knowledgator/gliclass-x-base

⭐ 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.

The backbone model is mdeberta-v3-base. It supports multilingual understanding, making it well-suited for tasks involving texts in different languages.

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:

English

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

model = GLiClassModel.from_pretrained("knowledgator/gliclass-x-base")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-x-base", 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"])

Spanish

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

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

text = "¡Un día veré el mundo!"
labels = ["viajes", "sueños", "deportes", "ciencia", "política"]
results = pipeline(text, labels, threshold=0.5)[0]
for result in results:
    print(result["label"], "=>", result["score"])

Italitan

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

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

text = "Un giorno vedrò il mondo!"
labels = ["viaggi", "sogni", "sport", "scienza", "politica"]
results = pipeline(text, labels, threshold=0.5)[0]
for result in results:
    print(result["label"], "=>", result["score"])

French

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

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

text = "Un jour, je verrai le monde!"
labels = ["voyage", "rêves", "sport", "science", "politique"]
results = pipeline(text, labels, threshold=0.5)[0]
for result in results:
    print(result["label"], "=>", result["score"])

German

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

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

text = "Eines Tages werde ich die Welt sehen!"
labels = ["Reisen", "Träume", "Sport", "Wissenschaft", "Politik"]
results = pipeline(text, labels, threshold=0.5)[0]
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.

Multilingual benchmarks

Dataset	gliclass-x-base	gliclass-base-v3.0	gliclass-large-v3.0
FredZhang7/toxi-text-3M	0.5972	0.5072	0.6118
SetFit/xglue_nc	0.5014	0.5348	0.5378
Davlan/sib200_14classes	0.4663	0.2867	0.3173
uhhlt/GermEval2017	0.3999	0.4010	0.4299
dolfsai/toxic_es	0.1250	0.1399	0.1412
Average	0.41796	0.37392	0.4076

General benchmarks

Dataset	gliclass-x-base	gliclass-base-v3.0	gliclass-large-v3.0
SetFit/CR	0.8630	0.9127	0.9398
SetFit/sst2	0.8554	0.8959	0.9192
SetFit/sst5	0.3287	0.3376	0.4606
AmazonScience/massive	0.2611	0.5040	0.5649
stanfordnlp/imdb	0.8840	0.9251	0.9366
SetFit/20_newsgroups	0.4116	0.4759	0.5958
SetFit/enron_spam	0.5929	0.6760	0.7584
PolyAI/banking77	0.3098	0.4698	0.5574
takala/financial_phrasebank	0.7851	0.8971	0.9000
ag_news	0.6815	0.7279	0.7181
dair-ai/emotion	0.3667	0.4447	0.4506
MoritzLaurer/cap_sotu	0.3935	0.4614	0.4589
cornell/rotten_tomatoes	0.7252	0.7943	0.8411
snips	0.6307	0.9474	0.9692
Average	0.5778	0.6764	0.7193

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}, 
}