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--- |
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license: apache-2.0 |
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datasets: |
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- knowledgator/events_classification_biotech |
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- knowledgator/Scientific-text-classification |
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--- |
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# ⭐ GLiClass: Generalist and Lightweight Model for Sequence Classification |
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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. |
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It can be used for `topic classification`, `sentiment analysis` and as a reranker in `RAG` pipelines. |
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The model was trained on synthetic and licensed data that allow commercial use and can be used in commercial applications. |
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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. |
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The model was fine-tuned using a new RL-based approach to classification, with F1 and recall rewards. |
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### How to use: |
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First of all, you need to install GLiClass library: |
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```bash |
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pip install gliclass |
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pip install -U transformers>=4.48.0 |
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``` |
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Than you need to initialize a model and a pipeline: |
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```python |
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from gliclass import GLiClassModel, ZeroShotClassificationPipeline |
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from transformers import AutoTokenizer |
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model = GLiClassModel.from_pretrained("knowledgator/gliclass-modern-base-v2.0") |
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tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-modern-base-v2.0", add_prefix_space=True) |
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pipeline = ZeroShotClassificationPipeline(model, tokenizer, classification_type='multi-label', device='cuda:0') |
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text = "One day I will see the world!" |
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labels = ["travel", "dreams", "sport", "science", "politics"] |
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results = pipeline(text, labels, threshold=0.5)[0] #because we have one text |
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for result in results: |
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print(result["label"], "=>", result["score"]) |
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``` |
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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. |
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```python |
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# Initialize model and multi-label pipeline |
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text = "The cat slept on the windowsill all afternoon" |
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labels = ["The cat was awake and playing outside."] |
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results = pipeline(text, labels, threshold=0.0)[0] |
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print(results) |
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``` |
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### Benchmarks: |
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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. |
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| Model | IMDB | AG_NEWS | Emotions | |
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|-----------------------------|------|---------|----------| |
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| [gliclass-modern-large-v2.0-init (399 M)](knowledgator/gliclass-modern-large-v2.0-init) | 0.9137 | 0.7357 | 0.4140 | |
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| [gliclass-modern-base-v2.0-init (151 M)](knowledgator/gliclass-modern-base-v2.0-init) | 0.8264 | 0.6637 | 0.2985 | |
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| [gliclass-modern-large-v2.0 (399 M)](knowledgator/gliclass-modern-large-v2.0) | 0.9448 | 0.736 | 0.4970 | |
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| [gliclass-modern-base-v2.0 (151 M)](knowledgator/gliclass-modern-base-v2.0) | 0.9188 | 0.7089 | 0.4250 | |
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| [gliclass-large-v1.0 (438 M)](https://huggingface.co/knowledgator/gliclass-large-v1.0) | 0.9404 | 0.7516 | 0.4874 | |
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| [gliclass-base-v1.0 (186 M)](https://huggingface.co/knowledgator/gliclass-base-v1.0) | 0.8650 | 0.6837 | 0.4749 | |
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| [gliclass-small-v1.0 (144 M)](https://huggingface.co/knowledgator/gliclass-small-v1.0) | 0.8650 | 0.6805 | 0.4664 | |
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| [Bart-large-mnli (407 M)](https://huggingface.co/facebook/bart-large-mnli) | 0.89 | 0.6887 | 0.3765 | |
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| [Deberta-base-v3 (184 M)](https://huggingface.co/cross-encoder/nli-deberta-v3-base) | 0.85 | 0.6455 | 0.5095 | |
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| [Comprehendo (184M)](https://huggingface.co/knowledgator/comprehend_it-base) | 0.90 | 0.7982 | 0.5660 | |
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| SetFit [BAAI/bge-small-en-v1.5 (33.4M)](https://huggingface.co/BAAI/bge-small-en-v1.5) | 0.86 | 0.5636 | 0.5754 | |
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Below you can find a comparison with other GLiClass models: |
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| Dataset | gliclass-modern-base-v2.0 | gliclass-modern-large-v2.0 | gliclass-modern-base-v2.0-init | gliclass-modern-large-v2.0-init | |
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|----------------------|-----------------------|-----------------------|---------------------|---------------------| |
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| CR | 0.8976 | 0.9198 | 0.9041 | 0.8980 | |
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| sst2 | 0.8525 | 0.9318 | 0.9011 | 0.9434 | |
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| sst5 | 0.2348 | 0.2147 | 0.1972 | 0.1123 | |
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| 20_news_groups | 0.351 | 0.3755 | 0.2448 | 0.2792 | |
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| spam | 0.483 | 0.6608 | 0.5074 | 0.6364 | |
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| financial_phrasebank | 0.3475 | 0.3157 | 0.2537 | 0.2562 | |
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| imdb | 0.9188 | 0.9448 | 0.8255 | 0.9137 | |
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| ag_news | 0.6835 | 0.7025 | 0.6050 | 0.6933 | |
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| emotion | 0.3925 | 0.4325 | 0.2474 | 0.3746 | |
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| cap_sotu | 0.3725 | 0.4157 | 0.2929 | 0.2919 | |
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| rotten_tomatoes | 0.6955 | 0.7357 | 0.6630 | 0.5928 | |
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| **AVERAGE:** | 0.5563 | 0.6045 | 0.5129 | 0.5447 | |
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## Citation |
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```bibtex |
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@misc{stepanov2025gliclassgeneralistlightweightmodel, |
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title={GLiClass: Generalist Lightweight Model for Sequence Classification Tasks}, |
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author={Ihor Stepanov and Mykhailo Shtopko and Dmytro Vodianytskyi and Oleksandr Lukashov and Alexander Yavorskyi and Mykyta Yaroshenko}, |
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year={2025}, |
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eprint={2508.07662}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.LG}, |
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url={https://arxiv.org/abs/2508.07662}, |
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} |
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``` |
