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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Install GLiClass easily using pip:
pip install gliclassClone 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__)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}")GLiClass now supports hierarchical label structures using dot notation:
hierarchical_labels = {
"sentiment": ["positive", "negative", "neutral"],
"topic": ["product", "service", "shipping"]
}
text = "The product quality is amazing but delivery was slow"
results = pipeline(text, hierarchical_labels, threshold=0.5)[0]
for result in results:
print(f"{result['label']} => {result['score']:.3f}")
# Output:
# sentiment.positive => 0.892
# topic.product => 0.921
# topic.shipping => 0.763Get hierarchical output matching your input structure:
results = pipeline(text, hierarchical_labels, return_hierarchical=True)[0]
print(results)
# Output:
# {
# "sentiment": {"positive": 0.892, "negative": 0.051, "neutral": 0.124},
# "topic": {"product": 0.921, "service": 0.153, "shipping": 0.763}
# }Improve classification accuracy with in-context examples using the <<EXAMPLE>> token:
examples = [
{
"text": "Love this item, great quality!",
"labels": ["positive", "product"]
},
{
"text": "Customer support was unhelpful",
"labels": ["negative", "service"]
}
]
text = "Fast delivery and the item works perfectly!"
labels = ["positive", "negative", "product", "service", "shipping"]
results = pipeline(text, labels, examples=examples, threshold=0.5)[0]
for result in results:
print(f"{result['label']} => {result['score']:.3f}")Add custom prompts to guide the classification task:
text = "The battery life on this phone is incredible"
labels = ["positive", "negative", "neutral"]
results = pipeline(
text,
labels,
prompt="Classify the sentiment of this product review:",
threshold=0.5
)[0]Use per-text prompts for batch processing:
texts = ["Review about electronics", "Review about clothing"]
prompts = [
"Analyze this electronics review:",
"Analyze this clothing review:"
]
results = pipeline(texts, labels, prompt=prompts)Process long documents with automatic text chunking:
from gliclass import ZeroShotClassificationWithChunkingPipeline
chunking_pipeline = ZeroShotClassificationWithChunkingPipeline(
model,
tokenizer,
text_chunk_size=8192,
text_chunk_overlap=256,
labels_chunk_size=8
)
long_document = "..." # Very long text
labels = ["category1", "category2", "category3"]
results = chunking_pipeline(long_document, labels, threshold=0.5)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}")Deploy GLiClass with Ray Serve for production workloads with dynamic batching and memory-aware processing.
pip install gliclass[serve]# Default model
python -m gliclass.serve
# Specify model and port
python -m gliclass.serve --model knowledgator/gliclass-edge-v3.0 --port 8000
# With config file
python -m gliclass.serve --config serve_configs/serve_config.yamlfrom gliclass.serve import GLiClassClient
client = GLiClassClient(url="http://localhost:8000/gliclass")
result = client.classify(
text="This is a great product!",
labels=["positive", "negative", "neutral"],
threshold=0.3,
)
print(result) # [{"label": "positive", "score": 0.95}, ...]The HTTP endpoint processes one text per request.
curl -X POST http://localhost:8000/gliclass \
-H "Content-Type: application/json" \
-d '{
"texts": "This is a great product!",
"labels": ["positive", "negative", "neutral"],
"threshold": 0.3
}'
# Response: [{"label": "positive", "score": 0.95}, ...]Note: For batch processing multiple texts, use the ZeroShotClassificationPipeline directly instead of the serving API.
See serve_configs/serve_config.yaml for full configuration options.
- 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.
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.
GLiClass supports multiple architecture types:
- uni-encoder: Single encoder for both text and labels (default, most efficient)
- bi-encoder: Separate encoders for text and labels
- bi-encoder-fused: Bi-encoder with label embeddings fused into text encoding
- encoder-decoder: Encoder-decoder architecture for sequence-to-sequence tasks
from gliclass import GLiClassBiEncoder
# Load a bi-encoder model
model = GLiClassBiEncoder.from_pretrained("knowledgator/gliclass-biencoder-v1.0")Configure how token embeddings are pooled:
- first: First token (CLS token)
- avg: Average pooling
- max: Max pooling
- last: Last token
- sum: Sum pooling
- rms: Root mean square pooling
- abs_max: Max of absolute values
- abs_avg: Average of absolute values
from gliclass import GLiClassModelConfig
config = GLiClassModelConfig(
pooling_strategy='avg',
class_token_pooling='average' # or 'first'
)Choose different scoring mechanisms for classification:
- simple: Dot product (fastest)
- weighted-dot: Weighted dot product with learned projections
- mlp: Multi-layer perceptron scorer
- hopfield: Hopfield network-based scorer
config = GLiClassModelConfig(
scorer_type='mlp'
)GLiClass supports incremental, multi-session text classification over a decoder-KV model. Instead of re-encoding the full document on every update, it maintains a persistent KV cache per session and runs the scorer only when a pluggable strategy decides classification should fire.
pip install gliclass[streaming]from gliclass.streaming import StreamingPipeline, SessionInput, EveryNTokensStrategy
pipeline = StreamingPipeline(model, tokenizer, device="cuda", max_cache_len=1024)
strategy = EveryNTokensStrategy(n=50)
for chunk in text_chunks:
outputs = pipeline([SessionInput(
session_id="doc_001",
text=chunk,
labels=["science", "politics", "finance"],
strategy=strategy,
classification_type="multi-label",
)])
if outputs[0].triggered:
print(outputs[0].predictions)Built-in strategies: EveryChunkStrategy, EveryNTokensStrategy, OnDelimiterStrategy, SlidingWindowStrategy, ComposedStrategy, NeverStrategy.
For full documentation on session management, KV cache internals, batching, CPU offloading, and custom strategies, see docs/streaming.md.
GLiClass supports optional flash attention backends for faster inference.
pip install flashdeberta # DeBERTa v2
pip install turbot5 # T5 / mT5Enable via environment variable:
export USE_FLASHDEBERTA=1If flashdeberta is installed, DeBERTa v2 models will use FlashDebertaV2Model.
Otherwise, GLiClass falls back to DebertaV2Model.
Enable via environment variable:
export TURBOT5_ATTN_TYPE=triton-basicIf turbot5 is installed, T5 / mT5 models will use FlashT5EncoderModel.
Otherwise, GLiClass falls back to T5EncoderModel.
Notes:
- Flash backends are optional
- Enabled automatically when available
- No code changes required
Want it even tighter (single block), or is this the sweet spot?
If you find GLiClass useful in your research or project, please cite our papers:
@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},
}