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