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Cataloged from knowledgator/gliclass-instruct-base-v1.0
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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:
sentiment.positive, topic.product).See the GLiClass library README for full details on these features.
Represent your premise as the text and the hypothesis as a label. The model works best with a single hypothesis at a time.
Score query–passage relevance by treating passages as texts and the query as the label:
Concatenate context, question, and answer into the text field:
Include the domain and rules as part of the text:
F1 scores on zero-shot text classification (no fine-tuning on these datasets):
GLiClass-V1 Multitask:
GLiClass-V3:
pip install gliclassfrom gliclass import GLiClassModel, ZeroShotClassificationPipelinefrom transformers import AutoTokenizer
model = GLiClassModel.from_pretrained("knowledgator/gliclass-instruct-base-v1.0")tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-instruct-base-v1.0")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"])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 => 0.95text = "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"])examples = [ {"text": "Wake me up at 6:30.", "labels": ["set_alarm"]}, {"text": "Play some jazz.", "labels": ["play_music"]},]
results = pipeline(text, labels, examples=examples, 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 → contradictionquery = "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 = ( "Context: The Eiffel Tower was built from 1887 to 1889 and is 330 m tall. " "It was the tallest structure until the Chrysler Building in 1930.\n" "Question: When was the Eiffel Tower built and how tall is it?\n" "Answer: It was built 1887–1889, stands 330 m tall, and was the tallest " "structure until the Empire State Building in 1931.")labels = ["hallucinated", "correct"]
results = pipeline(text, labels, threshold=0.0)[0]for r in results: print(r["label"], "=>", r["score"])# "hallucinated" should score higher (Empire State Building & 1931 are wrong)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"])text = "I'm looking for a good Italian restaurant near downtown Chicago, budget ~$50/person."labels = [ "benign request", "prompt injection", "system prompt extraction", "jailbreak attempt", "harmful content request", "social engineering", "data exfiltration",]
results = pipeline(text, labels, threshold=0.5)[0]for r in results: print(r["label"], "=>", r["score"])@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}, }