Gliclass Large V1.0

🚀 ⭐ GLiClass: Generalist and Lightweight Model for Sequence Classification

This is an efficient zero - shot classifier inspired by the GLiNER work. It offers comparable performance to a cross - encoder while being more compute - efficient, as classification is achieved in a single forward pass.

It can be utilized for topic classification, sentiment analysis, and as a reranker in RAG pipelines. The model was trained on synthetic data and is suitable for commercial applications.

🚀 Quick Start

How to use:

First, you need to install the GLiClass library:

pip install gliclass

Then, you need to initialize a model and a pipeline:

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

model = GLiClassModel.from_pretrained("knowledgator/gliclass-large-v1.0")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-large-v1.0")

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"])

✨ Features

• Efficient zero - shot classification.
• Can be used for multiple tasks such as topic classification, sentiment analysis, and as a reranker in RAG pipelines.
• Trained on synthetic data, suitable for commercial use.

📦 Installation

pip install gliclass

💻 Usage Examples

Basic Usage

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

model = GLiClassModel.from_pretrained("knowledgator/gliclass-large-v1.0")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-large-v1.0")

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"])

📚 Documentation

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.

Below you can find a comparison with other GLiClass models:

Here you can see how the performance of the model grows with more examples:

📄 License

The model is licensed under the Apache - 2.0 license.