---
license: apache-2.0
datasets:
- knowledgator/events_classification_biotech
- knowledgator/Scientific-text-classification
---
# ⭐ GLiClass: Generalist and Lightweight Model for Sequence Classification

This is an efficient zero-shot classifier inspired by [GLiNER](https://github.com/urchade/GLiNER/tree/main) work. It demonstrates the same performance as a cross-encoder while being more compute-efficient because classification is done at a single forward path.

It can be used for `topic classification`, `sentiment analysis` and as a reranker in `RAG` pipelines.

The model was trained on synthetic and licensed data that allow commercial use and can be used in commercial applications.

This version of the model uses a layer-wise selection of features that enables a better understanding of different levels of language. The backbone model is [ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base), which effectively processes long sequences.

The model was fine-tuned using a new RL-based approach to classification, with F1 and recall rewards. 

### How to use:
First of all, you need to install GLiClass library:
```bash
pip install gliclass
pip install -U transformers>=4.48.0
```

Than you need to initialize a model and a pipeline:
```python
from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer

model = GLiClassModel.from_pretrained("knowledgator/gliclass-modern-base-v2.0")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-modern-base-v2.0", add_prefix_space=True)
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"])
```

If you want to use it for NLI type of tasks, we recommend representing your premise as a text and hypothesis as a label, you can put several hypotheses, but the model works best with a single input hypothesis.
```python
# Initialize model and multi-label pipeline
text = "The cat slept on the windowsill all afternoon"
labels = ["The cat was awake and playing outside."]
results = pipeline(text, labels, threshold=0.0)[0]
print(results)
```

### 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.
| Model                       | IMDB | AG_NEWS | Emotions |
|-----------------------------|------|---------|----------|
| [gliclass-modern-large-v2.0-init (399 M)](knowledgator/gliclass-modern-large-v2.0-init) | 0.9137 | 0.7357  | 0.4140  |
| [gliclass-modern-base-v2.0-init (151 M)](knowledgator/gliclass-modern-base-v2.0-init) | 0.8264 | 0.6637  | 0.2985  |
| [gliclass-modern-large-v2.0 (399 M)](knowledgator/gliclass-modern-large-v2.0) | 0.9448 | 0.736  | 0.4970  |
| [gliclass-modern-base-v2.0 (151 M)](knowledgator/gliclass-modern-base-v2.0) | 0.9188 | 0.7089  | 0.4250  |
| [gliclass-large-v1.0 (438 M)](https://huggingface.co/knowledgator/gliclass-large-v1.0) | 0.9404 | 0.7516  | 0.4874  |
| [gliclass-base-v1.0 (186 M)](https://huggingface.co/knowledgator/gliclass-base-v1.0) | 0.8650 | 0.6837  | 0.4749  |
| [gliclass-small-v1.0 (144 M)](https://huggingface.co/knowledgator/gliclass-small-v1.0) | 0.8650 | 0.6805  | 0.4664   |
| [Bart-large-mnli (407 M)](https://huggingface.co/facebook/bart-large-mnli)      | 0.89 | 0.6887  | 0.3765   |
| [Deberta-base-v3 (184 M)](https://huggingface.co/cross-encoder/nli-deberta-v3-base)      | 0.85 | 0.6455  | 0.5095   |
| [Comprehendo (184M)](https://huggingface.co/knowledgator/comprehend_it-base)           | 0.90 | 0.7982  | 0.5660   |
| SetFit [BAAI/bge-small-en-v1.5 (33.4M)](https://huggingface.co/BAAI/bge-small-en-v1.5) | 0.86 | 0.5636 | 0.5754 |


Below you can find a comparison with other GLiClass models:

| Dataset              | gliclass-modern-base-v2.0 | gliclass-modern-large-v2.0 | gliclass-modern-base-v2.0-init | gliclass-modern-large-v2.0-init |
|----------------------|-----------------------|-----------------------|---------------------|---------------------|
| CR                   | 0.8976                | 0.9198                | 0.9041              | 0.8980              |
| sst2                 | 0.8525                | 0.9318                | 0.9011              | 0.9434              |
| sst5                 | 0.2348                | 0.2147                | 0.1972              | 0.1123              |
| 20_news_groups       | 0.351                | 0.3755                | 0.2448              | 0.2792              |
| spam                 | 0.483                | 0.6608                | 0.5074              | 0.6364              |
| financial_phrasebank | 0.3475                | 0.3157                | 0.2537              | 0.2562              |
| imdb                 | 0.9188                | 0.9448                | 0.8255              | 0.9137              |
| ag_news              | 0.6835                | 0.7025                | 0.6050              | 0.6933              |
| emotion              | 0.3925                | 0.4325                | 0.2474              | 0.3746              |
| cap_sotu             | 0.3725                | 0.4157                | 0.2929              | 0.2919              |
| rotten_tomatoes      | 0.6955                | 0.7357                | 0.6630              | 0.5928              |
| **AVERAGE:**         | 0.5563                | 0.6045                | 0.5129              | 0.5447              |