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--- |
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license: apache-2.0 |
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datasets: |
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- MoritzLaurer/synthetic_zeroshot_mixtral_v0.1 |
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language: |
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- en |
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metrics: |
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- f1 |
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pipeline_tag: zero-shot-classification |
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tags: |
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- text classification |
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- zero-shot |
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- small language models |
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- RAG |
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- sentiment analysis |
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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 data and can be used in commercial applications. |
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This model wasn't additionally fine-tuned on any dataset except initial (MoritzLaurer/synthetic_zeroshot_mixtral_v0.1). |
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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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``` |
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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-small-v1.0-init") |
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tokenizer = AutoTokenizer.from_pretrained("knowledgator/gliclass-small-v1.0-init") |
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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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### 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-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 | |
