knowledgator/gliclass-modern-base-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-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("knowledgator/gliclass-modern-base-v2.0-init")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-modern-base-v2.0-init", add_prefix_space=True)
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"])

If you want to use it for NLI type of tasks, we recommend representing your premise as a text and hypothesis as a label, you can put several hypotheses, but the model works best with a single input hypothesis.

# Initialize model and multi-label pipeline
text = "The cat slept on the windowsill all afternoon"
labels = ["The cat was awake and playing outside."]
results = pipeline(text, labels, threshold=0.0)[0]
print(results)

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. | Model | IMDB | AGNEWS | Emotions | |-----------------------------|------|---------|----------| | [gliclass-modern-large-v2.0-init (399 M)](https://huggingface.co/knowledgator/gliclass-modern-large-v2.0-init) | 0.9137 | 0.7357 | 0.4140 | | [gliclass-modern-base-v2.0-init (151 M)](https://huggingface.co/knowledgator/gliclass-modern-base-v2.0-init) | 0.8264 | 0.6637 | 0.2985 | | [gliclass-large-v1.0 (438 M)](https://huggingface.co/knowledgator/gliclass-large-v1.0) | 0.9404 | 0.7516 | 0.4874 | | [gliclass-base-v1.0 (186 M)](https://huggingface.co/knowledgator/gliclass-base-v1.0) | 0.8650 | 0.6837 | 0.4749 | | [gliclass-small-v1.0 (144 M)](https://huggingface.co/knowledgator/gliclass-small-v1.0) | 0.8650 | 0.6805 | 0.4664 | | [Bart-large-mnli (407 M)](https://huggingface.co/facebook/bart-large-mnli) | 0.89 | 0.6887 | 0.3765 | | [Deberta-base-v3 (184 M)](https://huggingface.co/cross-encoder/nli-deberta-v3-base) | 0.85 | 0.6455 | 0.5095 | | [Comprehendo (184M)](https://huggingface.co/knowledgator/comprehendit-base) | 0.90 | 0.7982 | 0.5660 | | SetFit BAAI/bge-small-en-v1.5 (33.4M) | 0.86 | 0.5636 | 0.5754 |

Below you can find a comparison with other GLiClass models:

Here you can see how the performance of the model grows providing more examples: | Model | Num Examples | sst5 | ag_news | emotion | AVERAGE: | |------------------------------------|------------------|--------|---------|--------------|----------| | gliclass-modern-large-v2.0-init | 0 | 0.1123 | 0.6933 | 0.3746 | 0.3934 | | gliclass-modern-large-v2.0-init | 8 | 0.5098 | 0.8339 | 0.5010 | 0.6149 | | gliclass-modern-large-v2.0-init | Weak Supervision | 0.0951 | 0.6478 | 0.4520 | 0.3983 | | gliclass-modern-base-v2.0-init | 0 | 0.1972 | 0.6050 | 0.2474 | 0.3499 | | gliclass-modern-base-v2.0-init | 8 | 0.3604 | 0.7481 | 0.4420 | 0.5168 | | gliclass-modern-base-v2.0-init | Weak Supervision | 0.1599 | 0.5713 | 0.3216 | 0.3509 | | gliclass-large-v1.0-init | 0 | 0.1639 | 0.7069 | 0.3840 | 0.4183 | | gliclass-large-v1.0-init | 8 | 0.4226 | 0.8415 | 0.4886 | 0.5842 | | gliclass-large-v1.0-init | Weak Supervision | 0.1689 | 0.7051 | 0.4586 | 0.4442 | | gliclass-base-v1.0-init | 0 | 0.2048 | 0.5614 | 0.2865 | 0.3509 | | gliclass-base-v1.0-init | 8 | 0.2007 | 0.8359 | 0.4856 | 0.5074 | | gliclass-base-v1.0-init | Weak Supervision | 0.0681 | 0.6627 | 0.3066 | 0.3458 |

Citation

@misc{stepanov2025gliclassgeneralistlightweightmodel,
      title={GLiClass: Generalist Lightweight Model for Sequence Classification Tasks}, 
      author={Ihor Stepanov and Mykhailo Shtopko and Dmytro Vodianytskyi and Oleksandr Lukashov and Alexander Yavorskyi and Mykyta Yaroshenko},
      year={2025},
      eprint={2508.07662},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2508.07662}, 
}