclass_msmarco_merged")
tokenizer = AutoTokenizer.from_pretrained("alexandrlukashov/gliclass_msmarco_merged", add_prefix_space=True)
pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')

text = "I want to live in New York."
labels =[
    'York is a cathedral city in North Yorkshire, England, with Roman origins',
    'San Francisco,[23] officially the City and County of San Francisco, is a commercial, financial, and cultural center within Northern California, United States.',
    'New York, often called New York City (NYC),[b] is the most populous city in the United States',
    "New York City is the third album by electronica group Brazilian Girls, released in 2008.",
    "New York City was an American R&B vocal group.",
    "New York City is an album by the Peter Malick Group featuring Norah Jones.",
    "New York City: The Album is the debut studio album by American rapper Troy Ave. ",
    '"New York City" is a song by British new wave band The Armoury Show',
]
results = pipeline(text, labels, threshold=0.5)[0] #because we have one text
for result in results:
 print(result["label"], "=>", result["score"])