knowledgator / gliclass-multilang-edge

GLiClass Multilang: Efficient multilingual zero-shot and few-shot multi-task model via sequence classification

GLiClass is an efficient zero-shot sequence classification model designed to achieve SoTA performance while being much faster than cross-encoders and LLMs, while preserving strong generalization capabilities.

The model supports text classification with any labels and can be used for the following tasks:

• Topic Classification
• Sentiment Analysis
• Intent Classification
• Reranking
• Hallucination Detection
• Rule-following Verification
• LLM-safety Classification
• Natural Language Inference

✨ What's New in GLiClass Multilang

• Multilingual Training — Natively trained on 20 languages: Swedish, Norwegian, Czech, Polish, Lithuanian, Estonian, Latvian, Spanish, Finnish, German, French, Romanian, Italian, Portuguese, Dutch, Ukrainian, Hindi, Chinese, Arabic, and Hebrew.
• Cross-lingual Classification — Labels and input texts can be in different languages; classify a German document with English labels, or mix languages freely across inputs and labels.
• CrossAttn Scorer — A new cross-attention scorer enables more efficient pooling independently for each label with unpadding and flash-attn.
• Hierarchical Labels — Organize labels into groups using dot notation or dictionaries (e.g., sentiment.positive , topic.product ).
• Few-Shot Examples — Provide in-context examples to boost accuracy on your specific task.
• Label Descriptions — Add natural-language descriptions to labels for more precise classification.
• Task Prompts — Prepend a custom prompt to guide the model's classification behavior.

See the GLiClass library README for full details on these features.

Installation

pip install gliclass

Quick Start

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

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

text = "NASA launched a new Mars rover to search for signs of ancient life."
labels = ["space", "politics", "sports", "technology", "health"]

results = pipeline(text, labels, threshold=0.5)[0]
for r in results:
    print(r["label"], "=>", r["score"])

Multilingual & Cross-lingual Capabilities

Natively trained on 20 languages. Labels and texts can be in different languages.

Same language (German):

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

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

text = "Die NASA hat einen neuen Mars-Rover gestartet, um nach Spuren alten Lebens zu suchen."
labels = ["Weltraum", "Politik", "Sport", "Technologie", "Gesundheit"]
results = pipeline(text, labels, threshold=0.5)[0]
for r in results:
    print(r["label"], "=>", r["score"])

Cross-lingual (French text, English labels):

text = "Le gouvernement français a annoncé de nouvelles mesures économiques."
labels = ["economy", "politics", "sports", "technology"]
results = pipeline(text, labels, threshold=0.5)[0]
for r in results:
    print(r["label"], "=>", r["score"])

Cross-lingual (Arabic text, English labels):

text = "أطلقت ناسا مركبة جديدة للمريخ للبحث عن آثار الحياة القديمة."
labels = ["space", "politics", "sports", "technology"]
results = pipeline(text, labels, threshold=0.5)[0]
for r in results:
    print(r["label"], "=>", r["score"])

Cross-lingual (English text, Spanish labels):

text = "NASA launched a new Mars rover to search for signs of ancient life."
labels = ["espacio", "política", "deportes", "tecnología", "salud"]
results = pipeline(text, labels, threshold=0.5)[0]
for r in results:
    print(r["label"], "=>", r["score"])

text = "NASA launched a new Mars rover to search for signs of ancient life."
labels = ["space", "politics", "sports", "technology", "health"]

results = pipeline(text, labels, threshold=0.5)[0]
for r in results:
    print(r["label"], "=>", r["score"])

hierarchical_labels = {
    "science": ["space", "biology", "physics"],
    "society": ["politics", "economics", "culture"]
}

results = pipeline(text, hierarchical_labels, threshold=0.5)[0]
for r in results:
    print(r["label"], "=>", r["score"])
# e.g. science.space => 0.95

text = "The food was excellent but the service was painfully slow."
labels = ["positive", "negative", "neutral"]

results = pipeline(text, labels, threshold=0.5)[0]
for r in results:
    print(r["label"], "=>", r["score"])

results = pipeline(
    text, labels,
    prompt="Classify the sentiment of this restaurant review:",
    threshold=0.5
)[0]

text = "Can you set an alarm for 7am tomorrow?"
labels = ["set_alarm", "play_music", "get_weather", "send_message", "set_reminder"]

results = pipeline(text, labels, threshold=0.5)[0]
for r in results:
    print(r["label"], "=>", r["score"])

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)
# Low score → contradiction

query = "How to train a neural network?"
passages = [
    "Backpropagation is the key algorithm for training deep neural networks.",
    "The stock market rallied on strong earnings reports.",
    "Gradient descent optimizes model weights during training.",
]

for passage in passages:
    score = pipeline(passage, [query], threshold=0.0)[0][0]["score"]
    print(f"{score:.3f}  {passage[:60]}")

text = (
    "Domain: e-commerce product reviews\n"
    "Rule: No promotion of illegal activity.\n"
    "Text: The software is okay, but search for 'productname_patch_v2.zip' "
    "to unlock all features for free."
)
labels = ["follows_guidelines", "violates_guidelines"]

results = pipeline(text, labels, threshold=0.0)[0]
for r in results:
    print(r["label"], "=>", r["score"])

Benchmarks

Model Overview

Summary across all evaluated multilingual-capable models (zero-shot, no fine-tuning). Speed averaged over all label counts and text lengths at batch_size=8 on NVIDIA RTX PRO 6000 Blackwell.

Multilingual avg F1 is the mean of 6 dataset-level scores (GermEval2017, MASSIVE, PolygloToxicityPrompts, SIB-200, TextDetox, TweetSentiment). Models without multilingual results (—) were only evaluated on English datasets.

F1 scores on zero-shot text classification (no fine-tuning on these datasets):

Table A: GLiClass Multilang (macro F1)

Table B: Baselines (macro F1)

Table C: GLiClass-V1 Multitask (macro F1)

Multilingual Benchmarks

Macro F1 averaged per dataset across all evaluated languages:

Per-language macro F1 (16-language fair comparison on massive + sib200):

Throughput

Throughput (samples/sec), batch_size=8, GPU: NVIDIA RTX PRO 6000 Blackwell. Averaged over text lengths (64 / 256 / 512 tokens).

NLI models (bge-m3, mDeBERTa) run one forward pass per label — throughput drops linearly with label count. GLiClass and GLiNER2 encode all labels in a single pass, so throughput stays nearly flat.

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}, 
}