knowledgator / gliclass-x-base

huggingface.co
Total runs: 1.9K
24-hour runs: 0
7-day runs: 680
30-day runs: 751
Model's Last Updated: August 12 2025
text-classification

Introduction of gliclass-x-base

Model Details of 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
Average 0.5737 0.6556 0.7001

Runs of knowledgator gliclass-x-base on huggingface.co

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