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---
license: apache-2.0
datasets:
- MoritzLaurer/synthetic_zeroshot_mixtral_v0.1
language:
- en
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 data 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.
### How to use:
First of all, you need to install GLiClass library:
```bash
pip install gliclass
```
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-large-v1.0-lw")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-large-v1.0-lw")
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-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 |
|----------------------|-----------------------|-----------------------|-----------------------|---------------------|---------------------|---------------------|
| CR | 0.8886 | 0.9097 | 0.9226 | 0.8824 | 0.8942 | 0.9219 |
| sst2 | 0.8392 | 0.8987 | 0.9247 | 0.8518 | 0.8979 | 0.9269 |
| sst5 | 0.2865 | 0.3779 | 0.2891 | 0.2424 | 0.2789 | 0.3900 |
| 20_news_groups | 0.4572 | 0.3953 | 0.4083 | 0.3366 | 0.3576 | 0.3863 |
| spam | 0.5118 | 0.5126 | 0.3642 | 0.4089 | 0.4938 | 0.3661 |
| rotten_tomatoes | 0.8015 | 0.8429 | 0.8807 | 0.7987 | 0.8508 | 0.8808 |
| massive | 0.3180 | 0.4635 | 0.5606 | 0.2546 | 0.1893 | 0.4376 |
| banking | 0.1768 | 0.4396 | 0.3317 | 0.1374 | 0.2077 | 0.2847 |
| yahoo_topics | 0.4686 | 0.4784 | 0.4760 | 0.4477 | 0.4516 | 0.4921 |
| financial_phrasebank | 0.8665 | 0.8880 | 0.9044 | 0.8901 | 0.8955 | 0.8735 |
| imdb | 0.9048 | 0.9351 | 0.9429 | 0.8982 | 0.9238 | 0.9333 |
| ag_news | 0.7252 | 0.6985 | 0.7559 | 0.7242 | 0.6848 | 0.7503 |
| dair_emotion | 0.4012 | 0.3516 | 0.3951 | 0.3450 | 0.2357 | 0.4013 |
| capsotu | 0.3794 | 0.4643 | 0.4749 | 0.3432 | 0.4375 | 0.4644 |
|Average:|0.5732|0.6183|0.6165|0.5401|0.5571|0.6078|
Here you can see how the performance of the model grows providing more examples:
| Model | Num Examples | sst5 | spam | massive | banking | ag news | dair emotion | capsotu | Average |
|-----------------------------|--------------|--------|---------|---------|---------|---------|--------------|---------|-------------|
| gliclass-small-v1.0-lw | 0 | 0.2865 | 0.5118 | 0.318 | 0.1768 | 0.7252 | 0.4012 | 0.3794 | 0.3998428571|
| gliclass-base-v1.0-lw | 0 | 0.3779 | 0.5126 | 0.4635 | 0.4396 | 0.6985 | 0.3516 | 0.4643 | 0.4725714286|
| gliclass-large-v1.0-lw | 0 | 0.2891 | 0.3642 | 0.5606 | 0.3317 | 0.7559 | 0.3951 | 0.4749 | 0.4530714286|
| gliclass-small-v1.0 | 0 | 0.2424 | 0.4089 | 0.2546 | 0.1374 | 0.7242 | 0.345 | 0.3432 | 0.3508142857|
| gliclass-base-v1.0 | 0 | 0.2789 | 0.4938 | 0.1893 | 0.2077 | 0.6848 | 0.2357 | 0.4375 | 0.3611 |
| gliclass-large-v1.0 | 0 | 0.39 | 0.3661 | 0.4376 | 0.2847 | 0.7503 | 0.4013 | 0.4644 | 0.4420571429|
| gliclass-small-v1.0-lw | 8 | 0.2709 | 0.84026 | 0.62 | 0.6883 | 0.7786 | 0.449 | 0.4918 | 0.5912657143|
| gliclass-base-v1.0-lw | 8 | 0.4275 | 0.8836 | 0.729 | 0.7667 | 0.7968 | 0.3866 | 0.4858 | 0.6394285714|
| gliclass-large-v1.0-lw | 8 | 0.3345 | 0.8997 | 0.7658 | 0.848 | 0.84843 | 0.5219 | 0.508 | 0.67519 |
| gliclass-small-v1.0 | 8 | 0.3042 | 0.5683 | 0.6332 | 0.7072 | 0.759 | 0.4509 | 0.4434 | 0.5523142857|
| gliclass-base-v1.0 | 8 | 0.3387 | 0.7361 | 0.7059 | 0.7456 | 0.7896 | 0.4323 | 0.4802 | 0.6040571429|
| gliclass-large-v1.0 | 8 | 0.4365 | 0.9018 | 0.77 | 0.8533 | 0.8509 | 0.5061 | 0.4935 | 0.6874428571|
|