gliclass instruct large v1.0 EU AI Act Compliance Profile

GLiClass-multitask: Efficient 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 V3

• 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-instruct-large-v1.0")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-instruct-large-v1.0")
pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0')

Task Examples

1. Topic Classification

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"])

With hierar

Your obligations as a gliclass instruct large v1.0 deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags