⭐ GLiClass: Generalist and Lightweight Model for Sequence Classification

GLiClass is an efficient, zero-shot sequence classification model inspired by the GLiNER framework. It achieves comparable performance to traditional cross-encoder models while being significantly more computationally efficient, offering classification results approximately 10 times faster by performing classification in a single forward pass.

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🚀 Quick Start

Install GLiClass easily using pip:

pip install gliclass

Install from Source

Clone and install directly from GitHub:

git clone https://github.com/Knowledgator/GLiClass
cd GLiClass

python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

pip install -r requirements.txt
pip install .

Verify your installation:

import gliclass
print(gliclass.__version__)

🧑‍💻 Usage Example

from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

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

for result in results:
    print(f"{result['label']} => {result['score']:.3f}")

🌟 Retrieval-Augmented Classification (RAC)

With new models trained with retrieval-agumented classification, such as this model you can specify examples to improve classification accuracy:

example = {
    "text": "A new machine learning platform automates complex data workflows but faces integration issues.",
    "all_labels": ["AI", "automation", "data_analysis", "usability", "integration"],
    "true_labels": ["AI", "integration", "automation"]
}

text = "The new AI-powered tool streamlines data analysis but has limited integration capabilities."
labels = ["AI", "automation", "data_analysis", "usability", "integration"]

results = pipeline(text, labels, threshold=0.1, rac_examples=[example])[0]

for predict in results:
    print(f"{predict['label']} => {predict['score']:.3f}")

🎯 Key Use Cases

• Sentiment Analysis: Rapidly classify texts as positive, negative, or neutral.
• Document Classification: Efficiently organize and categorize large document collections.
• Search Results Re-ranking: Improve relevance and precision by reranking search outputs.
• News Categorization: Automatically tag and organize news articles into predefined categories.
• Fact Checking: Quickly validate and categorize statements based on factual accuracy.

🛠️ How to Train

Prepare your training data as follows:

[
  {"text": "Sample text.", "all_labels": ["sports", "science", "business"], "true_labels": ["sports"]},
  ...
]

Optionally, specify confidence scores explicitly:

[
  {"text": "Sample text.", "all_labels": ["sports", "science"], "true_labels": {"sports": 0.9}},
  ...
]

Please, refer to the train.py script to set up your training from scratch or fine-tune existing models.