---
license: apache-2.0
datasets:
- MoritzLaurer/synthetic_zeroshot_mixtral_v0.1
- knowledgator/gliclass-v1.0
- fancyzhx/amazon_polarity
- cnmoro/QuestionClassification
- Arsive/toxicity_classification_jigsaw
- shishir-dwi/News-Article-Categorization_IAB
- SetFit/qnli
- nyu-mll/multi_nli
- SetFit/student-question-categories 
- SetFit/tweet_sentiment_extraction 
- SetFit/hate_speech18
- saattrupdan/doc-nli

language:
- en
- fr
- ge
metrics:
- f1
pipeline_tag: zero-shot-classification
tags:
- text classification
- zero-shot
- small language models
- RAG
- sentiment analysis
---
# ⭐ 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-large](https://huggingface.co/answerdotai/ModernBERT-large), which effectively processes long sequences.

### 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-large-v2.0-init")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-modern-large-v2.0-init")
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"])
```

### 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-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-small-v1.0-lw | gliclass-base-v1.0-lw | gliclass-large-v1.0-lw | gliclass-small-v1.0 | gliclass-base-v1.0 | gliclass-large-v1.0 | gliclass-modern-base-v2.0-init | gliclass-modern-large-v2.0-init |
|----------------------|-----------------------|-----------------------|-----------------------|---------------------|---------------------|---------------------|---------------------|---------------------|
| CR                   | 0.8886                | 0.9097                | 0.9226                | 0.8824              | 0.8942              | 0.9219              | 0.9041              | 0.8980              |
| sst2                 | 0.8392                | 0.8987                | 0.9247                | 0.8518              | 0.8979              | 0.9269              | 0.9011              | 0.9434              |
| sst5                 | 0.2865                | 0.3779                | 0.2891                | 0.2424              | 0.2789              | 0.3900              | 0.1972              | 0.1123              |
| 20_news_groups       | 0.4572                | 0.3953                | 0.4083                | 0.3366              | 0.3576              | 0.3863              | 0.2448              | 0.2792              |
| spam                 | 0.5118                | 0.5126                | 0.3642                | 0.4089              | 0.4938              | 0.3661              | 0.5074              | 0.6364              |
| rotten_tomatoes      | 0.8015                | 0.8429                | 0.8807                | 0.7987              | 0.8508              | 0.8808              | 0.6630              | 0.5928              |
| financial_phrasebank | 0.8665                | 0.8880                | 0.9044                | 0.8901              | 0.8955              | 0.8735              | 0.2537              | 0.2562              |
| imdb                 | 0.9048                | 0.9351                | 0.9429                | 0.8982              | 0.9238              | 0.9333              | 0.8255              | 0.9137              |
| ag_news              | 0.7252                | 0.6985                | 0.7559                | 0.7242              | 0.6848              | 0.7503              | 0.6050              | 0.6933              |
| dair_emotion         | 0.4012                | 0.3516                | 0.3951                | 0.3450              | 0.2357              | 0.4013              | 0.2474              | 0.3746              |
| capsotu              | 0.3794                | 0.4643                | 0.4749                | 0.3432              | 0.4375              | 0.4644              | 0.2929              | 0.2919              |
| **Average:**         | 0.5732                | 0.6183                | 0.6165                | 0.5401              | 0.5571              | 0.6078              | 0.5129              | 0.5447              |