knowledgator / gliclass-modern-large-v2.0-init

⭐ 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-large , 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("knowledgator/gliclass-modern-large-v2.0-init")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-modern-large-v2.0-init")
pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')
text = "One day I will see the world!"
labels = ["travel", "dreams", "sport", "science", "politics"]
results = pipeline(text, labels, threshold=0.5)[0] #because we have one text
for result in results:
 print(result["label"], "=>", result["score"])

Benchmarks:

Below, you can see the F1 score on several text classification datasets. All tested models were not fine-tuned on those datasets and were tested in a zero-shot setting.

Below you can find a comparison with other GLiClass models:

Here you can see how the performance of the model grows providing more examples: