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 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:
pip install gliclass
Than you need to initialize a model and a pipeline:
from gliclass import GLiClassModel, ZeroShotClassificationPipeline
from transformers import AutoTokenizer
model = GLiClassModel.from_pretrained("knowledgator/gliclass-base-v1.0-lw")
tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-base-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) | 0.9404 | 0.7516 | 0.4874 |
| gliclass-base-v1.0 (186 M) | 0.8650 | 0.6837 | 0.4749 |
| gliclass-small-v1.0 (144 M) | 0.8650 | 0.6805 | 0.4664 |
| Bart-large-mnli (407 M) | 0.89 | 0.6887 | 0.3765 |
| Deberta-base-v3 (184 M) | 0.85 | 0.6455 | 0.5095 |
| Comprehendo (184M) | 0.90 | 0.7982 | 0.5660 |
| SetFit BAAI/bge-small-en-v1.5 (33.4M) | 0.86 | 0.5636 | 0.5754 |