# GLiClass: Open-Source JEV

> Source: <https://github.com/knowledgator/gliclass>
> Published: 2026-09-16 04:47:07+00:00

**GLiClass** is an efficient, zero-shot sequence classification model inspired by the [GLiNER](https://github.com/urchade/GLiNER/tree/main) 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.

[📄 Blog](https://medium.com/@knowledgrator/pushing-zero-shot-classification-to-the-limit-696a2403032f)
  •  
[📢 Discord](https://discord.gg/dkyeAgs9DG)
  •  
[📺 Demo](https://huggingface.co/spaces/knowledgator/GLiClass_SandBox)
  •  
[🤗 Available models](https://huggingface.co/models?sort=trending&search=gliclass)
  •  

Install GLiClass easily using pip:

```
pip install gliclass
```

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:

``` python
import gliclass
print(gliclass.__version__)
python
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 => 0.763
```

Get 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:

``` python
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](https://huggingface.co/knowledgator/gliclass-base-v2.0-rac-init) 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.yaml
python
from 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

``` python
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

``` python
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]
python
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](https://github.com/Knowledgator/GLiClass/blob/main/docs/streaming.md).

GLiClass supports optional flash attention backends for faster inference.

```
pip install flashdeberta   # DeBERTa v2
pip install turbot5        # T5 / mT5
```

Enable via environment variable:

```
export USE_FLASHDEBERTA=1
```

If `flashdeberta` is installed, DeBERTa v2 models will use `FlashDebertaV2Model`.
Otherwise, GLiClass falls back to `DebertaV2Model`.

Enable via environment variable:

```
export TURBOT5_ATTN_TYPE=triton-basic
```

If `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}, 
}
```