⭐ 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. How to use: First of all, you need to install GLiClass library: Than you need to initialize a model and a pipeline: 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 Below you can find a comparison with other GLiClass models: Dataset gliclass sm…
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